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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-2493a163dd","isDeveloperGuidesPage":false,"quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroAuthor":"Joviane Bellegarde","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Cortex 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Framework"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/cortex-ai-demo-framework",":type":"snowflake-site/components/quickstart/quickstart-hero"},"flexible_column_cont":{"id":"flexible-column-container-19380f906d","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-4d6bc0f2cd","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-d0452919a2","description":"","title":"Cortex AI Demo Framework","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EDemo development is crucial for businesses to showcase their AI capabilities and win new customers. Through rapid prototyping and professional presentation tools, businesses can transform weeks of development into minutes of setup, dramatically accelerating sales cycles and proof-of-concept delivery.\u003C/p\u003E\n","\u003Cp\u003EIn this Quickstart, we will build a comprehensive demo development platform called "Cortex AI Demo Framework". This demonstrates how to use Snowflake Cortex AI functions to create synthetic data, build interactive analytics, deploy search capabilities, and generate complete demonstration environments.\u003C/p\u003E\n","\u003Cp\u003EThis Quickstart showcases the complete Cortex AI Demo Framework with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E6-application integrated demo platform\u003C/strong\u003E with Synthetic Data Generator, Structured Tables, SQL to YAML Converter, Snow Demo, YAML Wizard, and Snow Viz\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAI-powered data generation\u003C/strong\u003E using all Cortex functions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdvanced semantic search\u003C/strong\u003E and automated model creation\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search Service\u003C/strong\u003E for intelligent data discovery\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst integration\u003C/strong\u003E for natural language queries\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProduction-ready applications\u003C/strong\u003E with professional UI/UX\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EComplete 6-application integrated demo platform\u003C/li\u003E\u003Cli\u003EAI-powered synthetic data generation system using Cortex functions\u003C/li\u003E\u003Cli\u003EAdvanced semantic modeling and search capabilities\u003C/li\u003E\u003Cli\u003EProfessional demo orchestration and configuration tools\u003C/li\u003E\u003Cli\u003EInteractive dashboard creation wizard with database introspection\u003C/li\u003E\u003Cli\u003EAdvanced dashboard renderer with multiple visualization types\u003C/li\u003E\u003Cli\u003EInteractive Cortex Search Service for semantic discovery\u003C/li\u003E\u003Cli\u003EProduction-ready Streamlit applications with advanced visualizations\u003C/li\u003E\u003Cli\u003EReusable framework for rapid demo creation across any industry\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up a production demo development pipeline with Snowflake\u003C/li\u003E\u003Cli\u003EHow to use Snowflake Notebooks for complex AI demo workflows\u003C/li\u003E\u003Cli\u003EHow to implement all Cortex AI functions (SENTIMENT, EXTRACT_ANSWER, COMPLETE)\u003C/li\u003E\u003Cli\u003EHow to build scalable demo platforms with synthetic data\u003C/li\u003E\u003Cli\u003EHow to create automated semantic models and search services\u003C/li\u003E\u003Cli\u003EHow to deploy interactive Streamlit applications in Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Python and SQL\u003C/li\u003E\u003Cli\u003EFamiliarity with Streamlit applications\u003C/li\u003E\u003Cli\u003EGo to the \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake\u003C/a\u003E sign-up page and register for a free account\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Snowflake Environment\u003C/h2\u003E\n","\u003Cp\u003EIn this step, you'll create the Snowflake database objects and prepare for framework deployment.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Create Database Objects\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EStarting in September 2025, Snowflake is gradually upgrading accounts from Worksheets to \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/workspaces\"\u003EWorkspaces\u003C/a\u003E. Workspaces will become the default SQL editor. Follow the instructions below that match your interface.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIf you have Workspaces:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, click \u003Ccode\u003EProjects\u003C/code\u003E, then <a href="https://app.snowflake.com/_deeplink/#/workspaces?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_content=cortex-ai-demo-framework&utm_cta=developer-guides-deeplink" class="_deeplink">Workspaces</a> in the left navigation\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003E+ Add new\u003C/code\u003E to create a new <a href="https://app.snowflake.com/_deeplink/#/workspaces?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_content=cortex-ai-demo-framework&utm_cta=developer-guides-deeplink" class="_deeplink">Workspace</a>\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ESQL File\u003C/code\u003E to create a new SQL file\u003C/li\u003E\u003Cli\u003ECopy the setup script from \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/setup.sql\"\u003Esetup.sql\u003C/a\u003E and paste it into your SQL file, then run it\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIf you have Worksheets:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, click \u003Ccode\u003EProjects\u003C/code\u003E, then \u003Ccode\u003EWorksheets\u003C/code\u003E in the left navigation\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003E+\u003C/code\u003E in the top-right corner to open a new Worksheet\u003C/li\u003E\u003Cli\u003ECopy the setup script from \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/setup.sql\"\u003Esetup.sql\u003C/a\u003E and paste it into your worksheet, then run it\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe setup script creates:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E: \u003Ccode\u003ECORTEX_FRAMEWORK_DB\u003C/code\u003E with \u003Ccode\u003EBRONZE_LAYER\u003C/code\u003E, \u003Ccode\u003ESILVER_LAYER\u003C/code\u003E, \u003Ccode\u003EAPPS\u003C/code\u003E, and \u003Ccode\u003ECONFIGS\u003C/code\u003E schemas\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERole\u003C/strong\u003E: \u003Ccode\u003ECORTEX_FRAMEWORK_DATA_SCIENTIST\u003C/code\u003E with all necessary permissions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse\u003C/strong\u003E: \u003Ccode\u003ECORTEX_FRAMEWORK_WH\u003C/code\u003E for compute resources\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStages\u003C/strong\u003E: \u003Ccode\u003EFRAMEWORK_DATA_STAGE\u003C/code\u003E, \u003Ccode\u003ESEMANTIC_MODELS\u003C/code\u003E, and \u003Ccode\u003EDEMO_CONFIGS\u003C/code\u003E for file uploads\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFile Formats\u003C/strong\u003E: \u003Ccode\u003ECSV_FORMAT\u003C/code\u003E, \u003Ccode\u003EYAML_FORMAT\u003C/code\u003E, and \u003Ccode\u003EJSON_FORMAT\u003C/code\u003E for data processing\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAI Access\u003C/strong\u003E: \u003Ccode\u003ESNOWFLAKE.CORTEX_USER\u003C/code\u003E role for Cortex functions\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 2: Download Required Framework Files\u003C/h3\u003E\n","\u003Cp\u003EDownload these framework files from the GitHub repository:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFile\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDownload Link\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ENotebook\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESetup notebook for framework deployment\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/notebooks/cortex_ai_demo_framework_setup.ipynb\"\u003Ecortex_ai_demo_framework_setup.ipynb\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EEnvironment File\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConda environment configuration for latest Streamlit\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/environment.yml\"\u003Eenvironment.yml\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESynthetic Data Generator\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAI-powered synthetic data creation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/01_ai_framework_synthetic_data_generator.py\"\u003E01_ai_framework_synthetic_data_generator.py\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EStructured Tables\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData structuring and transformation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/02_ai_framework_structured_tables.py\"\u003E02_ai_framework_structured_tables.py\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESQL to YAML Converter\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESQL to YAML configuration converter (generates semantic models)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/03_ai_framework_sql_to_yaml_converter.py\"\u003E03_ai_framework_sql_to_yaml_converter.py\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESnow Demo\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDemo configuration and runner\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/04_ai_framework_snow_demo.py\"\u003E04_ai_framework_snow_demo.py\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EYAML Wizard\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInteractive dashboard configuration creator\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/05_ai_framework_snow_viz_yaml_wizard.py\"\u003E05_ai_framework_snow_viz_yaml_wizard.py\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESnow Viz\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAdvanced visualization dashboard renderer\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/06_ai_framework_snow_viz.py\"\u003E06_ai_framework_snow_viz.py\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EStep 3: Upload Framework Files to Single Stage\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EIn Snowsight, change your role to \u003Ccode\u003Ecortex_ai_demo_data_scientist\u003C/code\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to \u003Ccode\u003ECatalog\u003C/code\u003E → \u003Ccode\u003EDatabase Explorer\u003C/code\u003E → \u003Ccode\u003EAI_FRAMEWORK_DB\u003C/code\u003E → \u003Ccode\u003EAPPS\u003C/code\u003E → \u003Ccode\u003EStages\u003C/code\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUpload all framework files to the single \u003Ccode\u003EAI_FRAMEWORK_APPS\u003C/code\u003E stage:\u003C/strong\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EClick on \u003Ccode\u003EAI_FRAMEWORK_APPS\u003C/code\u003E stage, then click \u003Ccode\u003EEnable Directory Table\u003C/code\u003E and upload all 7 files:\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003E01_ai_framework_synthetic_data_generator.py\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E02_ai_framework_structured_tables.py\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E03_ai_framework_sql_to_yaml_converter.py\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E04_ai_framework_snow_demo.py\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E05_ai_framework_snow_viz_yaml_wizard.py\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E06_ai_framework_snow_viz.py\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eenvironment.yml\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 4: Import the Framework Setup Notebook\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImport into Snowflake\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Ccode\u003EProjects\u003C/code\u003E → \u003Ccode\u003ENotebooks\u003C/code\u003E in Snowsight\u003C/li\u003E\u003Cli\u003EClick the down arrow next to \u003Ccode\u003E+ Notebook\u003C/code\u003E and select \u003Ccode\u003EImport .ipynb file\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EChoose \u003Ccode\u003Ecortex_ai_demo_framework_setup.ipynb\u003C/code\u003E from your downloads\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConfigure the notebook settings\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ERole\u003C/strong\u003E: Select \u003Ccode\u003Ecortex_ai_demo_data_scientist\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E: Select \u003Ccode\u003EAI_FRAMEWORK_DB\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema\u003C/strong\u003E: Select \u003Ccode\u003EBRONZE_LAYER\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EQuery Warehouse\u003C/strong\u003E: Select \u003Ccode\u003Ecortex_ai_demo_wh\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENotebook Warehouse\u003C/strong\u003E: Select \u003Ccode\u003Ecortex_ai_demo_wh\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EClick \u003Ccode\u003ECreate\u003C/code\u003E\u003C/strong\u003E to import the notebook\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe notebook creates all 6 Streamlit applications using the single stage approach with automatic environment.yml detection for the latest Streamlit version.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun Framework Demo Notebook\u003C/h2\u003E\n","\u003Ch3\u003EExecute the Framework Deployment Workflow\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EGo to \u003Ccode\u003EProjects\u003C/code\u003E → \u003Ccode\u003ENotebooks\u003C/code\u003E in Snowsight\u003C/li\u003E\u003Cli\u003EClick on \u003Ccode\u003ECORTEX_FRAMEWORK_DEMO\u003C/code\u003E Notebook to open it\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ERun all\u003C/code\u003E to execute all cells in the notebook at once\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat the notebook does:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreates sample customer survey data tables\u003C/li\u003E\u003Cli\u003EProcesses data with Cortex AI functions (SENTIMENT, EXTRACT_ANSWER, COMPLETE)\u003C/li\u003E\u003Cli\u003EDeploys all 6 Streamlit applications from the uploaded stage files\u003C/li\u003E\u003Cli\u003ESets up the complete framework for immediate demo creation\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe notebook processes sample data and deploys the complete framework application suite.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFramework Overview\u003C/h2\u003E\n","\u003Ch3\u003EAccess Your Demo Framework\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Ccode\u003EProjects\u003C/code\u003E → \u003Ccode\u003EStreamlit\u003C/code\u003E in Snowsight\u003C/li\u003E\u003Cli\u003EYou'll see 6 framework applications deployed\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EThe 6 Applications\u003C/h3\u003E\n","\u003Ch4\u003E\u003Cstrong\u003E1. Synthetic Data Generator\u003C/strong\u003E 🎲 (Always Start Here)\u003C/h4\u003E\n","\u003Cp\u003ECreates realistic AI-powered datasets using Cortex LLMs. Saves raw JSON to \u003Ccode\u003EBRONZE_LAYER\u003C/code\u003E tables.\u003C/p\u003E\n","\u003Ch4\u003E\u003Cstrong\u003E2. Structured Tables\u003C/strong\u003E 🔄\u003C/h4\u003E\n","\u003Cp\u003ETransforms raw JSON into clean, structured database tables. Outputs analytics-ready data to \u003Ccode\u003ESILVER_LAYER\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch4\u003E\u003Cstrong\u003E3. SQL to YAML Converter\u003C/strong\u003E ⚙️\u003C/h4\u003E\n","\u003Cp\u003EConverts SQL queries into interactive demo configurations for Snow Demo (App 4).\u003C/p\u003E\n","\u003Ch4\u003E\u003Cstrong\u003E4. Snow Demo\u003C/strong\u003E 📊\u003C/h4\u003E\n","\u003Cp\u003ERuns interactive SQL-driven presentations with live visualizations and AI experimentation.\u003C/p\u003E\n","\u003Ch4\u003E\u003Cstrong\u003E5. YAML Wizard\u003C/strong\u003E 🧙\u003C/h4\u003E\n","\u003Cp\u003EGuided dashboard configuration creator. Generates YAML files for Snow Viz (App 6).\u003C/p\u003E\n","\u003Ch4\u003E\u003Cstrong\u003E6. Snow Viz\u003C/strong\u003E 📈\u003C/h4\u003E\n","\u003Cp\u003ERenders advanced interactive dashboards with multi-tab analytics and AI integration.\u003C/p\u003E\n","\u003Ch3\u003EApplication Dependencies\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-console\"\u003E1. SYNTHETIC DATA GENERATOR (START HERE)\n   └─ Creates realistic datasets\n      │\n      ├─ 2. STRUCTURED TABLES\n      │  └─ Transforms JSON → SQL tables\n      │     │\n      │     └─ 5. YAML WIZARD\n      │        └─ Generates dashboard configs\n      │           │\n      │           └─ 6. SNOW VIZ\n      │              └─ Renders dashboards\n      │\n      └─ 3. SQL TO YAML CONVERTER\n         └─ Converts queries → demo configs\n            │\n            └─ 4. SNOW DEMO\n               └─ Runs interactive SQL demos\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENext\u003C/strong\u003E: Page 5 shows which apps to use based on your role and goals.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPersona Workflows\u003C/h2\u003E\n","\u003Ch3\u003EWho Should Use This Framework?\u003C/h3\u003E\n","\u003Cp\u003EThe framework supports \u003Cstrong\u003E4 different user personas\u003C/strong\u003E. Find your role below to see which apps you need and in what order.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EPersona 1: Full-Stack Data Developer\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWho You Are\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EData engineers building end-to-end pipelines\u003C/li\u003E\u003Cli\u003EAnalytics developers creating dashboards\u003C/li\u003E\u003Cli\u003ETechnical users who want the complete experience\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat You'll Build\u003C/strong\u003E: A complete analytics pipeline from data generation to interactive dashboards\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EApps You'll Use\u003C/strong\u003E: Synthetic Data Generator → Structured Tables → YAML Wizard → Snow Viz\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETime Required\u003C/strong\u003E: ~25 minutes\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYour Workflow\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ESynthetic Data Generator\u003C/strong\u003E: Generate synthetic data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStructured Tables\u003C/strong\u003E: Transform JSON to structured table\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EYAML Wizard\u003C/strong\u003E: Create dashboard configuration\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnow Viz\u003C/strong\u003E: View your interactive dashboard\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat You'll Get\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESynthetic dataset with realistic values\u003C/li\u003E\u003Cli\u003EClean, structured database table\u003C/li\u003E\u003Cli\u003EInteractive dashboard with multiple visualization tabs\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EPersona 2: SQL Demo Creator / Solutions Architect\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWho You Are\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESolutions architects building customer demos\u003C/li\u003E\u003Cli\u003ETechnical evangelists presenting Snowflake capabilities\u003C/li\u003E\u003Cli\u003EDemo creators showcasing SQL + AI features\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat You'll Build\u003C/strong\u003E: Interactive SQL-driven presentations with live query execution and AI experimentation\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EApps You'll Use\u003C/strong\u003E: Synthetic Data Generator → Structured Tables → SQL to YAML Converter → Snow Demo\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETime Required\u003C/strong\u003E: ~30 minutes\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYour Workflow\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ESynthetic Data Generator\u003C/strong\u003E: Generate synthetic data for demos\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStructured Tables\u003C/strong\u003E: Create structured table\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESQL to YAML Converter\u003C/strong\u003E: Write SQL queries and convert to demo format\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnow Demo\u003C/strong\u003E: Run interactive SQL presentation\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat You'll Get\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERealistic demo dataset\u003C/li\u003E\u003Cli\u003EMulti-step SQL presentation\u003C/li\u003E\u003Cli\u003EInteractive visualizations with live AI experimentation\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EPersona 3: Data Preparation Specialist\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWho You Are\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EData scientists needing training data\u003C/li\u003E\u003Cli\u003EML engineers requiring test datasets\u003C/li\u003E\u003Cli\u003EBI developers prototyping dashboards\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat You'll Build\u003C/strong\u003E: Clean, structured datasets for export to external tools (notebooks, ML pipelines, BI tools)\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EApps You'll Use\u003C/strong\u003E: Synthetic Data Generator → Structured Tables\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETime Required\u003C/strong\u003E: ~15 minutes\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYour Workflow\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ESynthetic Data Generator\u003C/strong\u003E: Generate synthetic data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStructured Tables\u003C/strong\u003E: Transform to structured table\u003C/li\u003E\u003Cli\u003EExport data via CSV, Python/Snowpark, or direct BI tool connections\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat You'll Get\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EProduction-ready synthetic datasets\u003C/li\u003E\u003Cli\u003EValidated data quality\u003C/li\u003E\u003Cli\u003EExport-ready structured tables\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EPersona 4: Dashboard Consumer / Executive\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWho You Are\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EBusiness executives viewing insights\u003C/li\u003E\u003Cli\u003EManagers making data-driven decisions\u003C/li\u003E\u003Cli\u003EAnalysts exploring pre-built dashboards\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat You'll Do\u003C/strong\u003E: View and interact with dashboards created by your data team (no setup required)\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EApps You'll Use\u003C/strong\u003E: Snow Viz only (after colleague completes setup)\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETime Required\u003C/strong\u003E: ~5 minutes\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrerequisites\u003C/strong\u003E:\nA colleague must first complete Synthetic Data Generator → Structured Tables → YAML Wizard to create the dashboard. Once that's done, you can view and explore it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYour Workflow\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ESnow Viz\u003C/strong\u003E: Open app and select dashboard\u003C/li\u003E\u003Cli\u003EExplore tabs with different visualization types\u003C/li\u003E\u003Cli\u003EUse AI Assistant to ask questions in plain English\u003C/li\u003E\u003Cli\u003EExport data to CSV for further analysis\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat You Can Do\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EView key metrics and trends\u003C/li\u003E\u003Cli\u003EAsk questions in natural language\u003C/li\u003E\u003Cli\u003EExport results to spreadsheets\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EChoose Your Path\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReady to get started?\u003C/strong\u003E Jump to the pages for your persona:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPersona\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EApps to Follow\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat You'll Build\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EFull-Stack Developer\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESynthetic Data Generator → Structured Tables → YAML Wizard → Snow Viz\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EComplete analytics pipeline with dashboards\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESQL Demo Creator\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESynthetic Data Generator → Structured Tables → SQL to YAML Converter → Snow Demo\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInteractive SQL presentations with AI\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EData Preparation\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESynthetic Data Generator → Structured Tables\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClean datasets for ML/BI/external tools\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EDashboard Consumer\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnow Viz only\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExplore pre-built dashboards (no setup)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOr read all app instructions\u003C/strong\u003E (Pages 6-11) to understand the full framework capabilities.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESynthetic Data Generator\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Create realistic AI-powered datasets for any business scenario using Cortex LLMs\u003Cbr\u003E\n\u003Cstrong\u003EDependencies\u003C/strong\u003E: None (START HERE)\u003Cbr\u003E\n\u003Cstrong\u003EOutput\u003C/strong\u003E: Raw JSON data saved to \u003Ccode\u003EBRONZE_LAYER\u003C/code\u003E tables\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app1-synthetic-data-generator.gif\" alt=\"Synthetic Data Generator Demo\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWho Uses This App\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAll Personas start here!\u003C/strong\u003E This is the foundation of the framework.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 1\u003C/strong\u003E (Full-Stack Developer): Generate 100 records for dashboards\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 2\u003C/strong\u003E (SQL Demo Creator): Generate 150 records for presentations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 3\u003C/strong\u003E (Data Preparation): Generate 300+ records for ML/BI export\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 4\u003C/strong\u003E (Dashboard Consumer): Your colleague uses this to create data for you\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EStep-by-Step Instructions\u003C/h3\u003E\n","\u003Ch4\u003EStep 1: Open the App\u003C/h4\u003E\n","\u003Cp\u003ENavigate to \u003Ccode\u003EProjects\u003C/code\u003E → \u003Ccode\u003EStreamlit\u003C/code\u003E → \u003Cstrong\u003E\u003Ccode\u003ESYNTHETIC_DATA_GENERATOR\u003C/code\u003E\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Configuration Management (Optional)\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar - Top Section:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EFor first-time use, leave "Load Configuration" as \u003Cstrong\u003ECreate New\u003C/strong\u003E. If you have saved configurations, select one from dropdown and click \u003Cstrong\u003E📁 Load Configuration\u003C/strong\u003E.\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Dataset Configuration\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EEnter your company name and topic/domain:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAcme Corp\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003ECustomer Orders\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOther Examples\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EShopSmart + "Product Sales"\u003C/li\u003E\u003Cli\u003EMedCenter + "Patient Vitals"\u003C/li\u003E\u003Cli\u003EFinanceFirst + "Loan Applications"\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 4: Define Data Fields\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar - Fields Section:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EEnter your fields (one per line):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecustomer_id\ncustomer_name\nemail\norder_date\nproduct_name\nquantity\nprice\ntotal_amount\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETips\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOne field per line\u003C/li\u003E\u003Cli\u003EUse descriptive field names\u003C/li\u003E\u003Cli\u003EInclude date fields for time-series analysis\u003C/li\u003E\u003Cli\u003EAdd 6-10 fields for realistic datasets\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 5: Batch Configuration\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ESet your batch configuration using the sliders:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ERecords per Batch\u003C/strong\u003E: \u003Ccode\u003E10\u003C/code\u003E (Slider: 10-200, step 10)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENumber of Batches\u003C/strong\u003E: \u003Ccode\u003E10\u003C/code\u003E (Slider: 1-1000)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETotal records to generate\u003C/strong\u003E: \u003Ccode\u003E100\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERecommended Settings\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETesting\u003C/strong\u003E: 10 records × 10 batches = 100 records (~2-3 min)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDemos\u003C/strong\u003E: 30 records × 15 batches = 450 records (~8-10 min)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProduction\u003C/strong\u003E: 50 records × 20 batches = 1000 records (~15-20 min)\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy smaller batches?\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHigher accuracy (95%+ valid JSON)\u003C/li\u003E\u003Cli\u003EFaster generation per batch\u003C/li\u003E\u003Cli\u003EBetter error recovery\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 6: Configure Cortex LLM\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EConfigure the Cortex LLM settings:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EModel Type\u003C/strong\u003E: \u003Ccode\u003ELARGE\u003C/code\u003E (Options: SMALL, MEDIUM, LARGE)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EModel\u003C/strong\u003E: \u003Ccode\u003Emistral-large2\u003C/code\u003E (Recommended for consistent results)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETemperature\u003C/strong\u003E: \u003Ccode\u003E0.7\u003C/code\u003E (Slider: 0.0-1.0, step 0.1)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMax Tokens\u003C/strong\u003E: \u003Ccode\u003E4000\u003C/code\u003E (Slider: 100-8000, step 100)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EModel Selection Guide\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emistral-large2\u003C/strong\u003E (LARGE): Best accuracy, handles any batch size\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Emixtral-8x7b\u003C/strong\u003E (MEDIUM): Good balance, use ≤30 records/batch\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Ellama3.1-8b\u003C/strong\u003E (SMALL): Fastest, use ≤20 records/batch\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETemperature Guide\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E0.1-0.3\u003C/strong\u003E: Medical/financial data (high consistency)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E0.7\u003C/strong\u003E: General business data (balanced)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E0.9\u003C/strong\u003E: Creative content (reviews, feedback)\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 7: Performance Configuration\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E☑ \u003Cstrong\u003EHigh-Performance Mode\u003C/strong\u003E (Uses stored procedures - RECOMMENDED)\u003C/li\u003E\u003Cli\u003E☐ \u003Cstrong\u003EShow Manual Scripts\u003C/strong\u003E (Leave unchecked unless you need SQL)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EKeep "High-Performance Mode" checked for best results!\u003C/p\u003E\n","\u003Ch4\u003EStep 8: Auto-save Configuration\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ECheck the following options:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E☑ \u003Cstrong\u003EAuto-save batches to table\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E: \u003Ccode\u003EAI_FRAMEWORK_DB\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema\u003C/strong\u003E: \u003Ccode\u003EBRONZE_LAYER\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETable Name\u003C/strong\u003E: \u003Ccode\u003EGENERATED_DATA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E☑ \u003Cstrong\u003EAppend to existing table\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant\u003C/strong\u003E: Data saves to \u003Ccode\u003EBRONZE_LAYER\u003C/code\u003E first. You'll transform it to \u003Ccode\u003ESILVER_LAYER\u003C/code\u003E in App 2!\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 9: Generate Data\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003E"Generate Default Prompts"\u003C/strong\u003E → Review/edit prompts if needed\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E"🎲 Generate Synthetic Data"\u003C/strong\u003E → Wait ~2-3 minutes\u003C/li\u003E\u003Cli\u003EWatch progress: Batch 1/10... 10/10\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EStep 10: Verify Success\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGenerated 100 records successfully!\nData saved to: AI_FRAMEWORK_DB.BRONZE_LAYER.GENERATED_DATA\n\nSample data preview:\n| CUSTOMER_NAME | PRODUCT_NAME | QUANTITY | PRICE | TOTAL_AMOUNT |\n|---------------|--------------|----------|-------|--------------|\n| Sarah Johnson | Laptop Pro   | 1        | 1299  | 1299         |\n| Mike Chen     | Wireless Mouse| 2       | 29    | 58           |\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVerification Steps\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EGo to Snowsight → \u003Cstrong\u003EData\u003C/strong\u003E → \u003Cstrong\u003EDatabases\u003C/strong\u003E → \u003Cstrong\u003EAI_FRAMEWORK_DB\u003C/strong\u003E → \u003Cstrong\u003EBRONZE_LAYER\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EFind your table (e.g., \u003Ccode\u003EGENERATED_DATA\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EClick to view:\n\u003Cul\u003E\u003Cli\u003EShould see \u003Cstrong\u003E10 rows\u003C/strong\u003E (one per batch)\u003C/li\u003E\u003Cli\u003EEach row has \u003Cstrong\u003EMESSAGES\u003C/strong\u003E column with JSON array\u003C/li\u003E\u003Cli\u003ECheck \u003Cstrong\u003E\u003Cem\u003EMETA\u003C/em\u003E\u003C/strong\u003E columns for generation metadata\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData Quality Check\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Run this query to check your data\nSELECT \n    COUNT(*) as total_batches,\n    SUM(_META_RECORDS_IN_BATCH) as total_records,\n    AVG(_META_RECORDS_IN_BATCH) as avg_records_per_batch,\n    _META_COMPANY_NAME,\n    _META_TOPIC\nFROM AI_FRAMEWORK_DB.BRONZE_LAYER.GENERATED_DATA\nGROUP BY _META_COMPANY_NAME, _META_TOPIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExpected: 10 batches, 100 total records\u003C/p\u003E\n","\u003Ch4\u003EStep 11: Save Configuration (Optional)\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBottom of Main Panel:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EEnter a configuration name and click \u003Cstrong\u003E💾 Save Configuration\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAcme_Corp_Customer_Orders_Config\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESave your configuration to reuse later with different batch sizes or models!\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ECommon Use Cases\u003C/h3\u003E\n","\u003Ch4\u003E\u003Cstrong\u003ERetail / E-commerce\u003C/strong\u003E\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode\u003ECompany: ShopSmart\nTopic: Product Sales\nFields: product_id, product_name, category, sale_date, sale_amount, \n        customer_segment, region, payment_method\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E\u003Cstrong\u003EHealthcare\u003C/strong\u003E\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode\u003ECompany: MedCenter\nTopic: Patient Vitals\nFields: patient_id, age, gender, blood_pressure_systolic, \n        blood_pressure_diastolic, heart_rate, temperature, \n        oxygen_saturation, recorded_date\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E\u003Cstrong\u003EFinancial Services\u003C/strong\u003E\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode\u003ECompany: FinanceFirst\nTopic: Loan Applications\nFields: application_id, applicant_name, loan_amount, credit_score, \n        income, employment_status, application_date, approval_status\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EBest Practices\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStart small\u003C/strong\u003E: Test with 10 records × 10 batches first\u003Cbr\u003E\n\u003Cstrong\u003EUse mistral-large2\u003C/strong\u003E: Best accuracy across all scenarios\u003Cbr\u003E\n\u003Cstrong\u003EName tables descriptively\u003C/strong\u003E: Include company/topic in table name\u003Cbr\u003E\n\u003Cstrong\u003ESave configurations\u003C/strong\u003E: Reuse settings for consistent results\u003Cbr\u003E\n\u003Cstrong\u003ECheck data quality\u003C/strong\u003E: Verify first batch before generating more\u003Cbr\u003E\n\u003Cstrong\u003EUse appropriate temperature\u003C/strong\u003E: Low for factual, high for creative\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EWhat's Next?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor All Personas\u003C/strong\u003E:\n→ Continue to \u003Cstrong\u003EPage 7 (App 2 - Structured Tables)\u003C/strong\u003E to transform your data from \u003Ccode\u003EBRONZE_LAYER\u003C/code\u003E to \u003Ccode\u003ESILVER_LAYER\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EYour data is now in raw JSON format. App 2 will clean and structure it into proper database columns!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStructured Tables\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Transform raw JSON data into clean, structured database tables\u003Cbr\u003E\n\u003Cstrong\u003EDependencies\u003C/strong\u003E: Requires data from App 1\u003Cbr\u003E\n\u003Cstrong\u003EOutput\u003C/strong\u003E: Analytics-ready data in \u003Ccode\u003ESILVER_LAYER\u003C/code\u003E tables\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app2-structured-tables.gif\" alt=\"Structured Tables Demo\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWho Uses This App\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 1\u003C/strong\u003E (Full-Stack Developer): Transform to structured tables for dashboards\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 2\u003C/strong\u003E (SQL Demo Creator): Clean data for SQL presentations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 3\u003C/strong\u003E (Data Preparation): Structure data before export to ML/BI tools\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 4\u003C/strong\u003E (Dashboard Consumer): Your colleague uses this to prepare data\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EStep-by-Step Instructions\u003C/h3\u003E\n","\u003Ch4\u003EStep 1: Open the App\u003C/h4\u003E\n","\u003Cp\u003ENavigate to \u003Ccode\u003EProjects\u003C/code\u003E → \u003Ccode\u003EStreamlit\u003C/code\u003E → \u003Cstrong\u003E\u003Ccode\u003ESTRUCTURED_TABLES\u003C/code\u003E\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Select Source Table\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMain Panel - Left Column:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect source table with synthetic data from the dropdown (e.g., \u003Ccode\u003EGENERATED_DATA\u003C/code\u003E).\u003C/p\u003E\n","\u003Cp\u003EThe dropdown shows all tables from \u003Ccode\u003EBRONZE_LAYER\u003C/code\u003E that contain a \u003Ccode\u003EMESSAGES\u003C/code\u003E column (generated by Synthetic Data Generator).\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Configure Target Table Name\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMain Panel - Right Column:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EEnter name for structured table (e.g., \u003Ccode\u003EGENERATED_DATA_STRUCTURED\u003C/code\u003E).\u003C/p\u003E\n","\u003Cp\u003EThe app auto-fills this by adding \u003Ccode\u003E_STRUCTURED\u003C/code\u003E to your source table name. You can customize it if needed.\u003C/p\u003E\n","\u003Ch4\u003EStep 4: Filter by Company and Topic\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMain Panel - Filter Section:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect the company and topic you used when generating data in Step 1 from the dropdowns (e.g., \u003Ccode\u003EAcme Corp\u003C/code\u003E and \u003Ccode\u003ECustomer Orders\u003C/code\u003E).\u003C/p\u003E\n","\u003Cp\u003EThese dropdowns populate automatically from your source table's metadata (\u003Ccode\u003E_meta_company_name\u003C/code\u003E and \u003Ccode\u003E_meta_topic\u003C/code\u003E columns).\u003C/p\u003E\n","\u003Ch4\u003EStep 5: Review Data Quality Analysis\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAuto-generated after selection:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E📊 Data Quality Analysis\n\nLeft Column:\nTotal Records: 10\nValid JSON: 10\n\nMiddle Column:\nInvalid JSON: 0\nVery Short: 0\n\nRight Column:\nAvg Length: 2,500 chars\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EValid JSON\u003C/strong\u003E should match \u003Cstrong\u003ETotal Records\u003C/strong\u003E (if using mistral-large2)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInvalid JSON\u003C/strong\u003E should be 0 or very low\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVery Short\u003C/strong\u003E indicates truncated records\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGood Quality Indicators\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EValid JSON = Total Records (100% success rate)\u003C/li\u003E\u003Cli\u003EInvalid JSON = 0 (no errors)\u003C/li\u003E\u003Cli\u003EAvg Length > 1,000 chars (complete records)\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 6: Preview Sample Data\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESample of Cleaned Data section:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E| MESSAGES | _META_COMPANY_NAME | _META_TOPIC | _META_RECORDS_IN_BATCH |\n|----------|-------------------|-------------|------------------------|\n| [{"customer_id": 1, ...}] | Acme Corp | Customer Orders | 10 |\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis shows your raw BRONZE_LAYER data with JSON arrays in the \u003Ccode\u003EMESSAGES\u003C/code\u003E column.\u003C/p\u003E\n","\u003Ch4\u003EStep 7: Review Fields Analysis\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAuto-detected fields:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E🔍 Fields Analysis\n\nFound 8 fields: customer_id, customer_name, email, order_date, \n                 product_name, quantity, price, total_amount\n\n📝 View SQL Column Names (expandable):\nSQL column names: CUSTOMER_ID, CUSTOMER_NAME, EMAIL, ORDER_DATE, \n                  PRODUCT_NAME, QUANTITY, PRICE, TOTAL_AMOUNT\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe app automatically detects field names from your JSON structure and shows how they'll appear as SQL column names (uppercase).\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVerify\u003C/strong\u003E all your expected fields are detected!\u003C/p\u003E\n","\u003Ch4\u003EStep 8: Transform Data\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBottom Section:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConfiguration name:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAcme_Corp_Customer_Orders_GENERATED_DATA\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EOptional\u003C/strong\u003E: Edit the configuration name if you want to save settings\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E"🔄 Transform Data"\u003C/strong\u003E button\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EProgress indicator\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ETransforming data...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis process:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECleans LLM artifacts from JSON\u003C/li\u003E\u003Cli\u003EFlattens JSON arrays to individual rows\u003C/li\u003E\u003Cli\u003ECreates structured table in \u003Ccode\u003ESILVER_LAYER\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EValidates data quality\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected time\u003C/strong\u003E: 30 seconds to 2 minutes depending on data volume\u003C/p\u003E\n","\u003Ch4\u003EStep 9: Verify Success\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESuccessfully transformed data to table: GENERATED_DATA_STRUCTURED\n\n📋 Sample of Transformed Data\n\n| CUSTOMER_ID | CUSTOMER_NAME | EMAIL | ORDER_DATE | PRODUCT_NAME | QUANTITY | PRICE | TOTAL_AMOUNT |\n|-------------|---------------|-------|------------|--------------|----------|-------|--------------|\n| 1 | Sarah Johnson | sarah.j@email.com | 2024-03-15 | Laptop Pro | 1 | 1299 | 1299 |\n| 2 | Mike Chen | mike.c@email.com | 2024-03-12 | Wireless Mouse | 2 | 29 | 58 |\n\n📊 Transformation Summary:\nRecords processed: 100\nTarget table: AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat happened\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EBefore\u003C/strong\u003E: 10 rows in BRONZE_LAYER (batches with JSON arrays)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAfter\u003C/strong\u003E: 100 rows in SILVER_LAYER (individual records with columns)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 10: Verify in Snowsight\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVerification Steps\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EGo to Snowsight → \u003Cstrong\u003EData\u003C/strong\u003E → \u003Cstrong\u003EDatabases\u003C/strong\u003E → \u003Cstrong\u003EAI_FRAMEWORK_DB\u003C/strong\u003E → \u003Cstrong\u003ESILVER_LAYER\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EFind your table (e.g., \u003Ccode\u003EGENERATED_DATA_STRUCTURED\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EClick to view data\u003C/li\u003E\u003Cli\u003EVerify:\n\u003Cul\u003E\u003Cli\u003ERow count matches expected (e.g., 100 individual records)\u003C/li\u003E\u003Cli\u003EAll columns are present\u003C/li\u003E\u003Cli\u003EData looks clean and realistic\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData Quality Check\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Run this query to verify your structured data\nSELECT \n    COUNT(*) as total_records,\n    COUNT(DISTINCT customer_name) as unique_customers,\n    MIN(order_date) as earliest_order,\n    MAX(order_date) as latest_order,\n    SUM(total_amount) as total_revenue\nFROM AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EStep 11: Save Configuration (Optional)\u003C/h4\u003E\n","\u003Cp\u003EIf you clicked \u003Cstrong\u003E"💾 Save Configuration"\u003C/strong\u003E before transforming, your settings are saved for reuse:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESource table selection\u003C/li\u003E\u003Cli\u003ETarget table name\u003C/li\u003E\u003Cli\u003ECompany and topic filters\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ELoad it next time from the configuration dropdown!\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EUnderstanding the Transformation\u003C/h3\u003E\n","\u003Ch4\u003EWhat This App Does\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. Cleans LLM Artifacts\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERemoves incomplete JSON structures\u003C/li\u003E\u003Cli\u003EFixes truncated records\u003C/li\u003E\u003Cli\u003EStrips LLM wrapper text\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2. Flattens JSON Arrays\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EBefore (BRONZE_LAYER):\n[{"customer_id": 1, ...}, {"customer_id": 2, ...}]  ← 1 row, many records\n\nAfter (SILVER_LAYER):\nRow 1: customer_id=1, customer_name=..., email=...\nRow 2: customer_id=2, customer_name=..., email=...  ← Many rows, structured columns\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3. Creates Proper SQL Table\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEach field becomes a column\u003C/li\u003E\u003Cli\u003EEach JSON object becomes a row\u003C/li\u003E\u003Cli\u003EData types inferred automatically\u003C/li\u003E\u003Cli\u003EReady for SQL queries and analysis\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EData Flow\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode\u003EBRONZE_LAYER (Raw Synthetic Data)\n├─ Table: GENERATED_DATA\n├─ Structure: Batched JSON arrays\n├─ Columns: MESSAGES, _META_* fields\n└─ Rows: 10 (one per batch)\n\n         ↓ Transform ↓\n\nSILVER_LAYER (Structured Data)\n├─ Table: GENERATED_DATA_STRUCTURED\n├─ Structure: Individual records in columns\n├─ Columns: CUSTOMER_ID, CUSTOMER_NAME, EMAIL, ORDER_DATE, etc.\n└─ Rows: 100 (individual records)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ECommon Use Cases\u003C/h3\u003E\n","\u003Ch4\u003E\u003Cstrong\u003EFor Dashboard Building (Persona 1)\u003C/strong\u003E\u003C/h4\u003E\n","\u003Cp\u003EAfter transformation, your data is ready for:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EYAML Wizard (create dashboard configs)\u003C/li\u003E\u003Cli\u003ESnow Viz (render dashboards)\u003C/li\u003E\u003Cli\u003EDirect SQL analysis\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E\u003Cstrong\u003EFor SQL Demos (Persona 2)\u003C/strong\u003E\u003C/h4\u003E\n","\u003Cp\u003EStructured tables work with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESQL to YAML Converter\u003C/li\u003E\u003Cli\u003ESnow Demo presentations\u003C/li\u003E\u003Cli\u003ECustom SQL queries\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E\u003Cstrong\u003EFor Data Export (Persona 3)\u003C/strong\u003E\u003C/h4\u003E\n","\u003Cp\u003EExport structured data via:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Export to CSV\nSELECT * \nFROM AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED;\n\n-- Use in Python/Snowpark\nsession.table("AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED").to_pandas()\n\n-- Connect BI tools directly to SILVER_LAYER tables\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EWhat's Next?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor Persona 1\u003C/strong\u003E (Full-Stack Developer):\n→ Continue to \u003Cstrong\u003EPage 10 (YAML Wizard)\u003C/strong\u003E to create dashboard configurations\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor Persona 2\u003C/strong\u003E (SQL Demo Creator):\n→ Continue to \u003Cstrong\u003EPage 8 (SQL to YAML Converter)\u003C/strong\u003E to create demo flows\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor Persona 3\u003C/strong\u003E (Data Preparation):\n→ \u003Cstrong\u003EExport your data\u003C/strong\u003E from SILVER_LAYER or continue to other apps\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor All Personas\u003C/strong\u003E:\nYour data is now in clean, structured format in \u003Ccode\u003ESILVER_LAYER\u003C/code\u003E - ready for analytics, dashboards, demos, or export!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESQL to YAML Converter\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Convert SQL queries into interactive demo configurations for Snow Demo\u003Cbr\u003E\n\u003Cstrong\u003EDependencies\u003C/strong\u003E: Requires tables from App 1 or 2\u003Cbr\u003E\n\u003Cstrong\u003EOutput\u003C/strong\u003E: YAML files for \u003Ccode\u003EFRAMEWORK_YAML_STAGE\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app3-sql-to-yaml.gif\" alt=\"SQL to YAML Converter Demo\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWho Uses This App\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 2\u003C/strong\u003E (SQL Demo Creator): Convert SQL queries to demo YAML for interactive presentations\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EStep-by-Step Instructions\u003C/h3\u003E\n","\u003Ch4\u003EStep 1: Open the App\u003C/h4\u003E\n","\u003Cp\u003ENavigate to \u003Ccode\u003EProjects\u003C/code\u003E → \u003Ccode\u003EStreamlit\u003C/code\u003E → \u003Cstrong\u003E\u003Ccode\u003ESQL_TO_YAML_CONVERTER\u003C/code\u003E\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Choose Input Method\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMain Panel - Input SQL Worksheet Section:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EChoose Input Method:\n◉ Paste SQL\n○ Upload File\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESelect \u003Cstrong\u003EPaste SQL\u003C/strong\u003E to enter your queries directly, or \u003Cstrong\u003EUpload File\u003C/strong\u003E to upload a \u003Ccode\u003E.sql\u003C/code\u003E or \u003Ccode\u003E.txt\u003C/code\u003E file.\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Enter Your SQL Queries\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESQL Input Text Area:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EReplace the placeholder SQL with your actual queries from the structured tables you created:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Step 1: Customer Overview\nSELECT \n    CUSTOMER_NAME,\n    EMAIL,\n    ORDER_DATE,\n    PRODUCT_NAME,\n    TOTAL_AMOUNT\nFROM AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED\nLIMIT 10;\n\n-- Step 2: Revenue by Product\nSELECT \n    PRODUCT_NAME,\n    COUNT(*) as order_count,\n    SUM(TOTAL_AMOUNT) as total_revenue,\n    AVG(TOTAL_AMOUNT) as avg_order_value\nFROM AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED\nGROUP BY PRODUCT_NAME\nORDER BY total_revenue DESC;\n\n-- Step 3: Top Customers Analysis\nSELECT \n    CUSTOMER_NAME,\n    COUNT(*) as total_orders,\n    SUM(TOTAL_AMOUNT) as total_spent,\n    AVG(TOTAL_AMOUNT) as avg_order_value\nFROM AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED\nGROUP BY CUSTOMER_NAME\nORDER BY total_spent DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETips\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUse your actual table names from Structured Tables\u003C/li\u003E\u003Cli\u003EInclude Cortex AI functions for interactive demos\u003C/li\u003E\u003Cli\u003ESeparate steps with SQL comments (\u003Ccode\u003E-- Step X:\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EMix different query types (SELECT, GROUP BY, Cortex functions)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECortex AI Integration\u003C/strong\u003E: The app automatically detects \u003Ccode\u003ESNOWFLAKE.CORTEX.COMPLETE()\u003C/code\u003E calls and creates interactive steps where users can modify prompts and see real-time AI responses!\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 4: Configure Demo Metadata\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDemo Metadata Section (Two Columns):\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Column:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETopic:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECustomer Analytics\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESub-topic:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EOrder Analysis\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETertiary Topic:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ERevenue Insights\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAcme Corp Customer Orders Analytics Dashboard\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERight Column:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELogo URL:\u003C/strong\u003E (optional - leave blank)\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOwner:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EData Analytics Team\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDatabase:\u003C/strong\u003E (leave blank to auto-detect)\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESchema:\u003C/strong\u003E (leave blank to auto-detect)\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOverview Description:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EComprehensive analysis of Acme Corp customer order data showcasing:\n- Customer order patterns and revenue trends\n- Top-performing products and customer segments\n- AI-powered customer insights and recommendations\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETips\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EKeep Topic/Sub-topic/Tertiary Topic hierarchical (broad → specific)\u003C/li\u003E\u003Cli\u003ETitle is the main heading users see\u003C/li\u003E\u003Cli\u003EUse bullet points with \u003Ccode\u003E-\u003C/code\u003E for better formatting in Overview\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 5: Configure Advanced Options (Optional)\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpandable Advanced Options Section:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESQL Block Separator: GO\nRole: (leave blank)\nWarehouse: (leave blank)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDefault settings work for most cases. Only change if you have specific requirements.\u003C/p\u003E\n","\u003Ch4\u003EStep 6: Parse SQL Worksheet\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBottom of Input Section:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EClick the blue \u003Cstrong\u003E[Parse SQL Worksheet]\u003C/strong\u003E button\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat happens\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EApp analyzes your SQL queries\u003C/li\u003E\u003Cli\u003EDetects Cortex AI functions automatically\u003C/li\u003E\u003Cli\u003ESuggests visualizations based on query patterns:\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EGROUP BY\u003C/code\u003E → Bar Chart\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESELECT *\u003C/code\u003E → Table\u003C/li\u003E\u003Cli\u003ECortex functions → Interactive AI steps\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EComments out unsupported commands (USE statements)\u003C/li\u003E\u003Cli\u003EGenerates YAML configuration\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EProcessing time: ~5-10 seconds\u003C/p\u003E\n","\u003Ch4\u003EStep 7: Review Summary Tab\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EResults Section - Tab 1 (Summary):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EKey Metrics:\n- 3 Total Steps\n- 1 Table Referenced\n- 2 Visualization Types\n\nCortex AI Analysis:\n- 0 Cortex Complete calls detected\n- 0 Interactive Cortex steps created\n\nInteractive Steps:\n- None (add CORTEX.COMPLETE() for interactive AI steps)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis shows what the app detected in your SQL and how it will be presented in Snow Demo.\u003C/p\u003E\n","\u003Ch4\u003EStep 8: Review Parsed Blocks Tab\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EResults Section - Tab 2 (Parsed Blocks):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EStep 1: Customer Overview\n- Type: Query\n- Visualization: Table\n- SQL: SELECT CUSTOMER_NAME, EMAIL...\n\nStep 2: Revenue by Product\n- Type: Query  \n- Visualization: Bar Chart\n- SQL: SELECT PRODUCT_NAME, COUNT(*) as order_count...\n\nStep 3: Top Customers Analysis\n- Type: Query\n- Visualization: Table\n- SQL: SELECT CUSTOMER_NAME, COUNT(*) as total_orders...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify all your steps are correctly parsed and visualization types make sense.\u003C/p\u003E\n","\u003Ch4\u003EStep 9: Review Generated YAML\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EResults Section - Tab 3 (Generated YAML):\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EShows the complete YAML configuration that will be used by Snow Demo. This includes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EMetadata (topic, title, owner)\u003C/li\u003E\u003Cli\u003ESQL steps with visualization configurations\u003C/li\u003E\u003Cli\u003EInteractive Cortex AI steps\u003C/li\u003E\u003Cli\u003EExecution flow\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou don't need to edit this manually - it's automatically generated!\u003C/p\u003E\n","\u003Ch4\u003EStep 10: Download or Save Configuration\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EResults Section - Tab 4 (Download & Export):\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConfiguration Name:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECustomer_Analytics_Order_Analysis_Revenue_Insights_20250115\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOption 1: Save to Database\u003C/strong\u003E (Recommended)\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick \u003Cstrong\u003E"Save to Database"\u003C/strong\u003E button\u003C/li\u003E\u003Cli\u003EConfig saved to \u003Ccode\u003EAI_FRAMEWORK_DB.CONFIG.DEMO_CONFIGURATIONS\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOption 2: Download YAML File\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick \u003Cstrong\u003E"Download YAML Configuration"\u003C/strong\u003E button\u003C/li\u003E\u003Cli\u003EDownloads \u003Ccode\u003E.yaml\u003C/code\u003E file for uploading to Snow Demo stage\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EWhat This App Does Automatically\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESQL Analysis\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDetects all Cortex AI function calls\u003C/li\u003E\u003Cli\u003EIdentifies aggregation patterns (GROUP BY, SUM, AVG)\u003C/li\u003E\u003Cli\u003ERecognizes table and database references\u003C/li\u003E\u003Cli\u003EComments out unsupported SQL commands\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVisualization Suggestions\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EGROUP BY\u003C/code\u003E queries → Bar Chart visualizations\u003C/li\u003E\u003Cli\u003ESimple SELECT queries → Table views\u003C/li\u003E\u003Cli\u003ECortex functions → Interactive experimentation panels\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EInteractive AI Steps\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EExtracts prompts from \u003Ccode\u003ECORTEX.COMPLETE()\u003C/code\u003E calls\u003C/li\u003E\u003Cli\u003ECreates editable prompt interfaces\u003C/li\u003E\u003Cli\u003EAllows real-time model/parameter changes\u003C/li\u003E\u003Cli\u003EShows AI responses in demo flow\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYAML Generation\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EProfessional demo structure\u003C/li\u003E\u003Cli\u003ECompatible with Snow Demo harness\u003C/li\u003E\u003Cli\u003EReady for presentations\u003C/li\u003E\u003Cli\u003ENo manual YAML writing needed\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EExample SQL Patterns\u003C/h3\u003E\n","\u003Ch4\u003E\u003Cstrong\u003EBasic Analytics Query\u003C/strong\u003E\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Shows as Table view\nSELECT customer_name, order_date, total_amount\nFROM my_table\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E\u003Cstrong\u003EAggregation Query\u003C/strong\u003E\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Shows as Bar Chart\nSELECT product_category, SUM(revenue) as total_revenue\nFROM my_table\nGROUP BY product_category\nORDER BY total_revenue DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E\u003Cstrong\u003EInteractive Cortex AI\u003C/strong\u003E\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Shows as Interactive AI Panel\nSELECT \n    SNOWFLAKE.CORTEX.COMPLETE('mixtral-8x7b', \n        'Analyze this data: ' || column_name\n    ) as ai_insights\nFROM my_table;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EBest Practices\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWrite clear SQL comments\u003C/strong\u003E: Use \u003Ccode\u003E-- Step X:\u003C/code\u003E format for step detection\u003Cbr\u003E\n\u003Cstrong\u003EInclude Cortex AI\u003C/strong\u003E: Add CORTEX.COMPLETE() for interactive demos\u003Cbr\u003E\n\u003Cstrong\u003EMix query types\u003C/strong\u003E: Combine SELECT, GROUP BY, and AI functions\u003Cbr\u003E\n\u003Cstrong\u003EUse descriptive metadata\u003C/strong\u003E: Clear titles and topics help viewers understand\u003Cbr\u003E\n\u003Cstrong\u003ETest queries first\u003C/strong\u003E: Run SQL in worksheet before converting\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EWhat's Next?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor Persona 2 (SQL Demo Creator)\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EUpload your YAML to Snowflake Stage\u003C/strong\u003E (see upload instructions in Snow Demo section below)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EContinue to Page 9 (Snow Demo)\u003C/strong\u003E to run your interactive presentation\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYour SQL queries are now a professional, interactive demo ready for presentations!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnow Demo\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Run interactive SQL-driven presentations with live visualizations\u003Cbr\u003E\n\u003Cstrong\u003EDependencies\u003C/strong\u003E: Requires YAML configs from App 3 (uploaded to \u003Ccode\u003EFRAMEWORK_YAML_STAGE\u003C/code\u003E)\u003Cbr\u003E\n\u003Cstrong\u003EOutput\u003C/strong\u003E: Live demo orchestration with charts and AI experimentation\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app4-snow-demo.gif\" alt=\"Snow Demo\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWho Uses This App\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 2\u003C/strong\u003E (SQL Demo Creator): Present interactive SQL demos with live AI experimentation\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EUpload YAML to Stage\u003C/h3\u003E\n","\u003Cp\u003EBefore using Snow Demo, upload your YAML file to Snowflake:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EData\u003C/strong\u003E → \u003Cstrong\u003EDatabases\u003C/strong\u003E → \u003Cstrong\u003EAI_FRAMEWORK_DB\u003C/strong\u003E → \u003Cstrong\u003ECONFIGS\u003C/strong\u003E → \u003Cstrong\u003EStages\u003C/strong\u003E → \u003Cstrong\u003EFRAMEWORK_YAML_STAGE\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E"+ Files"\u003C/strong\u003E button\u003C/li\u003E\u003Cli\u003ESelect your downloaded YAML file\u003C/li\u003E\u003Cli\u003EIn path field, enter: \u003Ccode\u003E/analytics/\u003C/code\u003E (or choose: \u003Ccode\u003Esales_demo\u003C/code\u003E, \u003Ccode\u003Ecustomer_insights\u003C/code\u003E, etc.)\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E"Upload"\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EStep-by-Step Instructions\u003C/h3\u003E\n","\u003Ch4\u003EStep 1: Open the App\u003C/h4\u003E\n","\u003Cp\u003ENavigate to \u003Ccode\u003EProjects\u003C/code\u003E → \u003Ccode\u003EStreamlit\u003C/code\u003E → \u003Cstrong\u003E\u003Ccode\u003ESNOW_DEMO\u003C/code\u003E\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Select Area\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar:\u003C/strong\u003E Select the project directory where you uploaded your YAML file (e.g., \u003Ccode\u003Eanalytics\u003C/code\u003E)\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Select Demo\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar:\u003C/strong\u003E Select your YAML configuration file from the dropdown\u003C/p\u003E\n","\u003Ch4\u003EStep 4: Review and Run Demo\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar:\u003C/strong\u003E Review the auto-displayed demo metadata, then click \u003Cstrong\u003E[Run Demo]\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 5: Navigate Demo Steps\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMain Panel:\u003C/strong\u003E Each SQL step appears as a section with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAuto-executed query results\u003C/li\u003E\u003Cli\u003EVisualization selector (Table, Bar Chart, Line Chart, etc.)\u003C/li\u003E\u003Cli\u003EOptional instructions and talk track\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETips:\u003C/strong\u003E Change \u003Cstrong\u003EDisplay Options\u003C/strong\u003E dropdown to switch visualizations on-the-fly\u003C/p\u003E\n","\u003Ch4\u003EStep 6: Interactive Cortex AI (Optional)\u003C/h4\u003E\n","\u003Cp\u003EIf your SQL includes \u003Ccode\u003ESNOWFLAKE.CORTEX.COMPLETE()\u003C/code\u003E calls, you'll see an interactive panel where you can:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EChange the AI model (llama3.1-8b, mixtral-8x7b, etc.)\u003C/li\u003E\u003Cli\u003EAdjust temperature and max tokens\u003C/li\u003E\u003Cli\u003EEdit system and user prompts live\u003C/li\u003E\u003Cli\u003ERe-run queries with different parameters\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELive Audience Engagement\u003C/strong\u003E: Modify AI prompts in real-time during presentations!\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EBest Practices\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrepare ahead\u003C/strong\u003E: Test demo flow before presentations\u003Cbr\u003E\n\u003Cstrong\u003EUse talk tracks\u003C/strong\u003E: Add presenter notes in YAML for guidance\u003Cbr\u003E\n\u003Cstrong\u003EPractice transitions\u003C/strong\u003E: Know when to switch visualizations\u003Cbr\u003E\n\u003Cstrong\u003EEngage audience\u003C/strong\u003E: Ask for prompt suggestions during AI steps\u003Cbr\u003E\n\u003Cstrong\u003EKeep queries fast\u003C/strong\u003E: Use LIMIT clause for demo data\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EWhat's Next?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor Persona 2 (SQL Demo Creator)\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003EYour demo is complete! You can:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERun this demo in presentations\u003C/li\u003E\u003Cli\u003ECreate additional demos with different SQL queries\u003C/li\u003E\u003Cli\u003EEdit YAML to add more steps or visualizations\u003C/li\u003E\u003Cli\u003EShare demo with colleagues by sharing the YAML file\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReturn to Page 5\u003C/strong\u003E to explore other workflows or \u003Cstrong\u003Econtinue to Page 12\u003C/strong\u003E for cleanup instructions.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EYAML Wizard\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Create dashboard configurations through guided interface\u003Cbr\u003E\n\u003Cstrong\u003EDependencies\u003C/strong\u003E: Requires tables from App 1 or 2\u003Cbr\u003E\n\u003Cstrong\u003EOutput\u003C/strong\u003E: YAML files for \u003Ccode\u003EVISUALIZATION_YAML_STAGE\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app5-yaml-wizard.gif\" alt=\"YAML Wizard Demo\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWho Uses This App\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 1\u003C/strong\u003E (Full-Stack Developer): Create dashboard YAML from structured tables for Snow Viz\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EStep-by-Step Instructions\u003C/h3\u003E\n","\u003Ch4\u003EStep 1: Open the App\u003C/h4\u003E\n","\u003Cp\u003ENavigate to \u003Ccode\u003EProjects\u003C/code\u003E → \u003Ccode\u003EStreamlit\u003C/code\u003E → \u003Cstrong\u003E\u003Ccode\u003EYAML_WIZARD\u003C/code\u003E\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Select Data Source\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMain Panel - Top Section:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E◉ Create new (selected by default)\n○ Load existing\n\nDatabase: AI_FRAMEWORK_DB ▼\nSchema: SILVER_LAYER ▼\nTable: TECHCORP_ORDERS_STRUCTURED ▼\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESchema Selection Guide\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESILVER_LAYER\u003C/strong\u003E: Use if you completed Structured Tables (recommended)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBRONZE_LAYER\u003C/strong\u003E: Use if working with raw data directly\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESelect your structured table from the previous steps.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESuccess Check\u003C/strong\u003E: After selecting your table, you should see a preview showing your columns (CUSTOMER_NAME, ORDER_DATE, etc.) and sample data rows.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 3: Configure Dimensions and Metrics\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConfigure Dimensions, Metrics, Time Column Section:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDimensions (Left Column):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESelect text/categorical fields to group by:\n☑ CUSTOMER_NAME\n☑ PRODUCT_NAME\n☐ EMAIL\n☐ ...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck 2-5 key categorical fields you want to analyze.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETime Column (Right Column):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ETime Column for Trends:\nORDER_DATE ▼\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESelect your date/timestamp field for time-series analysis.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMetrics (Below Columns):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAuto-generated metrics from your table:\n☑ total_rows (COUNT(*))\n☑ avg_quantity (AVG(QUANTITY))\n☑ sum_total_amount (SUM(TOTAL_AMOUNT))\n☑ avg_price (AVG(PRICE))\n☐ ...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck 3-7 key metrics you want to calculate. The app automatically creates aggregation functions.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETips\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDon't check ALL metrics - pick the most important 5-10\u003C/li\u003E\u003Cli\u003EDimensions are for grouping (categories, names)\u003C/li\u003E\u003Cli\u003EMetrics are for calculations (numbers, aggregations)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 4: Customize Dimensions\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EClick the "Dimensions" tab\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EFor each dimension, you can customize:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECUSTOMER_NAME:\nLabel: Customer Name\nDescription: Customer who placed the order\nPriority: 0\nUnique Values: (auto-detected)\n\nPRODUCT_NAME:\nLabel: Product\nDescription: Product purchased\nPriority: 1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to customize\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ELabel\u003C/strong\u003E: User-friendly display name (e.g., "Product Category" instead of "PRODUCT_NAME")\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription\u003C/strong\u003E: Help text for users\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPriority\u003C/strong\u003E: Display order (0 = first, 1 = second, etc.)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIMPORTANT\u003C/strong\u003E: After editing, click \u003Cstrong\u003E"Apply All Dimension Changes"\u003C/strong\u003E button at the bottom!\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERequired Step\u003C/strong\u003E: You MUST click "Apply All Dimension Changes" or your edits won't be saved!\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 5: Customize Metrics\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EClick the "Metrics" tab\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EFor each metric, you can customize:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Etotal_rows:\nLabel: Total Orders\nSQL: COUNT(*)\nFormat: number\nDecimals: 0\n\nsum_total_amount:\nLabel: Total Revenue\nSQL: SUM(TOTAL_AMOUNT)\nFormat: currency\nDecimals: 2\n\navg_price:\nLabel: Average Price\nSQL: AVG(PRICE)\nFormat: currency\nDecimals: 2\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to customize\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ELabel\u003C/strong\u003E: User-friendly display name\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESQL\u003C/strong\u003E: The aggregation function (modify if needed)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFormat\u003C/strong\u003E: number, percent, currency, integer\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDecimals\u003C/strong\u003E: Decimal places to display\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIMPORTANT\u003C/strong\u003E: After editing, click \u003Cstrong\u003E"Apply All Metric Changes"\u003C/strong\u003E button at the bottom!\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERequired Step\u003C/strong\u003E: You MUST click "Apply All Metric Changes" or your edits won't be saved!\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 6: Generate Dashboard YAML\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EClick the "Generate" tab\u003C/strong\u003E, then enter:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EApp Name:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAcme Corp Customer Orders Dashboard\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDescription:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EComprehensive analysis of customer order data\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYAML Filename:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eacme_corp_orders_dashboard.yaml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003E"Generate Customized YAML"\u003C/strong\u003E → Generates 8 tabs (Overview, Product/Category, VS, Top N, Self Service, Search, AI Assistant, Raw Data)\u003C/p\u003E\n","\u003Ch4\u003EStep 7: Download and Save\u003C/h4\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003E"Download YAML"\u003C/strong\u003E button\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOptional:\u003C/strong\u003E Click \u003Cstrong\u003E"Save to AI_FRAMEWORK_DB.CONFIGS"\u003C/strong\u003E to save your customizations for later editing\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EUpload YAML to Stage for Snow Viz\u003C/h3\u003E\n","\u003Cp\u003EUpload your YAML file to Snowflake:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EData\u003C/strong\u003E → \u003Cstrong\u003EDatabases\u003C/strong\u003E → \u003Cstrong\u003EAI_FRAMEWORK_DB\u003C/strong\u003E → \u003Cstrong\u003ECONFIGS\u003C/strong\u003E → \u003Cstrong\u003EStages\u003C/strong\u003E → \u003Cstrong\u003EVISUALIZATION_YAML_STAGE\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E"+ Files"\u003C/strong\u003E button\u003C/li\u003E\u003Cli\u003ESelect your downloaded YAML file\u003C/li\u003E\u003Cli\u003EIn path field, enter: \u003Ccode\u003E/customer_orders/\u003C/code\u003E (or your project name)\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E"Upload"\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EUnderstanding the Output\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat You Created\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EYAML Configuration File\u003C/strong\u003E: Recipe for your dashboard\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E8 Interactive Tabs\u003C/strong\u003E: Different ways to explore your data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustomized Labels\u003C/strong\u003E: User-friendly names for dimensions and metrics\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFormatted Metrics\u003C/strong\u003E: Currency, percentages, decimals as configured\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy Two Saves?\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDownload YAML\u003C/strong\u003E: For uploading to stage (Snow Viz needs this)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESave to CONFIGS\u003C/strong\u003E: For editing later (preserves your customizations)\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EWhat to Ignore (Normal Messages)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThese messages are NORMAL for first-time use:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ENo Cortex Search services found in this database/schema\nCreate a Cortex Search service first to enable semantic search\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIgnore this\u003C/strong\u003E - Search services are advanced/optional\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ETable exists but no configurations found\nNo configs saved yet.\nConfiguration table has 0 saved configs\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIgnore this\u003C/strong\u003E - Normal until you save your first config\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EBest Practices\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStart simple\u003C/strong\u003E: Pick 2-3 dimensions and 3-5 metrics for first try\u003Cbr\u003E\n\u003Cstrong\u003EUse clear labels\u003C/strong\u003E: "Product Category" is better than "PRODUCT_CATEGORY"\u003Cbr\u003E\n\u003Cstrong\u003EFormat metrics\u003C/strong\u003E: Use currency for money, percent for rates\u003Cbr\u003E\n\u003Cstrong\u003ESave your work\u003C/strong\u003E: Both download AND save to database\u003Cbr\u003E\n\u003Cstrong\u003ETest in Snow Viz\u003C/strong\u003E: Verify dashboard works as expected\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EWhat's Next?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor Persona 1 (Full-Stack Developer)\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003EYou now have a dashboard configuration file! Next steps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EUpload YAML to stage\u003C/strong\u003E (instructions above)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EContinue to Page 11 (Snow Viz)\u003C/strong\u003E to view your interactive dashboard\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYour data is now ready for visual analytics with 8 interactive dashboard tabs!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnow Viz\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Render advanced interactive dashboards from YAML configurations\u003Cbr\u003E\n\u003Cstrong\u003EDependencies\u003C/strong\u003E: Requires YAML configs from App 5 (uploaded to \u003Ccode\u003EVISUALIZATION_YAML_STAGE\u003C/code\u003E)\u003Cbr\u003E\n\u003Cstrong\u003EOutput\u003C/strong\u003E: Multi-tab analytics dashboards with AI integration\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app6-snow-viz.gif\" alt=\"Snow Viz Demo\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWho Uses This App\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 1\u003C/strong\u003E (Full-Stack Developer): View and validate dashboards after creation\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPersona 4\u003C/strong\u003E (Dashboard Consumer): Explore interactive dashboards and run natural language queries\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EStep-by-Step Instructions\u003C/h3\u003E\n","\u003Ch4\u003EStep 1: Open the App\u003C/h4\u003E\n","\u003Cp\u003ENavigate to \u003Ccode\u003EProjects\u003C/code\u003E → \u003Ccode\u003EStreamlit\u003C/code\u003E → \u003Cstrong\u003E\u003Ccode\u003ESNOW_VIZ\u003C/code\u003E\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Select Configuration Source\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar - Configuration Source:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ELoad from:\n◉ Stage\n○ Local file\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESelect \u003Cstrong\u003EStage\u003C/strong\u003E (recommended - loads from \u003Ccode\u003EVISUALIZATION_YAML_STAGE\u003C/code\u003E)\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Select Project and YAML File\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar - After selecting Stage:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EProject: [Select Project] ▼\nAvailable: techcorp_orders, analytics, sales_dashboard\n\nYAML File: [Select YAML] ▼\nAvailable: techcorp_orders_dashboard.yaml\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ESelect Project\u003C/strong\u003E: Choose the directory where you uploaded your YAML\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESelect YAML File\u003C/strong\u003E: Choose your dashboard configuration file\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe dashboard will automatically load.\u003C/p\u003E\n","\u003Ch4\u003EStep 4: Navigate Dashboard Tabs\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Sidebar - Navigation Section:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESelect Page:\n◉ Overview\n○ Product / Category\n○ VS (Compare)\n○ Top N\n○ Self Service\n○ Search\n○ AI Assistant\n○ Raw Data\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEach tab provides different analytical views of your data.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ETab 1: Overview Dashboard\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMain Panel - Overview Tab:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETime Controls (Top):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ETime Window: last_3_months ▼\nOptions: last_7_days, last_30_days, last_3_months, last_6_months, last_year, all_time\n\nTime Grain: month ▼\nOptions: day, week, month, quarter, year\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Metrics Cards:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E[Total Orders]        [Total Revenue]       [Average Price]\n1,234                $156,789              $127.15\n↑ 12% vs prev        ↑ 8% vs prev         ↓ 3% vs prev\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EMetric cards are interactive - click to select which metric to visualize below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVisualizations:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeft Side: Time Series Chart\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EShows trend line for selected metric over time\n- X-axis: Time periods (based on Time Grain)\n- Y-axis: Metric values\n- Hover for exact values\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERight Side: Ranked Grid\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EDimension: [Select Dimension] ▼\nShows top 10 results in table format:\n| Product Name    | Total Revenue | % of Total |\n|----------------|---------------|------------|\n| Laptop Pro     | $45,678      | 29%        |\n| Wireless Mouse | $23,456      | 15%        |\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ETab 2: Product / Category (Drill-Down)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDimension Analysis:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESelect Dimension: PRODUCT_NAME ▼\nOptions: All configured dimensions\n\nSelect Metric: Total Revenue ▼\nOptions: All configured metrics\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EShows detailed breakdown by selected dimension with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EBar chart visualization\u003C/li\u003E\u003Cli\u003EData table with all values\u003C/li\u003E\u003Cli\u003EFiltering and sorting capabilities\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ETab 3: VS (Compare Entities)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESide-by-Side Comparison:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ELeft Entity: [Select] ▼\nRight Entity: [Select] ▼\n\nMetrics to Compare:\n☑ Total Orders\n☑ Total Revenue\n☑ Average Price\n☐ ...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EComparison Table:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E| Metric        | Laptop Pro | Wireless Mouse | Winner        | Delta    |\n|--------------|------------|----------------|---------------|----------|\n| Total Orders | 456        | 789            | Wireless Mouse| +73%     |\n| Total Revenue| $45,678    | $23,456        | Laptop Pro    | +95%     |\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPerfect for comparing products, customers, or any dimension values.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ETab 4: Top N (Rankings)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELeaderboard Analysis:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESelect Dimension: PRODUCT_NAME ▼\nSelect Metric: Total Revenue ▼\nTop N: 10 ▼\nOptions: 5, 10, 20, 50, 100\n\nSort Order:\n◉ Descending (highest first)\n○ Ascending (lowest first)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EShows ranked list with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EBar chart visualization\u003C/li\u003E\u003Cli\u003ENumeric rankings\u003C/li\u003E\u003Cli\u003EPercentage of total\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ETab 5: Self Service\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECustom Analysis:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESelect Dimensions (grouping):\n☑ PRODUCT_NAME\n☑ CUSTOMER_NAME\n☐ ...\n\nSelect Metrics (calculations):\n☑ Total Revenue\n☑ Average Price\n☐ ...\n\nTime Range: last_3_months ▼\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBuild custom reports by selecting any combination of dimensions and metrics.\u003C/p\u003E\n","\u003Cp\u003EResults show in interactive data table with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESorting by any column\u003C/li\u003E\u003Cli\u003EFiltering capabilities\u003C/li\u003E\u003Cli\u003EExport to CSV option\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ETab 6: AI Assistant (Natural Language Queries)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPowered by Cortex Analyst:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EType your question in natural language:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWhat are the top 3 products by revenue in the last quarter?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003E[Ask Analyst]\u003C/strong\u003E → Select view option (Grid, Bar, or Line chart)\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAI Narrative (Optional):\u003C/strong\u003E Generate AI analysis by selecting a model, adjusting temperature, and clicking \u003Cstrong\u003E[Generate Analysis]\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExample Questions\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"What is the average revenue per customer?"\u003C/li\u003E\u003Cli\u003E"Show me products with revenue greater than $10,000"\u003C/li\u003E\u003Cli\u003E"Which month had the highest number of orders?"\u003C/li\u003E\u003Cli\u003E"Compare revenue between Electronics and Software categories"\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ETab 7: Search (Cortex Search)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic Search (if configured):\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EEnter your search query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Elaptop with high ratings\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003E[Search]\u003C/strong\u003E → Shows relevant records based on semantic similarity\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Requires Cortex Search service to be configured. If not set up, this tab will show a setup message.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ETab 8: Raw Data\u003C/h3\u003E\n","\u003Cp\u003EShows complete dataset in table format with sortable columns and CSV export option.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUse this tab to:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EVerify data quality\u003C/li\u003E\u003Cli\u003EExport raw data\u003C/li\u003E\u003Cli\u003ESee all available fields\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EDashboard Features\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EInteractive Elements\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick metric cards to change visualizations\u003C/li\u003E\u003Cli\u003EHover over charts for detailed values\u003C/li\u003E\u003Cli\u003ESort tables by any column\u003C/li\u003E\u003Cli\u003EFilter and drill down into data\u003C/li\u003E\u003Cli\u003EExport results to CSV\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETime Controls\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAdjust time windows dynamically\u003C/li\u003E\u003Cli\u003EChange time grain (day/week/month)\u003C/li\u003E\u003Cli\u003ESee period-over-period comparisons\u003C/li\u003E\u003Cli\u003EView trends over time\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAI Integration\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENatural language queries (Cortex Analyst)\u003C/li\u003E\u003Cli\u003EAI-generated narratives (Cortex Complete)\u003C/li\u003E\u003Cli\u003ESemantic search (Cortex Search, if configured)\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EUse Cases\u003C/h3\u003E\n","\u003Ch4\u003E\u003Cstrong\u003EFor Persona 1 (Full-Stack Developer)\u003C/strong\u003E\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EValidate dashboard configuration\u003C/li\u003E\u003Cli\u003ETest all tabs and features\u003C/li\u003E\u003Cli\u003EVerify metrics calculate correctly\u003C/li\u003E\u003Cli\u003EShare with business users\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E\u003Cstrong\u003EFor Persona 4 (Dashboard Consumer)\u003C/strong\u003E\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EExplore business metrics\u003C/li\u003E\u003Cli\u003EAsk questions in plain English\u003C/li\u003E\u003Cli\u003ECompare entities side-by-side\u003C/li\u003E\u003Cli\u003EExport data for presentations\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EBest Practices\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExplore systematically\u003C/strong\u003E: Start with Overview, then drill into specific tabs\u003Cbr\u003E\n\u003Cstrong\u003EUse AI Assistant\u003C/strong\u003E: Natural language queries are powerful and intuitive\u003Cbr\u003E\n\u003Cstrong\u003ECompare entities\u003C/strong\u003E: VS tab helps identify top performers\u003Cbr\u003E\n\u003Cstrong\u003EExport insights\u003C/strong\u003E: Share findings via CSV export\u003Cbr\u003E\n\u003Cstrong\u003EAdjust time windows\u003C/strong\u003E: Find the right time range for your analysis\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EWhat's Next?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor Persona 1 (Full-Stack Developer)\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003EYour complete analytics pipeline is built! You've created:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESynthetic data (Synthetic Data Generator)\u003C/li\u003E\u003Cli\u003EStructured tables (Structured Tables)\u003C/li\u003E\u003Cli\u003EDashboard configuration (YAML Wizard)\u003C/li\u003E\u003Cli\u003EInteractive dashboard (Snow Viz)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EShare your dashboard\u003C/strong\u003E with business users and stakeholders!\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor Persona 4 (Dashboard Consumer)\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003EYou now have an interactive analytics dashboard! You can:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EExplore metrics and trends\u003C/li\u003E\u003Cli\u003EAsk questions in plain English\u003C/li\u003E\u003Cli\u003ECompare products/customers/categories\u003C/li\u003E\u003Cli\u003EExport data for presentations\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReturn to Page 5\u003C/strong\u003E to explore other workflows or \u003Cstrong\u003Econtinue to Page 12\u003C/strong\u003E for cleanup instructions.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up Resources\u003C/h2\u003E\n","\u003Ch3\u003ERemove All Created Objects\u003C/h3\u003E\n","\u003Cp\u003EWhen you're ready to remove all the resources created during this quickstart:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-cortex-demo-developer-framework/blob/main/scripts/setup.sql\"\u003Esetup.sql\u003C/a\u003E script\u003C/li\u003E\u003Cli\u003EScroll to the bottom to find the "TEARDOWN SCRIPT" section\u003C/li\u003E\u003Cli\u003EUncomment the teardown statements\u003C/li\u003E\u003Cli\u003ERun the freshly uncommented script to remove all databases, warehouses, roles, and objects\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis will clean up all framework components while preserving any other work in your Snowflake account.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built the complete Cortex AI Demo Framework using Snowflake Cortex AI!\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E6-Application Demo Platform\u003C/strong\u003E: How to build complete demo infrastructure from data generation to visualization\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPersona-Based Workflows\u003C/strong\u003E: How different roles use the framework for their specific needs\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdvanced AI Processing\u003C/strong\u003E: How to implement Cortex AI integration with SENTIMENT, EXTRACT_ANSWER, and COMPLETE functions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProduction-Ready Streamlit Apps\u003C/strong\u003E: How to develop interactive demo platforms with advanced visualizations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERapid Demo Development\u003C/strong\u003E: How to transform weeks of development into minutes of setup\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/user-guide/snowflake-cortex/llm-functions\"\u003ESnowflake Cortex AI Functions\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks\"\u003ESnowflake Notebooks\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n\n## Overview\n\nDemo development is crucial for businesses to showcase their AI capabilities and win new customers. Through rapid prototyping and professional presentation tools, businesses can transform weeks of development into minutes of setup, dramatically accelerating sales cycles and proof-of-concept delivery.\n\nIn this Quickstart, we will build a comprehensive demo development platform called \"Cortex AI Demo Framework\". This demonstrates how to use Snowflake Cortex AI functions to create synthetic data, build interactive analytics, deploy search capabilities, and generate complete demonstration environments.\n\nThis Quickstart showcases the complete Cortex AI Demo Framework with:\n- **6-application integrated demo platform** with Synthetic Data Generator, Structured Tables, SQL to YAML Converter, Snow Demo, YAML Wizard, and Snow Viz\n- **AI-powered data generation** using all Cortex functions\n- **Advanced semantic search** and automated model creation\n- **Cortex Search Service** for intelligent data discovery\n- **Cortex Analyst integration** for natural language queries\n- **Production-ready applications** with professional UI/UX\n\n\n### What You Will Build\n- Complete 6-application integrated demo platform\n- AI-powered synthetic data generation system using Cortex functions\n- Advanced semantic modeling and search capabilities\n- Professional demo orchestration and configuration tools\n- Interactive dashboard creation wizard with database introspection\n- Advanced dashboard renderer with multiple visualization types\n- Interactive Cortex Search Service for semantic discovery\n- Production-ready Streamlit applications with advanced visualizations\n- Reusable framework for rapid demo creation across any industry\n\n### What You Will Learn\n- How to set up a production demo development pipeline with Snowflake\n- How to use Snowflake Notebooks for complex AI demo workflows\n- How to implement all Cortex AI functions (SENTIMENT, EXTRACT_ANSWER, COMPLETE)\n- How to build scalable demo platforms with synthetic data\n- How to create automated semantic models and search services\n- How to deploy interactive Streamlit applications in Snowflake\n\n### Prerequisites\n- Familiarity with Python and SQL\n- Familiarity with Streamlit applications\n- Go to the [Snowflake](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) sign-up page and register for a free account\n\n\u003C!-- ------------------------ --\u003E\n## Setup Snowflake Environment  \n\nIn this step, you'll create the Snowflake database objects and prepare for framework deployment.\n\n### Step 1: Create Database Objects\n\n\u003E \n\u003E Starting in September 2025, Snowflake is gradually upgrading accounts from Worksheets to [Workspaces](https://docs.snowflake.com/en/user-guide/ui-snowsight/workspaces). Workspaces will become the default SQL editor. Follow the instructions below that match your interface.\n\n**If you have Workspaces:**\n1. In Snowsight, click `Projects`, then \u003Ca href=\"https://app.snowflake.com/_deeplink/#/workspaces?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_content=cortex-ai-demo-framework&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003EWorkspaces\u003C/a\u003E in the left navigation\n2. Click `+ Add new` to create a new \u003Ca href=\"https://app.snowflake.com/_deeplink/#/workspaces?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_content=cortex-ai-demo-framework&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003EWorkspace\u003C/a\u003E\n3. Click `SQL File` to create a new SQL file\n4. Copy the setup script from [setup.sql](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/setup.sql) and paste it into your SQL file, then run it\n\n**If you have Worksheets:**\n1. In Snowsight, click `Projects`, then `Worksheets` in the left navigation\n2. Click `+` in the top-right corner to open a new Worksheet\n3. Copy the setup script from [setup.sql](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/setup.sql) and paste it into your worksheet, then run it\n\nThe setup script creates:\n- **Database**: `CORTEX_FRAMEWORK_DB` with `BRONZE_LAYER`, `SILVER_LAYER`, `APPS`, and `CONFIGS` schemas\n- **Role**: `CORTEX_FRAMEWORK_DATA_SCIENTIST` with all necessary permissions  \n- **Warehouse**: `CORTEX_FRAMEWORK_WH` for compute resources\n- **Stages**: `FRAMEWORK_DATA_STAGE`, `SEMANTIC_MODELS`, and `DEMO_CONFIGS` for file uploads\n- **File Formats**: `CSV_FORMAT`, `YAML_FORMAT`, and `JSON_FORMAT` for data processing\n- **AI Access**: `SNOWFLAKE.CORTEX_USER` role for Cortex functions\n\n### Step 2: Download Required Framework Files\n\nDownload these framework files from the GitHub repository:\n\n| File | Purpose | Download Link |\n|------|---------|---------------|\n| **Notebook** | Setup notebook for framework deployment | [cortex_ai_demo_framework_setup.ipynb](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/notebooks/cortex_ai_demo_framework_setup.ipynb) |\n| **Environment File** | Conda environment configuration for latest Streamlit | [environment.yml](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/environment.yml) |\n| **Synthetic Data Generator** | AI-powered synthetic data creation | [01_ai_framework_synthetic_data_generator.py](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/01_ai_framework_synthetic_data_generator.py) |\n| **Structured Tables** | Data structuring and transformation | [02_ai_framework_structured_tables.py](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/02_ai_framework_structured_tables.py) |\n| **SQL to YAML Converter** | SQL to YAML configuration converter (generates semantic models) | [03_ai_framework_sql_to_yaml_converter.py](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/03_ai_framework_sql_to_yaml_converter.py) |\n| **Snow Demo** | Demo configuration and runner | [04_ai_framework_snow_demo.py](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/04_ai_framework_snow_demo.py) |\n| **YAML Wizard** | Interactive dashboard configuration creator | [05_ai_framework_snow_viz_yaml_wizard.py](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/05_ai_framework_snow_viz_yaml_wizard.py) |\n| **Snow Viz** | Advanced visualization dashboard renderer | [06_ai_framework_snow_viz.py](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-ai-demo-framework/blob/main/scripts/06_ai_framework_snow_viz.py) |\n\n### Step 3: Upload Framework Files to Single Stage\n\n1. In Snowsight, change your role to `cortex_ai_demo_data_scientist`\n\n2. Navigate to `Catalog` → `Database Explorer` → `AI_FRAMEWORK_DB` → `APPS` → `Stages`\n\n**Upload all framework files to the single `AI_FRAMEWORK_APPS` stage:**\n\n3. Click on `AI_FRAMEWORK_APPS` stage, then click `Enable Directory Table` and upload all 7 files:\n   - `01_ai_framework_synthetic_data_generator.py`\n   - `02_ai_framework_structured_tables.py`\n   - `03_ai_framework_sql_to_yaml_converter.py`\n   - `04_ai_framework_snow_demo.py`\n   - `05_ai_framework_snow_viz_yaml_wizard.py`\n   - `06_ai_framework_snow_viz.py`\n   - `environment.yml`\n\n### Step 4: Import the Framework Setup Notebook\n\n1. **Import into Snowflake**:\n   - Navigate to `Projects` → `Notebooks` in Snowsight\n   - Click the down arrow next to `+ Notebook` and select `Import .ipynb file`\n   - Choose `cortex_ai_demo_framework_setup.ipynb` from your downloads\n\n2. **Configure the notebook settings**:\n   - **Role**: Select `cortex_ai_demo_data_scientist`\n   - **Database**: Select `AI_FRAMEWORK_DB`\n   - **Schema**: Select `BRONZE_LAYER`  \n   - **Query Warehouse**: Select `cortex_ai_demo_wh`\n   - **Notebook Warehouse**: Select `cortex_ai_demo_wh`\n\n3. **Click `Create`** to import the notebook\n\nThe notebook creates all 6 Streamlit applications using the single stage approach with automatic environment.yml detection for the latest Streamlit version.\n\n\u003C!-- ------------------------ --\u003E\n## Run Framework Demo Notebook\n\n### Execute the Framework Deployment Workflow\n\n1. Go to `Projects` → `Notebooks` in Snowsight\n2. Click on `CORTEX_FRAMEWORK_DEMO` Notebook to open it\n3. Click `Run all` to execute all cells in the notebook at once\n\n**What the notebook does:**\n- Creates sample customer survey data tables\n- Processes data with Cortex AI functions (SENTIMENT, EXTRACT_ANSWER, COMPLETE)\n- Deploys all 6 Streamlit applications from the uploaded stage files\n- Sets up the complete framework for immediate demo creation\n\nThe notebook processes sample data and deploys the complete framework application suite.\n\n\u003C!-- ------------------------ --\u003E\n## Framework Overview\n\n### Access Your Demo Framework\n\n1. Navigate to `Projects` → `Streamlit` in Snowsight\n2. You'll see 6 framework applications deployed\n\n### The 6 Applications\n\n#### **1. Synthetic Data Generator** 🎲 (Always Start Here)\nCreates realistic AI-powered datasets using Cortex LLMs. Saves raw JSON to `BRONZE_LAYER` tables.\n\n#### **2. Structured Tables** 🔄\nTransforms raw JSON into clean, structured database tables. Outputs analytics-ready data to `SILVER_LAYER`.\n\n#### **3. SQL to YAML Converter** ⚙️\nConverts SQL queries into interactive demo configurations for Snow Demo (App 4).\n\n#### **4. Snow Demo** 📊\nRuns interactive SQL-driven presentations with live visualizations and AI experimentation.\n\n#### **5. YAML Wizard** 🧙\nGuided dashboard configuration creator. Generates YAML files for Snow Viz (App 6).\n\n#### **6. Snow Viz** 📈\nRenders advanced interactive dashboards with multi-tab analytics and AI integration.\n\n### Application Dependencies\n\n```console\n1. SYNTHETIC DATA GENERATOR (START HERE)\n   └─ Creates realistic datasets\n      │\n      ├─ 2. STRUCTURED TABLES\n      │  └─ Transforms JSON → SQL tables\n      │     │\n      │     └─ 5. YAML WIZARD\n      │        └─ Generates dashboard configs\n      │           │\n      │           └─ 6. SNOW VIZ\n      │              └─ Renders dashboards\n      │\n      └─ 3. SQL TO YAML CONVERTER\n         └─ Converts queries → demo configs\n            │\n            └─ 4. SNOW DEMO\n               └─ Runs interactive SQL demos\n```\n\n**Next**: Page 5 shows which apps to use based on your role and goals.\n\n\u003C!-- ------------------------ --\u003E\n## Persona Workflows\n\n### Who Should Use This Framework?\n\nThe framework supports **4 different user personas**. Find your role below to see which apps you need and in what order.\n\n---\n\n### Persona 1: Full-Stack Data Developer\n\n**Who You Are**:\n- Data engineers building end-to-end pipelines\n- Analytics developers creating dashboards\n- Technical users who want the complete experience\n\n**What You'll Build**: A complete analytics pipeline from data generation to interactive dashboards\n\n**Apps You'll Use**: Synthetic Data Generator → Structured Tables → YAML Wizard → Snow Viz\n\n**Time Required**: ~25 minutes\n\n**Your Workflow**:\n1. **Synthetic Data Generator**: Generate synthetic data\n2. **Structured Tables**: Transform JSON to structured table\n3. **YAML Wizard**: Create dashboard configuration\n4. **Snow Viz**: View your interactive dashboard\n\n**What You'll Get**:\n- Synthetic dataset with realistic values\n- Clean, structured database table\n- Interactive dashboard with multiple visualization tabs\n\n---\n\n### Persona 2: SQL Demo Creator / Solutions Architect\n\n**Who You Are**:\n- Solutions architects building customer demos\n- Technical evangelists presenting Snowflake capabilities\n- Demo creators showcasing SQL + AI features\n\n**What You'll Build**: Interactive SQL-driven presentations with live query execution and AI experimentation\n\n**Apps You'll Use**: Synthetic Data Generator → Structured Tables → SQL to YAML Converter → Snow Demo\n\n**Time Required**: ~30 minutes\n\n**Your Workflow**:\n1. **Synthetic Data Generator**: Generate synthetic data for demos\n2. **Structured Tables**: Create structured table\n3. **SQL to YAML Converter**: Write SQL queries and convert to demo format\n4. **Snow Demo**: Run interactive SQL presentation\n\n**What You'll Get**:\n- Realistic demo dataset\n- Multi-step SQL presentation\n- Interactive visualizations with live AI experimentation\n\n---\n\n### Persona 3: Data Preparation Specialist\n\n**Who You Are**:\n- Data scientists needing training data\n- ML engineers requiring test datasets\n- BI developers prototyping dashboards\n\n**What You'll Build**: Clean, structured datasets for export to external tools (notebooks, ML pipelines, BI tools)\n\n**Apps You'll Use**: Synthetic Data Generator → Structured Tables\n\n**Time Required**: ~15 minutes\n\n**Your Workflow**:\n1. **Synthetic Data Generator**: Generate synthetic data\n2. **Structured Tables**: Transform to structured table\n3. Export data via CSV, Python/Snowpark, or direct BI tool connections\n\n**What You'll Get**:\n- Production-ready synthetic datasets\n- Validated data quality\n- Export-ready structured tables\n\n---\n\n### Persona 4: Dashboard Consumer / Executive\n\n**Who You Are**:\n- Business executives viewing insights\n- Managers making data-driven decisions\n- Analysts exploring pre-built dashboards\n\n**What You'll Do**: View and interact with dashboards created by your data team (no setup required)\n\n**Apps You'll Use**: Snow Viz only (after colleague completes setup)\n\n**Time Required**: ~5 minutes\n\n**Prerequisites**:\nA colleague must first complete Synthetic Data Generator → Structured Tables → YAML Wizard to create the dashboard. Once that's done, you can view and explore it.\n\n**Your Workflow**:\n1. **Snow Viz**: Open app and select dashboard\n2. Explore tabs with different visualization types\n3. Use AI Assistant to ask questions in plain English\n4. Export data to CSV for further analysis\n\n**What You Can Do**:\n- View key metrics and trends\n- Ask questions in natural language\n- Export results to spreadsheets\n\n---\n\n### Choose Your Path\n\n**Ready to get started?** Jump to the pages for your persona:\n\n| Persona | Apps to Follow | What You'll Build |\n|---------|----------------|-------------------|\n| **Full-Stack Developer** | Synthetic Data Generator → Structured Tables → YAML Wizard → Snow Viz | Complete analytics pipeline with dashboards |\n| **SQL Demo Creator** | Synthetic Data Generator → Structured Tables → SQL to YAML Converter → Snow Demo | Interactive SQL presentations with AI |\n| **Data Preparation** | Synthetic Data Generator → Structured Tables | Clean datasets for ML/BI/external tools |\n| **Dashboard Consumer** | Snow Viz only | Explore pre-built dashboards (no setup) |\n\n**Or read all app instructions** (Pages 6-11) to understand the full framework capabilities.\n\n\u003C!-- ------------------------ --\u003E\n## Synthetic Data Generator\n\n**Purpose**: Create realistic AI-powered datasets for any business scenario using Cortex LLMs  \n**Dependencies**: None (START HERE)  \n**Output**: Raw JSON data saved to `BRONZE_LAYER` tables\n\n![Synthetic Data Generator Demo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app1-synthetic-data-generator.gif)\n\n### Who Uses This App\n\n**All Personas start here!** This is the foundation of the framework.\n\n- **Persona 1** (Full-Stack Developer): Generate 100 records for dashboards\n- **Persona 2** (SQL Demo Creator): Generate 150 records for presentations  \n- **Persona 3** (Data Preparation): Generate 300+ records for ML/BI export\n- **Persona 4** (Dashboard Consumer): Your colleague uses this to create data for you\n\n---\n\n### Step-by-Step Instructions\n\n#### Step 1: Open the App\n\nNavigate to `Projects` → `Streamlit` → **`SYNTHETIC_DATA_GENERATOR`**\n\n#### Step 2: Configuration Management (Optional)\n\n**Left Sidebar - Top Section:**\n\nFor first-time use, leave \"Load Configuration\" as **Create New**. If you have saved configurations, select one from dropdown and click **📁 Load Configuration**.\n\n#### Step 3: Dataset Configuration\n\n**Left Sidebar:**\n\nEnter your company name and topic/domain:\n\n```\nAcme Corp\n```\n\n```\nCustomer Orders\n```\n\n**Other Examples**:\n- ShopSmart + \"Product Sales\"\n- MedCenter + \"Patient Vitals\"\n- FinanceFirst + \"Loan Applications\"\n\n#### Step 4: Define Data Fields\n\n**Left Sidebar - Fields Section:**\n\nEnter your fields (one per line):\n\n```\ncustomer_id\ncustomer_name\nemail\norder_date\nproduct_name\nquantity\nprice\ntotal_amount\n```\n\n**Tips**:\n- One field per line\n- Use descriptive field names\n- Include date fields for time-series analysis\n- Add 6-10 fields for realistic datasets\n\n#### Step 5: Batch Configuration\n\n**Left Sidebar:**\n\nSet your batch configuration using the sliders:\n\n- **Records per Batch**: `10` (Slider: 10-200, step 10)\n- **Number of Batches**: `10` (Slider: 1-1000)\n- **Total records to generate**: `100`\n\n\u003E \n\u003E **Recommended Settings**:\n\u003E - **Testing**: 10 records × 10 batches = 100 records (~2-3 min)\n\u003E - **Demos**: 30 records × 15 batches = 450 records (~8-10 min)\n\u003E - **Production**: 50 records × 20 batches = 1000 records (~15-20 min)\n\n**Why smaller batches?**\n- Higher accuracy (95%+ valid JSON)\n- Faster generation per batch\n- Better error recovery\n\n#### Step 6: Configure Cortex LLM\n\n**Left Sidebar:**\n\nConfigure the Cortex LLM settings:\n\n- **Model Type**: `LARGE` (Options: SMALL, MEDIUM, LARGE)\n- **Model**: `mistral-large2` (Recommended for consistent results)\n- **Temperature**: `0.7` (Slider: 0.0-1.0, step 0.1)\n- **Max Tokens**: `4000` (Slider: 100-8000, step 100)\n\n\u003E \n\u003E **Model Selection Guide**:\n\u003E - **mistral-large2** (LARGE): Best accuracy, handles any batch size\n\u003E - **mixtral-8x7b** (MEDIUM): Good balance, use ≤30 records/batch\n\u003E - **llama3.1-8b** (SMALL): Fastest, use ≤20 records/batch\n\u003E \n\u003E **Temperature Guide**:\n\u003E - **0.1-0.3**: Medical/financial data (high consistency)\n\u003E - **0.7**: General business data (balanced)\n\u003E - **0.9**: Creative content (reviews, feedback)\n\n#### Step 7: Performance Configuration\n\n**Left Sidebar:**\n\n- ☑ **High-Performance Mode** (Uses stored procedures - RECOMMENDED)\n- ☐ **Show Manual Scripts** (Leave unchecked unless you need SQL)\n\nKeep \"High-Performance Mode\" checked for best results!\n\n#### Step 8: Auto-save Configuration\n\n**Left Sidebar:**\n\nCheck the following options:\n\n- ☑ **Auto-save batches to table**\n- **Database**: `AI_FRAMEWORK_DB`\n- **Schema**: `BRONZE_LAYER`\n- **Table Name**: `GENERATED_DATA`\n- ☑ **Append to existing table**\n\n\u003E \n\u003E **Important**: Data saves to `BRONZE_LAYER` first. You'll transform it to `SILVER_LAYER` in App 2!\n\n#### Step 9: Generate Data\n\n1. Click **\"Generate Default Prompts\"** → Review/edit prompts if needed\n2. Click **\"🎲 Generate Synthetic Data\"** → Wait ~2-3 minutes\n3. Watch progress: Batch 1/10... 10/10\n\n#### Step 10: Verify Success\n\n**Expected Output**:\n```\nGenerated 100 records successfully!\nData saved to: AI_FRAMEWORK_DB.BRONZE_LAYER.GENERATED_DATA\n\nSample data preview:\n| CUSTOMER_NAME | PRODUCT_NAME | QUANTITY | PRICE | TOTAL_AMOUNT |\n|---------------|--------------|----------|-------|--------------|\n| Sarah Johnson | Laptop Pro   | 1        | 1299  | 1299         |\n| Mike Chen     | Wireless Mouse| 2       | 29    | 58           |\n```\n\n**Verification Steps**:\n\n1. Go to Snowsight → **Data** → **Databases** → **AI_FRAMEWORK_DB** → **BRONZE_LAYER**\n2. Find your table (e.g., `GENERATED_DATA`)\n3. Click to view:\n   - Should see **10 rows** (one per batch)\n   - Each row has **MESSAGES** column with JSON array\n   - Check **_META_** columns for generation metadata\n\n**Data Quality Check**:\n```sql\n-- Run this query to check your data\nSELECT \n    COUNT(*) as total_batches,\n    SUM(_META_RECORDS_IN_BATCH) as total_records,\n    AVG(_META_RECORDS_IN_BATCH) as avg_records_per_batch,\n    _META_COMPANY_NAME,\n    _META_TOPIC\nFROM AI_FRAMEWORK_DB.BRONZE_LAYER.GENERATED_DATA\nGROUP BY _META_COMPANY_NAME, _META_TOPIC;\n```\n\nExpected: 10 batches, 100 total records\n\n#### Step 11: Save Configuration (Optional)\n\n**Bottom of Main Panel:**\n\nEnter a configuration name and click **💾 Save Configuration**:\n\n```\nAcme_Corp_Customer_Orders_Config\n```\n\nSave your configuration to reuse later with different batch sizes or models!\n\n---\n\n### Common Use Cases\n\n#### **Retail / E-commerce**\n```\nCompany: ShopSmart\nTopic: Product Sales\nFields: product_id, product_name, category, sale_date, sale_amount, \n        customer_segment, region, payment_method\n```\n\n#### **Healthcare**\n```\nCompany: MedCenter\nTopic: Patient Vitals\nFields: patient_id, age, gender, blood_pressure_systolic, \n        blood_pressure_diastolic, heart_rate, temperature, \n        oxygen_saturation, recorded_date\n```\n\n#### **Financial Services**\n```\nCompany: FinanceFirst\nTopic: Loan Applications\nFields: application_id, applicant_name, loan_amount, credit_score, \n        income, employment_status, application_date, approval_status\n```\n\n---\n\n### Best Practices\n\n**Start small**: Test with 10 records × 10 batches first  \n**Use mistral-large2**: Best accuracy across all scenarios  \n**Name tables descriptively**: Include company/topic in table name  \n**Save configurations**: Reuse settings for consistent results  \n**Check data quality**: Verify first batch before generating more  \n**Use appropriate temperature**: Low for factual, high for creative\n\n---\n\n### What's Next?\n\n**For All Personas**:\n→ Continue to **Page 7 (App 2 - Structured Tables)** to transform your data from `BRONZE_LAYER` to `SILVER_LAYER`\n\nYour data is now in raw JSON format. App 2 will clean and structure it into proper database columns!\n\n\u003C!-- ------------------------ --\u003E\n## Structured Tables\n\n**Purpose**: Transform raw JSON data into clean, structured database tables  \n**Dependencies**: Requires data from App 1  \n**Output**: Analytics-ready data in `SILVER_LAYER` tables\n\n![Structured Tables Demo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app2-structured-tables.gif)\n\n### Who Uses This App\n\n- **Persona 1** (Full-Stack Developer): Transform to structured tables for dashboards\n- **Persona 2** (SQL Demo Creator): Clean data for SQL presentations\n- **Persona 3** (Data Preparation): Structure data before export to ML/BI tools\n- **Persona 4** (Dashboard Consumer): Your colleague uses this to prepare data\n\n---\n\n### Step-by-Step Instructions\n\n#### Step 1: Open the App\n\nNavigate to `Projects` → `Streamlit` → **`STRUCTURED_TABLES`**\n\n#### Step 2: Select Source Table\n\n**Main Panel - Left Column:**\n\nSelect source table with synthetic data from the dropdown (e.g., `GENERATED_DATA`).\n\nThe dropdown shows all tables from `BRONZE_LAYER` that contain a `MESSAGES` column (generated by Synthetic Data Generator).\n\n#### Step 3: Configure Target Table Name\n\n**Main Panel - Right Column:**\n\nEnter name for structured table (e.g., `GENERATED_DATA_STRUCTURED`).\n\nThe app auto-fills this by adding `_STRUCTURED` to your source table name. You can customize it if needed.\n\n#### Step 4: Filter by Company and Topic\n\n**Main Panel - Filter Section:**\n\nSelect the company and topic you used when generating data in Step 1 from the dropdowns (e.g., `Acme Corp` and `Customer Orders`).\n\nThese dropdowns populate automatically from your source table's metadata (`_meta_company_name` and `_meta_topic` columns).\n\n#### Step 5: Review Data Quality Analysis\n\n**Auto-generated after selection:**\n\n```\n📊 Data Quality Analysis\n\nLeft Column:\nTotal Records: 10\nValid JSON: 10\n\nMiddle Column:\nInvalid JSON: 0\nVery Short: 0\n\nRight Column:\nAvg Length: 2,500 chars\n```\n\n**What to look for**:\n- **Valid JSON** should match **Total Records** (if using mistral-large2)\n- **Invalid JSON** should be 0 or very low\n- **Very Short** indicates truncated records\n\n\u003E \n\u003E **Good Quality Indicators**:\n\u003E - Valid JSON = Total Records (100% success rate)\n\u003E - Invalid JSON = 0 (no errors)\n\u003E - Avg Length \u003E 1,000 chars (complete records)\n\n#### Step 6: Preview Sample Data\n\n**Sample of Cleaned Data section:**\n\n```\n| MESSAGES | _META_COMPANY_NAME | _META_TOPIC | _META_RECORDS_IN_BATCH |\n|----------|-------------------|-------------|------------------------|\n| [{\"customer_id\": 1, ...}] | Acme Corp | Customer Orders | 10 |\n```\n\nThis shows your raw BRONZE_LAYER data with JSON arrays in the `MESSAGES` column.\n\n#### Step 7: Review Fields Analysis\n\n**Auto-detected fields:**\n\n```\n🔍 Fields Analysis\n\nFound 8 fields: customer_id, customer_name, email, order_date, \n                 product_name, quantity, price, total_amount\n\n📝 View SQL Column Names (expandable):\nSQL column names: CUSTOMER_ID, CUSTOMER_NAME, EMAIL, ORDER_DATE, \n                  PRODUCT_NAME, QUANTITY, PRICE, TOTAL_AMOUNT\n```\n\nThe app automatically detects field names from your JSON structure and shows how they'll appear as SQL column names (uppercase).\n\n**Verify** all your expected fields are detected!\n\n#### Step 8: Transform Data\n\n**Bottom Section:**\n\n**Configuration name:**\n```\nAcme_Corp_Customer_Orders_GENERATED_DATA\n```\n\n1. **Optional**: Edit the configuration name if you want to save settings\n2. Click **\"🔄 Transform Data\"** button\n\n**Progress indicator**:\n```\nTransforming data...\n```\n\nThis process:\n- Cleans LLM artifacts from JSON\n- Flattens JSON arrays to individual rows\n- Creates structured table in `SILVER_LAYER`\n- Validates data quality\n\n**Expected time**: 30 seconds to 2 minutes depending on data volume\n\n#### Step 9: Verify Success\n\n**Expected Output**:\n\n```\nSuccessfully transformed data to table: GENERATED_DATA_STRUCTURED\n\n📋 Sample of Transformed Data\n\n| CUSTOMER_ID | CUSTOMER_NAME | EMAIL | ORDER_DATE | PRODUCT_NAME | QUANTITY | PRICE | TOTAL_AMOUNT |\n|-------------|---------------|-------|------------|--------------|----------|-------|--------------|\n| 1 | Sarah Johnson | sarah.j@email.com | 2024-03-15 | Laptop Pro | 1 | 1299 | 1299 |\n| 2 | Mike Chen | mike.c@email.com | 2024-03-12 | Wireless Mouse | 2 | 29 | 58 |\n\n📊 Transformation Summary:\nRecords processed: 100\nTarget table: AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED\n```\n\n**What happened**:\n- **Before**: 10 rows in BRONZE_LAYER (batches with JSON arrays)\n- **After**: 100 rows in SILVER_LAYER (individual records with columns)\n\n#### Step 10: Verify in Snowsight\n\n**Verification Steps**:\n\n1. Go to Snowsight → **Data** → **Databases** → **AI_FRAMEWORK_DB** → **SILVER_LAYER**\n2. Find your table (e.g., `GENERATED_DATA_STRUCTURED`)\n3. Click to view data\n4. Verify:\n   - Row count matches expected (e.g., 100 individual records)\n   - All columns are present\n   - Data looks clean and realistic\n\n**Data Quality Check**:\n```sql\n-- Run this query to verify your structured data\nSELECT \n    COUNT(*) as total_records,\n    COUNT(DISTINCT customer_name) as unique_customers,\n    MIN(order_date) as earliest_order,\n    MAX(order_date) as latest_order,\n    SUM(total_amount) as total_revenue\nFROM AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED;\n```\n\n#### Step 11: Save Configuration (Optional)\n\nIf you clicked **\"💾 Save Configuration\"** before transforming, your settings are saved for reuse:\n- Source table selection\n- Target table name\n- Company and topic filters\n\nLoad it next time from the configuration dropdown!\n\n---\n\n### Understanding the Transformation\n\n#### What This App Does\n\n**1. Cleans LLM Artifacts**:\n- Removes incomplete JSON structures\n- Fixes truncated records\n- Strips LLM wrapper text\n\n**2. Flattens JSON Arrays**:\n```\nBefore (BRONZE_LAYER):\n[{\"customer_id\": 1, ...}, {\"customer_id\": 2, ...}]  ← 1 row, many records\n\nAfter (SILVER_LAYER):\nRow 1: customer_id=1, customer_name=..., email=...\nRow 2: customer_id=2, customer_name=..., email=...  ← Many rows, structured columns\n```\n\n**3. Creates Proper SQL Table**:\n- Each field becomes a column\n- Each JSON object becomes a row\n- Data types inferred automatically\n- Ready for SQL queries and analysis\n\n#### Data Flow\n\n```\nBRONZE_LAYER (Raw Synthetic Data)\n├─ Table: GENERATED_DATA\n├─ Structure: Batched JSON arrays\n├─ Columns: MESSAGES, _META_* fields\n└─ Rows: 10 (one per batch)\n\n         ↓ Transform ↓\n\nSILVER_LAYER (Structured Data)\n├─ Table: GENERATED_DATA_STRUCTURED\n├─ Structure: Individual records in columns\n├─ Columns: CUSTOMER_ID, CUSTOMER_NAME, EMAIL, ORDER_DATE, etc.\n└─ Rows: 100 (individual records)\n```\n\n---\n\n### Common Use Cases\n\n#### **For Dashboard Building (Persona 1)**\nAfter transformation, your data is ready for:\n- YAML Wizard (create dashboard configs)\n- Snow Viz (render dashboards)\n- Direct SQL analysis\n\n#### **For SQL Demos (Persona 2)**\nStructured tables work with:\n- SQL to YAML Converter\n- Snow Demo presentations\n- Custom SQL queries\n\n#### **For Data Export (Persona 3)**\nExport structured data via:\n```sql\n-- Export to CSV\nSELECT * \nFROM AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED;\n\n-- Use in Python/Snowpark\nsession.table(\"AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED\").to_pandas()\n\n-- Connect BI tools directly to SILVER_LAYER tables\n```\n\n---\n\n### What's Next?\n\n**For Persona 1** (Full-Stack Developer):\n→ Continue to **Page 10 (YAML Wizard)** to create dashboard configurations\n\n**For Persona 2** (SQL Demo Creator):\n→ Continue to **Page 8 (SQL to YAML Converter)** to create demo flows\n\n**For Persona 3** (Data Preparation):\n→ **Export your data** from SILVER_LAYER or continue to other apps\n\n**For All Personas**:\nYour data is now in clean, structured format in `SILVER_LAYER` - ready for analytics, dashboards, demos, or export!\n\n\u003C!-- ------------------------ --\u003E\n## SQL to YAML Converter\n\n**Purpose**: Convert SQL queries into interactive demo configurations for Snow Demo  \n**Dependencies**: Requires tables from App 1 or 2  \n**Output**: YAML files for `FRAMEWORK_YAML_STAGE`\n\n![SQL to YAML Converter Demo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app3-sql-to-yaml.gif)\n\n### Who Uses This App\n\n- **Persona 2** (SQL Demo Creator): Convert SQL queries to demo YAML for interactive presentations\n\n---\n\n### Step-by-Step Instructions\n\n#### Step 1: Open the App\n\nNavigate to `Projects` → `Streamlit` → **`SQL_TO_YAML_CONVERTER`**\n\n#### Step 2: Choose Input Method\n\n**Main Panel - Input SQL Worksheet Section:**\n\n```\nChoose Input Method:\n◉ Paste SQL\n○ Upload File\n```\n\nSelect **Paste SQL** to enter your queries directly, or **Upload File** to upload a `.sql` or `.txt` file.\n\n#### Step 3: Enter Your SQL Queries\n\n**SQL Input Text Area:**\n\nReplace the placeholder SQL with your actual queries from the structured tables you created:\n\n```sql\n-- Step 1: Customer Overview\nSELECT \n    CUSTOMER_NAME,\n    EMAIL,\n    ORDER_DATE,\n    PRODUCT_NAME,\n    TOTAL_AMOUNT\nFROM AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED\nLIMIT 10;\n\n-- Step 2: Revenue by Product\nSELECT \n    PRODUCT_NAME,\n    COUNT(*) as order_count,\n    SUM(TOTAL_AMOUNT) as total_revenue,\n    AVG(TOTAL_AMOUNT) as avg_order_value\nFROM AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED\nGROUP BY PRODUCT_NAME\nORDER BY total_revenue DESC;\n\n-- Step 3: Top Customers Analysis\nSELECT \n    CUSTOMER_NAME,\n    COUNT(*) as total_orders,\n    SUM(TOTAL_AMOUNT) as total_spent,\n    AVG(TOTAL_AMOUNT) as avg_order_value\nFROM AI_FRAMEWORK_DB.SILVER_LAYER.GENERATED_DATA_STRUCTURED\nGROUP BY CUSTOMER_NAME\nORDER BY total_spent DESC\nLIMIT 10;\n```\n\n**Tips**:\n- Use your actual table names from Structured Tables\n- Include Cortex AI functions for interactive demos\n- Separate steps with SQL comments (`-- Step X:`)\n- Mix different query types (SELECT, GROUP BY, Cortex functions)\n\n\u003E \n\u003E **Cortex AI Integration**: The app automatically detects `SNOWFLAKE.CORTEX.COMPLETE()` calls and creates interactive steps where users can modify prompts and see real-time AI responses!\n\n#### Step 4: Configure Demo Metadata\n\n**Demo Metadata Section (Two Columns):**\n\n**Left Column:**\n\n**Topic:**\n```\nCustomer Analytics\n```\n\n**Sub-topic:**\n```\nOrder Analysis\n```\n\n**Tertiary Topic:**\n```\nRevenue Insights\n```\n\n**Title:**\n```\nAcme Corp Customer Orders Analytics Dashboard\n```\n\n**Right Column:**\n\n**Logo URL:** (optional - leave blank)\n\n**Owner:**\n```\nData Analytics Team\n```\n\n**Database:** (leave blank to auto-detect)\n\n**Schema:** (leave blank to auto-detect)\n\n**Overview Description:**\n```\nComprehensive analysis of Acme Corp customer order data showcasing:\n- Customer order patterns and revenue trends\n- Top-performing products and customer segments\n- AI-powered customer insights and recommendations\n```\n\n**Tips**:\n- Keep Topic/Sub-topic/Tertiary Topic hierarchical (broad → specific)\n- Title is the main heading users see\n- Use bullet points with `-` for better formatting in Overview\n\n#### Step 5: Configure Advanced Options (Optional)\n\n**Expandable Advanced Options Section:**\n\n```\nSQL Block Separator: GO\nRole: (leave blank)\nWarehouse: (leave blank)\n```\n\nDefault settings work for most cases. Only change if you have specific requirements.\n\n#### Step 6: Parse SQL Worksheet\n\n**Bottom of Input Section:**\n\nClick the blue **[Parse SQL Worksheet]** button\n\n**What happens**:\n1. App analyzes your SQL queries\n2. Detects Cortex AI functions automatically\n3. Suggests visualizations based on query patterns:\n   - `GROUP BY` → Bar Chart\n   - `SELECT *` → Table\n   - Cortex functions → Interactive AI steps\n4. Comments out unsupported commands (USE statements)\n5. Generates YAML configuration\n\nProcessing time: ~5-10 seconds\n\n#### Step 7: Review Summary Tab\n\n**Results Section - Tab 1 (Summary):**\n\n```\nKey Metrics:\n- 3 Total Steps\n- 1 Table Referenced\n- 2 Visualization Types\n\nCortex AI Analysis:\n- 0 Cortex Complete calls detected\n- 0 Interactive Cortex steps created\n\nInteractive Steps:\n- None (add CORTEX.COMPLETE() for interactive AI steps)\n```\n\nThis shows what the app detected in your SQL and how it will be presented in Snow Demo.\n\n#### Step 8: Review Parsed Blocks Tab\n\n**Results Section - Tab 2 (Parsed Blocks):**\n\n```\nStep 1: Customer Overview\n- Type: Query\n- Visualization: Table\n- SQL: SELECT CUSTOMER_NAME, EMAIL...\n\nStep 2: Revenue by Product\n- Type: Query  \n- Visualization: Bar Chart\n- SQL: SELECT PRODUCT_NAME, COUNT(*) as order_count...\n\nStep 3: Top Customers Analysis\n- Type: Query\n- Visualization: Table\n- SQL: SELECT CUSTOMER_NAME, COUNT(*) as total_orders...\n```\n\nVerify all your steps are correctly parsed and visualization types make sense.\n\n#### Step 9: Review Generated YAML\n\n**Results Section - Tab 3 (Generated YAML):**\n\nShows the complete YAML configuration that will be used by Snow Demo. This includes:\n- Metadata (topic, title, owner)\n- SQL steps with visualization configurations\n- Interactive Cortex AI steps\n- Execution flow\n\nYou don't need to edit this manually - it's automatically generated!\n\n#### Step 10: Download or Save Configuration\n\n**Results Section - Tab 4 (Download & Export):**\n\n**Configuration Name:**\n```\nCustomer_Analytics_Order_Analysis_Revenue_Insights_20250115\n```\n\n**Option 1: Save to Database** (Recommended)\n- Click **\"Save to Database\"** button\n- Config saved to `AI_FRAMEWORK_DB.CONFIG.DEMO_CONFIGURATIONS`\n\n**Option 2: Download YAML File**\n- Click **\"Download YAML Configuration\"** button\n- Downloads `.yaml` file for uploading to Snow Demo stage\n\n---\n\n### What This App Does Automatically\n\n**SQL Analysis**:\n- Detects all Cortex AI function calls\n- Identifies aggregation patterns (GROUP BY, SUM, AVG)\n- Recognizes table and database references\n- Comments out unsupported SQL commands\n\n**Visualization Suggestions**:\n- `GROUP BY` queries → Bar Chart visualizations\n- Simple SELECT queries → Table views\n- Cortex functions → Interactive experimentation panels\n\n**Interactive AI Steps**:\n- Extracts prompts from `CORTEX.COMPLETE()` calls\n- Creates editable prompt interfaces\n- Allows real-time model/parameter changes\n- Shows AI responses in demo flow\n\n**YAML Generation**:\n- Professional demo structure\n- Compatible with Snow Demo harness\n- Ready for presentations\n- No manual YAML writing needed\n\n---\n\n### Example SQL Patterns\n\n#### **Basic Analytics Query**\n```sql\n-- Shows as Table view\nSELECT customer_name, order_date, total_amount\nFROM my_table\nLIMIT 10;\n```\n\n#### **Aggregation Query**\n```sql\n-- Shows as Bar Chart\nSELECT product_category, SUM(revenue) as total_revenue\nFROM my_table\nGROUP BY product_category\nORDER BY total_revenue DESC;\n```\n\n#### **Interactive Cortex AI**\n```sql\n-- Shows as Interactive AI Panel\nSELECT \n    SNOWFLAKE.CORTEX.COMPLETE('mixtral-8x7b', \n        'Analyze this data: ' || column_name\n    ) as ai_insights\nFROM my_table;\n```\n\n---\n\n### Best Practices\n\n**Write clear SQL comments**: Use `-- Step X:` format for step detection  \n**Include Cortex AI**: Add CORTEX.COMPLETE() for interactive demos  \n**Mix query types**: Combine SELECT, GROUP BY, and AI functions  \n**Use descriptive metadata**: Clear titles and topics help viewers understand  \n**Test queries first**: Run SQL in worksheet before converting\n\n---\n\n### What's Next?\n\n**For Persona 2 (SQL Demo Creator)**:\n\n1. **Upload your YAML to Snowflake Stage** (see upload instructions in Snow Demo section below)\n2. **Continue to Page 9 (Snow Demo)** to run your interactive presentation\n\nYour SQL queries are now a professional, interactive demo ready for presentations!\n\n\u003C!-- ------------------------ --\u003E\n## Snow Demo\n\n**Purpose**: Run interactive SQL-driven presentations with live visualizations  \n**Dependencies**: Requires YAML configs from App 3 (uploaded to `FRAMEWORK_YAML_STAGE`)  \n**Output**: Live demo orchestration with charts and AI experimentation\n\n![Snow Demo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app4-snow-demo.gif)\n\n### Who Uses This App\n\n- **Persona 2** (SQL Demo Creator): Present interactive SQL demos with live AI experimentation\n\n---\n\n### Upload YAML to Stage\n\nBefore using Snow Demo, upload your YAML file to Snowflake:\n\n1. Navigate to **Data** → **Databases** → **AI_FRAMEWORK_DB** → **CONFIGS** → **Stages** → **FRAMEWORK_YAML_STAGE**\n2. Click **\"+ Files\"** button\n3. Select your downloaded YAML file\n4. In path field, enter: `/analytics/` (or choose: `sales_demo`, `customer_insights`, etc.)\n5. Click **\"Upload\"**\n\n---\n\n### Step-by-Step Instructions\n\n#### Step 1: Open the App\n\nNavigate to `Projects` → `Streamlit` → **`SNOW_DEMO`**\n\n#### Step 2: Select Area\n\n**Left Sidebar:** Select the project directory where you uploaded your YAML file (e.g., `analytics`)\n\n#### Step 3: Select Demo\n\n**Left Sidebar:** Select your YAML configuration file from the dropdown\n\n#### Step 4: Review and Run Demo\n\n**Left Sidebar:** Review the auto-displayed demo metadata, then click **[Run Demo]**\n\n#### Step 5: Navigate Demo Steps\n\n**Main Panel:** Each SQL step appears as a section with:\n- Auto-executed query results\n- Visualization selector (Table, Bar Chart, Line Chart, etc.)\n- Optional instructions and talk track\n\n**Tips:** Change **Display Options** dropdown to switch visualizations on-the-fly\n\n#### Step 6: Interactive Cortex AI (Optional)\n\nIf your SQL includes `SNOWFLAKE.CORTEX.COMPLETE()` calls, you'll see an interactive panel where you can:\n- Change the AI model (llama3.1-8b, mixtral-8x7b, etc.)\n- Adjust temperature and max tokens\n- Edit system and user prompts live\n- Re-run queries with different parameters\n\n\u003E \n\u003E **Live Audience Engagement**: Modify AI prompts in real-time during presentations!\n\n---\n\n### Best Practices\n\n**Prepare ahead**: Test demo flow before presentations  \n**Use talk tracks**: Add presenter notes in YAML for guidance  \n**Practice transitions**: Know when to switch visualizations  \n**Engage audience**: Ask for prompt suggestions during AI steps  \n**Keep queries fast**: Use LIMIT clause for demo data\n\n---\n\n### What's Next?\n\n**For Persona 2 (SQL Demo Creator)**:\n\nYour demo is complete! You can:\n- Run this demo in presentations\n- Create additional demos with different SQL queries\n- Edit YAML to add more steps or visualizations\n- Share demo with colleagues by sharing the YAML file\n\n**Return to Page 5** to explore other workflows or **continue to Page 12** for cleanup instructions.\n\n\u003C!-- ------------------------ --\u003E\n## YAML Wizard\n\n**Purpose**: Create dashboard configurations through guided interface  \n**Dependencies**: Requires tables from App 1 or 2  \n**Output**: YAML files for `VISUALIZATION_YAML_STAGE`\n\n![YAML Wizard Demo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app5-yaml-wizard.gif)\n\n### Who Uses This App\n\n- **Persona 1** (Full-Stack Developer): Create dashboard YAML from structured tables for Snow Viz\n\n---\n\n### Step-by-Step Instructions\n\n#### Step 1: Open the App\n\nNavigate to `Projects` → `Streamlit` → **`YAML_WIZARD`**\n\n#### Step 2: Select Data Source\n\n**Main Panel - Top Section:**\n\n```\n◉ Create new (selected by default)\n○ Load existing\n\nDatabase: AI_FRAMEWORK_DB ▼\nSchema: SILVER_LAYER ▼\nTable: TECHCORP_ORDERS_STRUCTURED ▼\n```\n\n**Schema Selection Guide**:\n- **SILVER_LAYER**: Use if you completed Structured Tables (recommended)\n- **BRONZE_LAYER**: Use if working with raw data directly\n\nSelect your structured table from the previous steps.\n\n\u003E \n\u003E **Success Check**: After selecting your table, you should see a preview showing your columns (CUSTOMER_NAME, ORDER_DATE, etc.) and sample data rows.\n\n#### Step 3: Configure Dimensions and Metrics\n\n**Configure Dimensions, Metrics, Time Column Section:**\n\n**Dimensions (Left Column):**\n\n```\nSelect text/categorical fields to group by:\n☑ CUSTOMER_NAME\n☑ PRODUCT_NAME\n☐ EMAIL\n☐ ...\n```\n\nCheck 2-5 key categorical fields you want to analyze.\n\n**Time Column (Right Column):**\n\n```\nTime Column for Trends:\nORDER_DATE ▼\n```\n\nSelect your date/timestamp field for time-series analysis.\n\n**Metrics (Below Columns):**\n\n```\nAuto-generated metrics from your table:\n☑ total_rows (COUNT(*))\n☑ avg_quantity (AVG(QUANTITY))\n☑ sum_total_amount (SUM(TOTAL_AMOUNT))\n☑ avg_price (AVG(PRICE))\n☐ ...\n```\n\nCheck 3-7 key metrics you want to calculate. The app automatically creates aggregation functions.\n\n**Tips**:\n- Don't check ALL metrics - pick the most important 5-10\n- Dimensions are for grouping (categories, names)\n- Metrics are for calculations (numbers, aggregations)\n\n#### Step 4: Customize Dimensions\n\n**Click the \"Dimensions\" tab**\n\nFor each dimension, you can customize:\n\n```\nCUSTOMER_NAME:\nLabel: Customer Name\nDescription: Customer who placed the order\nPriority: 0\nUnique Values: (auto-detected)\n\nPRODUCT_NAME:\nLabel: Product\nDescription: Product purchased\nPriority: 1\n```\n\n**What to customize**:\n- **Label**: User-friendly display name (e.g., \"Product Category\" instead of \"PRODUCT_NAME\")\n- **Description**: Help text for users\n- **Priority**: Display order (0 = first, 1 = second, etc.)\n\n**IMPORTANT**: After editing, click **\"Apply All Dimension Changes\"** button at the bottom!\n\n\u003E \n\u003E **Required Step**: You MUST click \"Apply All Dimension Changes\" or your edits won't be saved!\n\n#### Step 5: Customize Metrics\n\n**Click the \"Metrics\" tab**\n\nFor each metric, you can customize:\n\n```\ntotal_rows:\nLabel: Total Orders\nSQL: COUNT(*)\nFormat: number\nDecimals: 0\n\nsum_total_amount:\nLabel: Total Revenue\nSQL: SUM(TOTAL_AMOUNT)\nFormat: currency\nDecimals: 2\n\navg_price:\nLabel: Average Price\nSQL: AVG(PRICE)\nFormat: currency\nDecimals: 2\n```\n\n**What to customize**:\n- **Label**: User-friendly display name\n- **SQL**: The aggregation function (modify if needed)\n- **Format**: number, percent, currency, integer\n- **Decimals**: Decimal places to display\n\n**IMPORTANT**: After editing, click **\"Apply All Metric Changes\"** button at the bottom!\n\n\u003E \n\u003E **Required Step**: You MUST click \"Apply All Metric Changes\" or your edits won't be saved!\n\n#### Step 6: Generate Dashboard YAML\n\n**Click the \"Generate\" tab**, then enter:\n\n**App Name:**\n```\nAcme Corp Customer Orders Dashboard\n```\n\n**Description:**\n```\nComprehensive analysis of customer order data\n```\n\n**YAML Filename:**\n```\nacme_corp_orders_dashboard.yaml\n```\n\nClick **\"Generate Customized YAML\"** → Generates 8 tabs (Overview, Product/Category, VS, Top N, Self Service, Search, AI Assistant, Raw Data)\n\n#### Step 7: Download and Save\n\nClick **\"Download YAML\"** button\n\n**Optional:** Click **\"Save to AI_FRAMEWORK_DB.CONFIGS\"** to save your customizations for later editing\n\n---\n\n### Upload YAML to Stage for Snow Viz\n\nUpload your YAML file to Snowflake:\n\n1. Navigate to **Data** → **Databases** → **AI_FRAMEWORK_DB** → **CONFIGS** → **Stages** → **VISUALIZATION_YAML_STAGE**\n2. Click **\"+ Files\"** button\n3. Select your downloaded YAML file\n4. In path field, enter: `/customer_orders/` (or your project name)\n5. Click **\"Upload\"**\n\n---\n\n### Understanding the Output\n\n**What You Created**:\n- **YAML Configuration File**: Recipe for your dashboard\n- **8 Interactive Tabs**: Different ways to explore your data\n- **Customized Labels**: User-friendly names for dimensions and metrics\n- **Formatted Metrics**: Currency, percentages, decimals as configured\n\n**Why Two Saves?**:\n- **Download YAML**: For uploading to stage (Snow Viz needs this)\n- **Save to CONFIGS**: For editing later (preserves your customizations)\n\n---\n\n### What to Ignore (Normal Messages)\n\n**These messages are NORMAL for first-time use:**\n\n```\nNo Cortex Search services found in this database/schema\nCreate a Cortex Search service first to enable semantic search\n```\n**Ignore this** - Search services are advanced/optional\n\n```\nTable exists but no configurations found\nNo configs saved yet.\nConfiguration table has 0 saved configs\n```\n**Ignore this** - Normal until you save your first config\n\n---\n\n### Best Practices\n\n**Start simple**: Pick 2-3 dimensions and 3-5 metrics for first try  \n**Use clear labels**: \"Product Category\" is better than \"PRODUCT_CATEGORY\"  \n**Format metrics**: Use currency for money, percent for rates  \n**Save your work**: Both download AND save to database  \n**Test in Snow Viz**: Verify dashboard works as expected\n\n---\n\n### What's Next?\n\n**For Persona 1 (Full-Stack Developer)**:\n\nYou now have a dashboard configuration file! Next steps:\n\n1. **Upload YAML to stage** (instructions above)\n2. **Continue to Page 11 (Snow Viz)** to view your interactive dashboard\n\nYour data is now ready for visual analytics with 8 interactive dashboard tabs!\n\n\u003C!-- ------------------------ --\u003E\n## Snow Viz\n\n**Purpose**: Render advanced interactive dashboards from YAML configurations  \n**Dependencies**: Requires YAML configs from App 5 (uploaded to `VISUALIZATION_YAML_STAGE`)  \n**Output**: Multi-tab analytics dashboards with AI integration\n\n![Snow Viz Demo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-ai-demo-framework/app6-snow-viz.gif)\n\n### Who Uses This App\n\n- **Persona 1** (Full-Stack Developer): View and validate dashboards after creation\n- **Persona 4** (Dashboard Consumer): Explore interactive dashboards and run natural language queries\n\n---\n\n### Step-by-Step Instructions\n\n#### Step 1: Open the App\n\nNavigate to `Projects` → `Streamlit` → **`SNOW_VIZ`**\n\n#### Step 2: Select Configuration Source\n\n**Left Sidebar - Configuration Source:**\n\n```\nLoad from:\n◉ Stage\n○ Local file\n```\n\nSelect **Stage** (recommended - loads from `VISUALIZATION_YAML_STAGE`)\n\n#### Step 3: Select Project and YAML File\n\n**Left Sidebar - After selecting Stage:**\n\n```\nProject: [Select Project] ▼\nAvailable: techcorp_orders, analytics, sales_dashboard\n\nYAML File: [Select YAML] ▼\nAvailable: techcorp_orders_dashboard.yaml\n```\n\n1. **Select Project**: Choose the directory where you uploaded your YAML\n2. **Select YAML File**: Choose your dashboard configuration file\n\nThe dashboard will automatically load.\n\n#### Step 4: Navigate Dashboard Tabs\n\n**Left Sidebar - Navigation Section:**\n\n```\nSelect Page:\n◉ Overview\n○ Product / Category\n○ VS (Compare)\n○ Top N\n○ Self Service\n○ Search\n○ AI Assistant\n○ Raw Data\n```\n\nEach tab provides different analytical views of your data.\n\n---\n\n### Tab 1: Overview Dashboard\n\n**Main Panel - Overview Tab:**\n\n**Time Controls (Top):**\n```\nTime Window: last_3_months ▼\nOptions: last_7_days, last_30_days, last_3_months, last_6_months, last_year, all_time\n\nTime Grain: month ▼\nOptions: day, week, month, quarter, year\n```\n\n**Key Metrics Cards:**\n```\n[Total Orders]        [Total Revenue]       [Average Price]\n1,234                $156,789              $127.15\n↑ 12% vs prev        ↑ 8% vs prev         ↓ 3% vs prev\n```\n\nMetric cards are interactive - click to select which metric to visualize below.\n\n**Visualizations:**\n\n**Left Side: Time Series Chart**\n```\nShows trend line for selected metric over time\n- X-axis: Time periods (based on Time Grain)\n- Y-axis: Metric values\n- Hover for exact values\n```\n\n**Right Side: Ranked Grid**\n```\nDimension: [Select Dimension] ▼\nShows top 10 results in table format:\n| Product Name    | Total Revenue | % of Total |\n|----------------|---------------|------------|\n| Laptop Pro     | $45,678      | 29%        |\n| Wireless Mouse | $23,456      | 15%        |\n```\n\n---\n\n### Tab 2: Product / Category (Drill-Down)\n\n**Dimension Analysis:**\n\n```\nSelect Dimension: PRODUCT_NAME ▼\nOptions: All configured dimensions\n\nSelect Metric: Total Revenue ▼\nOptions: All configured metrics\n```\n\nShows detailed breakdown by selected dimension with:\n- Bar chart visualization\n- Data table with all values\n- Filtering and sorting capabilities\n\n---\n\n### Tab 3: VS (Compare Entities)\n\n**Side-by-Side Comparison:**\n\n```\nLeft Entity: [Select] ▼\nRight Entity: [Select] ▼\n\nMetrics to Compare:\n☑ Total Orders\n☑ Total Revenue\n☑ Average Price\n☐ ...\n```\n\n**Comparison Table:**\n```\n| Metric        | Laptop Pro | Wireless Mouse | Winner        | Delta    |\n|--------------|------------|----------------|---------------|----------|\n| Total Orders | 456        | 789            | Wireless Mouse| +73%     |\n| Total Revenue| $45,678    | $23,456        | Laptop Pro    | +95%     |\n```\n\nPerfect for comparing products, customers, or any dimension values.\n\n---\n\n### Tab 4: Top N (Rankings)\n\n**Leaderboard Analysis:**\n\n```\nSelect Dimension: PRODUCT_NAME ▼\nSelect Metric: Total Revenue ▼\nTop N: 10 ▼\nOptions: 5, 10, 20, 50, 100\n\nSort Order:\n◉ Descending (highest first)\n○ Ascending (lowest first)\n```\n\nShows ranked list with:\n- Bar chart visualization\n- Numeric rankings\n- Percentage of total\n\n---\n\n### Tab 5: Self Service\n\n**Custom Analysis:**\n\n```\nSelect Dimensions (grouping):\n☑ PRODUCT_NAME\n☑ CUSTOMER_NAME\n☐ ...\n\nSelect Metrics (calculations):\n☑ Total Revenue\n☑ Average Price\n☐ ...\n\nTime Range: last_3_months ▼\n```\n\nBuild custom reports by selecting any combination of dimensions and metrics.\n\nResults show in interactive data table with:\n- Sorting by any column\n- Filtering capabilities\n- Export to CSV option\n\n---\n\n### Tab 6: AI Assistant (Natural Language Queries)\n\n**Powered by Cortex Analyst:**\n\nType your question in natural language:\n\n```\nWhat are the top 3 products by revenue in the last quarter?\n```\n\nClick **[Ask Analyst]** → Select view option (Grid, Bar, or Line chart)\n\n**AI Narrative (Optional):** Generate AI analysis by selecting a model, adjusting temperature, and clicking **[Generate Analysis]**\n\n**Example Questions**:\n- \"What is the average revenue per customer?\"\n- \"Show me products with revenue greater than $10,000\"\n- \"Which month had the highest number of orders?\"\n- \"Compare revenue between Electronics and Software categories\"\n\n---\n\n### Tab 7: Search (Cortex Search)\n\n**Semantic Search (if configured):**\n\nEnter your search query:\n\n```\nlaptop with high ratings\n```\n\nClick **[Search]** → Shows relevant records based on semantic similarity\n\n**Note**: Requires Cortex Search service to be configured. If not set up, this tab will show a setup message.\n\n---\n\n### Tab 8: Raw Data\n\nShows complete dataset in table format with sortable columns and CSV export option.\n\n**Use this tab to:**\n- Verify data quality\n- Export raw data\n- See all available fields\n\n---\n\n### Dashboard Features\n\n**Interactive Elements**:\n- Click metric cards to change visualizations\n- Hover over charts for detailed values\n- Sort tables by any column\n- Filter and drill down into data\n- Export results to CSV\n\n**Time Controls**:\n- Adjust time windows dynamically\n- Change time grain (day/week/month)\n- See period-over-period comparisons\n- View trends over time\n\n**AI Integration**:\n- Natural language queries (Cortex Analyst)\n- AI-generated narratives (Cortex Complete)\n- Semantic search (Cortex Search, if configured)\n\n---\n\n### Use Cases\n\n#### **For Persona 1 (Full-Stack Developer)**\n- Validate dashboard configuration\n- Test all tabs and features\n- Verify metrics calculate correctly\n- Share with business users\n\n#### **For Persona 4 (Dashboard Consumer)**\n- Explore business metrics\n- Ask questions in plain English\n- Compare entities side-by-side\n- Export data for presentations\n\n---\n\n### Best Practices\n\n**Explore systematically**: Start with Overview, then drill into specific tabs  \n**Use AI Assistant**: Natural language queries are powerful and intuitive  \n**Compare entities**: VS tab helps identify top performers  \n**Export insights**: Share findings via CSV export  \n**Adjust time windows**: Find the right time range for your analysis\n\n---\n\n### What's Next?\n\n**For Persona 1 (Full-Stack Developer)**:\n\nYour complete analytics pipeline is built! 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