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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}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"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-de4fac4f65","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroTitle":{"lines":["Build an End-to-End Application Using CoCo on Snowflake"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Dash 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complete AI-powered retail analytics platform entirely within Snowflake — no external infrastructure required. Using Snowflake CoCo as your AI-assisted development environment, you'll work through the full data lifecycle: stream real-time orders via Snowpipe Streaming, MERGE them into production tables with Gen2 Warehouses, transform them through a 3-tier Dynamic Tables pipeline, and serve them with Interactive Tables for low-latency point lookups.\u003C/p\u003E\n","\u003Cp\u003EYou'll build analytical models with dbt, monitor data quality with Data Metric Functions, and create custom CoCo skills for reusable workflows. Tie it all together with Snowflake CoWork — a conversational AI interface where a Cortex Agent orchestrates Cortex Analyst and Agentic Search to answer "what happened" and "why" from both structured and unstructured data. Finally, evaluate your agent with ground-truth datasets, implement row-level security, and expose your agent as a managed MCP server for external AI clients.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccelerate development with Snowflake CoCo (AI-assisted SQL, deployment, and data exploration)\u003C/li\u003E\u003Cli\u003EStream real-time data with Snowpipe Streaming and transform with Dynamic Tables\u003C/li\u003E\u003Cli\u003EServe low-latency queries with Interactive Tables and Gen2 Warehouses\u003C/li\u003E\u003Cli\u003EBuild analytical models with dbt\u003C/li\u003E\u003Cli\u003EMonitor data quality automatically with Data Metric Functions\u003C/li\u003E\u003Cli\u003ECreate and query managed Iceberg V3 tables (deletion vectors, row lineage) \u003Cem\u003E(optional)\u003C/em\u003E\u003C/li\u003E\u003Cli\u003ECreate custom CoCo skills for reusable team workflows\u003C/li\u003E\u003Cli\u003EBuild a Cortex Agent with Cortex Analyst (semantic view + verified queries) and Agentic Search (multi-index Cortex Search)\u003C/li\u003E\u003Cli\u003EEvaluate agent quality with ground-truth datasets and LLM judges\u003C/li\u003E\u003Cli\u003EExpose agents as managed MCP servers for external AI clients\u003C/li\u003E\u003Cli\u003EImplement transparent row-level security with Row Access Policies\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA production-grade AI-powered retail analytics platform on Snowflake — from raw data to conversational AI insights, entirely within a single platform. You'll create dynamic transformation pipelines, interactive low-latency tables, dbt analytical models, a Cortex Agent that answers questions across structured and unstructured data, row-level security that works transparently through AI, and an MCP server that exposes your agent to external clients.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EPython 3.8+ installed locally\u003C/li\u003E\u003Cli\u003EGit installed locally\u003C/li\u003E\u003Cli\u003EBasic familiarity with SQL and command-line tools\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Ch3\u003EInstall Snowflake CLI\u003C/h3\u003E\n","\u003Cp\u003EThe Snowflake CLI (\u003Ccode\u003Esnow\u003C/code\u003E) lets you run SQL, deploy apps, and manage Snowflake objects from your terminal.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EmacOS (using Homebrew):\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EIf you don't have Homebrew installed, first install it by opening Terminal and running:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen install Snowflake CLI:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ebrew install snowflake-cli\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWindows:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-powershell\"\u003Epip install snowflake-cli\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELinux:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install snowflake-cli\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify the installation:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow --version\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see output like \u003Ccode\u003ESnowflake CLI version: 3.x.x\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EInstall Snowflake CoCo\u003C/h3\u003E\n","\u003Cp\u003ESnowflake CoCo is an AI-powered coding assistant that runs in your terminal. It helps you write SQL, build pipelines, deploy apps, and explore your data using natural language prompts.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EmacOS (using Homebrew):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ebrew install cortex\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWindows / Linux:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install cortex\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify the installation:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex --version\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAlternative: Snowflake CoCo Desktop (Preview)\u003C/h3\u003E\n","\u003Cp\u003EIf you prefer a visual IDE experience, \u003Ca href=\"https://www.snowflake.com/en/product/limited-access/cortex-code/\"\u003Edownload CoCo Desktop\u003C/a\u003E instead of (or alongside) the CLI. It's a native Mac/Windows app with a file editor, integrated terminal, agentic browser, and the same AI capabilities.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E CoCo Desktop is currently a \u003Ca href=\"https://docs.snowflake.com/release-notes/preview-features\"\u003EPreview Feature\u003C/a\u003E available to all accounts.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EOn first launch, follow the onboarding wizard:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E on the Welcome screen\u003C/li\u003E\u003Cli\u003EAdd your connection (same account identifier and credentials from above) or select an existing one detected from \u003Ccode\u003Econnections.toml\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EChoose \u003Cstrong\u003EAgent\u003C/strong\u003E mode\u003C/li\u003E\u003Cli\u003EPick your theme, then click \u003Cstrong\u003EGet Started\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E If you already configured a connection with \u003Ccode\u003Esnow connection add\u003C/code\u003E (below), CoCo Desktop detects it automatically — just select it from the list.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ESee \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-desktop/index\"\u003ECoCo Desktop documentation\u003C/a\u003E for full details.\u003C/p\u003E\n","\u003Ch3\u003EConfigure Snowflake Connection\u003C/h3\u003E\n","\u003Cp\u003EBefore you can run any commands against Snowflake, you need to configure a connection. This tells the CLI which Snowflake account to connect to and how to authenticate.\u003C/p\u003E\n","\u003Cp\u003ERun the interactive connection wizard:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow connection add\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou'll be prompted for the following values (enter them one at a time):\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPrompt\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat to enter\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConnection name\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA short name for this connection\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ehol\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAccount identifier\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYour Snowflake account URL (without \u003Ccode\u003E.snowflakecomputing.com\u003C/code\u003E)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Emyorg-myaccount\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUser\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYour Snowflake username\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ejsmith\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPassword\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYour Snowflake password\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cem\u003E(hidden)\u003C/em\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERole\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWarehouse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELeave blank (will be created by setup)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cem\u003E(leave empty)\u003C/em\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDatabase\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELeave blank (will be created by setup)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cem\u003E(leave empty)\u003C/em\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E Your account identifier is the part before \u003Ccode\u003E.snowflakecomputing.com\u003C/code\u003E in your Snowflake URL. For example, if you log in at \u003Ccode\u003Ehttps://myorg-myaccount.snowflakecomputing.com\u003C/code\u003E, your account identifier is \u003Ccode\u003Emyorg-myaccount\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ETest that your connection works:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow connection test -c hol\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see \u003Ccode\u003EStatus: OK\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EClone the Lab Repository\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/Snowflake-Labs/automated-intelligence-dev-day-2026-hol.git\ncd automated-intelligence-dev-day-2026-hol\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun Infrastructure Setup\u003C/h3\u003E\n","\u003Cp\u003ELaunch Snowflake CoCo and verify your connection:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to expect:\u003C/strong\u003E CoCo will start an interactive session in your terminal. You'll see your active connection, role, and warehouse displayed. You can type natural language prompts and CoCo will translate them into SQL or actions.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThen run the core infrastructure script (this takes ~10-15 minutes):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -f setup.sql -c hol\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis creates the database, schemas, warehouses, tables, Dynamic Tables pipeline, Interactive Tables, Cortex Search Services, Semantic View, seed data (10M orders, 25M order items, 2M customers), and Row Access Policy.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing CoCo Desktop?\u003C/strong\u003E All "Prompt CoCo" instructions below work identically in both CLI and Desktop — type the same text into the chat input. Anywhere you see \u003Ccode\u003Ecortex\u003C/code\u003E as a terminal command, use the Desktop's built-in terminal or chat instead.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExplore Your Data\u003C/h2\u003E\n","\u003Cp\u003ENow that setup is complete, let's get familiar with what was created. This is your first interaction with CoCo — try asking natural language questions about your data.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt CoCo:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"What schemas and tables are in my database?"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECoCo queries \u003Ccode\u003EINFORMATION_SCHEMA\u003C/code\u003E and shows the database structure: RAW (source tables + views), STAGING (streaming landing zone), DYNAMIC_TABLES (pipeline), INTERACTIVE (low-latency), and SEMANTIC (AI layer).\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Show me 5 sample rows from the orders table"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"How many orders, customers, and products do we have?"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EExpected: ~10M orders, ~25M order items, 2M customers, 10 products, 1200 reviews, 1200 support tickets.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"What's the date range of our order data?"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EExpected: June 2025 to September 2025.\u003C/p\u003E\n","\u003Cp\u003EThis gives you a mental model of the dataset before we start transforming and analyzing it.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EData Quality\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-intro\"\u003EData Metric Functions (DMFs)\u003C/a\u003E let you attach automated quality checks directly to table columns. Snowflake runs them on a schedule and stores results in \u003Ccode\u003ESNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_RESULTS\u003C/code\u003E. Built-in DMFs include \u003Ccode\u003ENULL_COUNT\u003C/code\u003E, \u003Ccode\u003EDUPLICATE_COUNT\u003C/code\u003E, \u003Ccode\u003EUNIQUE_COUNT\u003C/code\u003E, and \u003Ccode\u003EFRESHNESS\u003C/code\u003E — or you can write custom ones.\u003C/p\u003E\n","\u003Cp\u003EThe setup script injected ~200 NULL values into \u003Ccode\u003Eorders.total_amount\u003C/code\u003E and \u003Ccode\u003Eorder_items.quantity\u003C/code\u003E, plus ~150 NULLs into \u003Ccode\u003Eorder_items.product_name\u003C/code\u003E. DMFs detect the first two — but there's a gap.\u003C/p\u003E\n","\u003Ch3\u003EDiscover the Gap\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt CoCo:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Check the data quality monitoring results and show me which columns have NULL violations"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECoCo shows that \u003Ccode\u003ETOTAL_AMOUNT\u003C/code\u003E (200 NULLs) and \u003Ccode\u003EQUANTITY\u003C/code\u003E (200 NULLs) have violations — but \u003Ccode\u003Eproduct_name\u003C/code\u003E NULLs are going undetected.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Are there any NULL values in order_items.product_name? Is that column being monitored?"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECoCo finds ~150 NULLs and reveals the DMF is mis-attached to \u003Ccode\u003Eproduct_category\u003C/code\u003E instead of \u003Ccode\u003Eproduct_name\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EFix the Coverage\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Fix the DMF — remove the NULL check from product_category and add it to product_name instead"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE order_items DROP DATA METRIC FUNCTION SNOWFLAKE.CORE.NULL_COUNT ON (product_category);\nALTER TABLE order_items ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.NULL_COUNT ON (product_name);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis demonstrates the real-world workflow: monitor, discover gaps, fix coverage.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDynamic Tables Pipeline\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-about\"\u003EDynamic Tables\u003C/a\u003E are declarative data pipelines — you define the target state as a SQL query and Snowflake handles incremental refresh automatically. The \u003Ccode\u003ETARGET_LAG\u003C/code\u003E parameter controls freshness: set a time-based lag (e.g., \u003Ccode\u003E1 minute\u003C/code\u003E) for Tier 1 tables, or use \u003Ccode\u003EDOWNSTREAM\u003C/code\u003E for tables that refresh only when their upstream sources change.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt CoCo:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Show me the Dynamic Tables pipeline status — names, target lag, last refresh time, and row counts for each tier"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECoCo displays the 3-tier pipeline:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETier 1\u003C/strong\u003E (1-min lag): \u003Ccode\u003Eenriched_orders\u003C/code\u003E (10M rows), \u003Ccode\u003Eenriched_order_items\u003C/code\u003E (25M rows)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETier 2\u003C/strong\u003E (DOWNSTREAM): \u003Ccode\u003Efact_orders\u003C/code\u003E (25M rows)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETier 3\u003C/strong\u003E (DOWNSTREAM): \u003Ccode\u003Edaily_business_metrics\u003C/code\u003E (118 rows), \u003Ccode\u003Eproduct_performance_metrics\u003C/code\u003E (4 rows)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Row counts shown are for the default \u003Ccode\u003Edata_scale = '10M'\u003C/code\u003E. If you chose \u003Ccode\u003E'50M'\u003C/code\u003E in \u003Ccode\u003Esetup.sql\u003C/code\u003E, expect ~50M enriched_orders, ~161M order_items, and 365 daily metrics rows.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EExplore Results\u003C/h3\u003E\n","\u003Cp\u003EAsk CoCo:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Show me a sample of the daily business metrics — top 5 days by revenue"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EExpected: Top-5 days are in September 2025 (back-to-season peak), each with ~$183M revenue and ~117K orders.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt Analytics\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-engineering/dbt-projects-on-snowflake\"\u003Edbt-snowflake\u003C/a\u003E transforms raw data into analytics-ready models using SQL SELECT statements. The dbt-snowflake adapter integrates natively with Snowflake, supporting incremental models, snapshots, and built-in testing. In this HOL, dbt creates staging views and mart tables for customer lifetime value, segmentation, product performance, and support analytics.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrerequisites:\u003C/strong\u003E \u003Ccode\u003Edbt-core\u003C/code\u003E and \u003Ccode\u003Edbt-snowflake\u003C/code\u003E must be installed (\u003Ccode\u003Epip install dbt-snowflake\u003C/code\u003E). CoCo will handle this for you if not already installed.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt CoCo:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Install dbt dependencies and build all models in the dbt-analytics project"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECoCo runs \u003Ccode\u003Edbt deps\u003C/code\u003E then \u003Ccode\u003Edbt build\u003C/code\u003E to create all staging views and mart tables (9+ models). CoCo automatically injects your active Snowflake connection into the dbt profile — no manual configuration needed.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected output:\u003C/strong\u003E 71 tests pass, 1 warning (the \u003Ccode\u003Esource_not_null_raw_orders_total_amount\u003C/code\u003E test detects the 200 NULLs we injected for the Data Quality exercise — this is working as designed).\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EExplore Results\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Show me the customer lifetime value segments — how many customers are in each value tier?"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGen2 Warehouse: Optima Indexing\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/warehouses-gen2\"\u003EGen2 Warehouses\u003C/a\u003E introduce Optima Indexing — an automatic indexing layer that prunes partitions at query time without explicit clustering keys. Gen2 warehouses learn from your query patterns and build internal indices that accelerate point lookups and filtered scans.\u003C/p\u003E\n","\u003Cp\u003EDemonstrate Optima Indexing in action:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt CoCo:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Run a point lookup for customer_id 5000 on the Gen2 warehouse"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EOpen the query profile in Snowsight to see partition pruning — only a fraction of partitions scanned despite no explicit clustering key. This is Gen2's Optima Indexing in action.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInteractive Tables\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/interactive\"\u003EInteractive Tables\u003C/a\u003E are purpose-built for low-latency, high-concurrency point lookups. They maintain pre-computed results with clustering optimized for equality predicates, delivering sub-second response times for dashboard filters and application queries that would otherwise require full table scans.\u003C/p\u003E\n","\u003Ch3\u003EPoint Lookups\u003C/h3\u003E\n","\u003Cp\u003ERun queries in Snowsight to observe sub-second latency:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE WAREHOUSE hol_interactive_wh;\nALTER SESSION SET USE_CACHED_RESULT = FALSE;\n\n-- Point lookup by customer ID\nSELECT * FROM dash_automated_intelligence_db.interactive.customer_order_analytics\nWHERE customer_id = 1;\n\n-- Point lookup by order ID\n-- Run: SELECT order_id FROM dash_automated_intelligence_db.interactive.order_lookup LIMIT 5; to get UUIDs to use\nSELECT * FROM dash_automated_intelligence_db.interactive.order_lookup\nWHERE order_id = '<any-order-uuid>';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConcurrency Load Test\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt CoCo:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Run the interactive tables load test at interactive/load_test.py"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThis fires 200 concurrent sessions (1000 queries total) against both Interactive and Standard warehouses, then compares P50/P90/P99 latencies. You should see notably lower latency and higher throughput on the Interactive warehouse.\u003C/p\u003E\n","\u003Cp\u003ERun the load test a second time to observe the effect of warm caches. Results may vary depending on account, region, and data scale.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECoCo Custom Skill\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/extensibility#skills\"\u003ECoCo Custom Skills\u003C/a\u003E let you package repeatable workflows into named commands that any team member can invoke. A skill is a Markdown file (\u003Ccode\u003E.cortex/skills/<name>/SKILL.md\u003C/code\u003E) that defines triggers, parameters, and step-by-step instructions CoCo follows when the skill is activated.\u003C/p\u003E\n","\u003Cp\u003ECreate a reusable skill that automates table profiling:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt CoCo:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Create a custom CoCo skill called 'profile-table' that takes a table name, counts rows, checks for NULL columns, shows distinct value counts, and flags potential data quality issues"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECoCo creates \u003Ccode\u003E.cortex/skills/profile-table/SKILL.md\u003C/code\u003E with the skill definition, triggers, and step-by-step instructions.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E After creating the skill, restart your CoCo session for the new skill to become active. \u003Cstrong\u003ECLI:\u003C/strong\u003E type \u003Ccode\u003E/quit\u003C/code\u003E then \u003Ccode\u003Ecortex\u003C/code\u003E. \u003Cstrong\u003EDesktop:\u003C/strong\u003E open a new session from the sidebar.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ETest It\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"$profile-table DASH_AUTOMATED_INTELLIGENCE_DB.RAW.ORDERS"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThis demonstrates how teams package repeatable workflows as shareable CoCo skills.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake CoWork\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents\u003C/a\u003E are multi-tool AI orchestrators that route questions to the right data source. They combine \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E (text-to-SQL over \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view\"\u003ESemantic Views\u003C/a\u003E) with \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search\u003C/a\u003E (vector + keyword search over unstructured data) to answer both "what happened" and "why" from a single conversational interface.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Agent\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt CoCo:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Run snowflake-cowork/create_agent.sql to create the Business Insights Agent"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ETest Agent Routing\u003C/h3\u003E\n","\u003Cp\u003EOpen the \u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E interface in Snowsight: navigate to \u003Cstrong\u003EAI & ML → CoWork\u003C/strong\u003E (or search "CoWork" in the global search bar). Select the \u003Ccode\u003EBUSINESS_INSIGHTS_AGENT\u003C/code\u003E agent. Then try each question to demonstrate different tool routing:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuestion\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETools Used\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Show me monthly revenue trend from June to September 2025"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Analyst (text-to-SQL)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Which month had the lowest revenue, and what do customer reviews say about that period?"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Analyst + Agentic Search\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Find reviews mentioning wrong size with a rating below 3"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAgentic Search (filtered)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Why are customers returning ski boots?"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAgentic Search (reviews + tickets)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What is our total revenue and customer count by state?"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Analyst (text-to-SQL)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What are the top complaint themes in support tickets?"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAgentic Search (filter + AI_AGG)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"How many reviews mention sizing issues, and which products are most affected?"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAgentic Search (search + breakdown)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThis is the capstone moment — the agent routes across structured data (text-to-SQL) and unstructured data (Cortex Search) to answer "what happened" and "why."\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESecurity and Governance\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-row-intro\"\u003ERow Access Policies\u003C/a\u003E enforce row-level security declaratively — you define a boolean expression that determines which rows are visible to which roles, and Snowflake applies it transparently to every query (including those generated by AI agents). No application code changes required.\u003C/p\u003E\n","\u003Cp\u003EThe Row Access Policy and WEST_COAST_MANAGER role were created by \u003Ccode\u003Esetup.sql\u003C/code\u003E. Demonstrate how row-level security transparently filters data based on role:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAs ACCOUNTADMIN (full access):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nUSE WAREHOUSE HOL_WH;\nSELECT c.state, SUM(o.total_amount) AS total_revenue, COUNT(DISTINCT c.customer_id) AS customer_count\nFROM dash_automated_intelligence_db.raw.orders o\nJOIN dash_automated_intelligence_db.raw.customers c ON o.customer_id = c.customer_id\nGROUP BY c.state ORDER BY total_revenue DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EResult: all 10 states visible.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAs WEST_COAST_MANAGER (restricted):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE WEST_COAST_MANAGER;\nUSE WAREHOUSE HOL_WH;\nSELECT c.state, SUM(o.total_amount) AS total_revenue, COUNT(DISTINCT c.customer_id) AS customer_count\nFROM dash_automated_intelligence_db.raw.orders o\nJOIN dash_automated_intelligence_db.raw.customers c ON o.customer_id = c.customer_id\nGROUP BY c.state ORDER BY total_revenue DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EResult: only CA, OR, WA appear — the Row Access Policy transparently filters data.\u003C/p\u003E\n","\u003Cp\u003EKey insight: Same query, same tables — different results based on who's asking. Row-level security enforces data boundaries without changing application logic.\u003C/p\u003E\n","\u003Ch3\u003EVerify Through CoWork\u003C/h3\u003E\n","\u003Cp\u003ESwitch to the \u003Cstrong\u003EWEST_COAST_MANAGER\u003C/strong\u003E role in Snowsight, then open CoWork and ask the agent:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"What is our total revenue and customer count by state?"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe agent returns results for only CA, OR, and WA — the Row Access Policy filters data transparently, even through AI-generated SQL.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStreamlit Dashboard\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E lets you build and deploy interactive data applications directly within your Snowflake account — no external infrastructure needed. Apps run securely inside Snowflake, with native access to your data and governed by the same role-based access control.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrerequisites:\u003C/strong\u003E The dbt models must be built first (see the \u003Cstrong\u003Edbt Analytics\u003C/strong\u003E section). The dashboard queries \u003Ccode\u003EDBT_ANALYTICS\u003C/code\u003E and \u003Ccode\u003EDBT_STAGING\u003C/code\u003E tables.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt CoCo:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Deploy the Streamlit dashboard to Snowflake"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECoCo runs \u003Ccode\u003Esnow streamlit deploy\u003C/code\u003E from the \u003Ccode\u003Estreamlit-dashboard/\u003C/code\u003E directory. Once deployed, open the app URL in Snowsight to explore:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESummary\u003C/strong\u003E — Revenue KPIs, order trends, customer counts\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustomer & Product Analytics\u003C/strong\u003E — Lifetime value segments, product performance\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPipeline Health\u003C/strong\u003E — Dynamic Tables refresh status, data freshness monitoring\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAgent Evaluation\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#evaluating-agents\"\u003EAgent Evaluation\u003C/a\u003E lets you measure agent quality using ground-truth datasets and LLM-as-judge scoring. You define input queries and expected answers, then Snowflake runs the agent against each question and scores responses on Answer Correctness (does the response match ground truth?) and Logical Consistency (are the reasoning steps internally coherent?).\u003C/p\u003E\n","\u003Cp\u003EThe evaluation dataset (7 questions + ground truth) was created by \u003Ccode\u003Esetup.sql\u003C/code\u003E. Run the evaluation in Snowsight:\u003C/p\u003E\n","\u003Ch3\u003ERun via Snowsight UI\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ESwitch to the \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E role in Snowsight (top-left role selector)\u003C/li\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAI and ML > Agents > BUSINESS_INSIGHTS_AGENT > Evaluations\u003C/strong\u003E tab\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ENew evaluation run\u003C/strong\u003E, name it (e.g. \u003Ccode\u003Ehol-eval-run-1\u003C/code\u003E), click \u003Cstrong\u003ENext\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ECreate new dataset from table\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EUnder \u003Cstrong\u003ESource table\u003C/strong\u003E, set Database and schema to \u003Ccode\u003EDASH_AUTOMATED_INTELLIGENCE_DB.SEMANTIC\u003C/code\u003E, then select \u003Ccode\u003EAGENT_EVALUATION_DATA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EUnder \u003Cstrong\u003ENew dataset location\u003C/strong\u003E, keep \u003Ccode\u003EDASH_AUTOMATED_INTELLIGENCE_DB.SEMANTIC\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESet \u003Cstrong\u003EDataset name\u003C/strong\u003E: \u003Ccode\u003Ehol_eval_dataset\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EUnder \u003Cstrong\u003EDefine metrics\u003C/strong\u003E, confirm \u003Cstrong\u003EInput query\u003C/strong\u003E = \u003Ccode\u003EINPUT_QUERY\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EToggle on \u003Cstrong\u003EAnswer Correctness\u003C/strong\u003E, set \u003Cstrong\u003EExpected answer\u003C/strong\u003E = \u003Ccode\u003EGROUND_TRUTH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EToggle on \u003Cstrong\u003ELogical Consistency\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate\u003C/strong\u003E — evaluation starts automatically (~3 min)\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EInterpret Results\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAnswer Correctness\u003C/strong\u003E — Did the agent's response match ground truth? Scored 0-1 per question.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELogical Consistency\u003C/strong\u003E — Were planning steps, tool calls, and response internally consistent? (Reference-free.)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPer-question drill-down\u003C/strong\u003E — Select any row to see the full thread: planning, tool invocations, response generation.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EImprove Scores (Stretch)\u003C/h3\u003E\n","\u003Cp\u003EIf questions score low on logical consistency:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick a low-scoring row and view Thread details\u003C/li\u003E\u003Cli\u003ELook for vague reasoning about tool selection in the Planning step\u003C/li\u003E\u003Cli\u003EUpdate the agent's instructions to be more explicit\u003C/li\u003E\u003Cli\u003ERecreate the agent and re-run the evaluation\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMCP Server\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#managed-mcp-servers\"\u003ESnowflake MCP Servers\u003C/a\u003E expose your Cortex Agents, Semantic Views, and Search Services as tools discoverable via the open Model Context Protocol (MCP). Any MCP-compatible client (CoCo CLI, Claude Desktop, custom apps) can connect to the server endpoint and invoke tools programmatically — turning your Snowflake AI stack into a reusable service layer.\u003C/p\u003E\n","\u003Cp\u003EExpose the Business Insights Agent as a managed MCP server:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt CoCo:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Create a Snowflake-managed MCP server that exposes our Business Insights Agent, semantic view, and customer feedback search as tools"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECoCo creates the MCP server:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E CoCo generates tool names (like \u003Ccode\u003Erevenue-analytics\u003C/code\u003E, \u003Ccode\u003Ecustomer-feedback-search\u003C/code\u003E) based on your prompt. Your names may differ — what matters is that the \u003Ccode\u003Etype\u003C/code\u003E and \u003Ccode\u003Eidentifier\u003C/code\u003E point to the correct objects.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE MCP SERVER business_insights_mcp\n  FROM SPECIFICATION $$\n    tools:\n      - name: "business-insights-agent"\n        type: "CORTEX_AGENT_RUN"\n        identifier: "DASH_AUTOMATED_INTELLIGENCE_DB.SEMANTIC.BUSINESS_INSIGHTS_AGENT"\n        description: "AI agent that answers business questions using structured data and customer feedback"\n        title: "Business Insights Agent"\n\n      - name: "revenue-analytics"\n        type: "CORTEX_ANALYST_MESSAGE"\n        identifier: "DASH_AUTOMATED_INTELLIGENCE_DB.SEMANTIC.BUSINESS_ANALYTICS_SEMANTIC"\n        description: "Text-to-SQL for revenue, orders, customers, and product metrics"\n        title: "Revenue Analytics"\n\n      - name: "customer-feedback-search"\n        type: "CORTEX_SEARCH_SERVICE_QUERY"\n        identifier: "DASH_AUTOMATED_INTELLIGENCE_DB.RAW.CUSTOMER_FEEDBACK_SEARCH"\n        description: "Search across product reviews and support tickets"\n        title: "Customer Feedback Search"\n  $$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConnect from CoCo\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex mcp add business-insights https://<account_url>/api/v2/databases/DASH_AUTOMATED_INTELLIGENCE_DB/schemas/SEMANTIC/mcp-servers/BUSINESS-INSIGHTS-MCP --type http\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDesktop:\u003C/strong\u003E To add the MCP server in CoCo Desktop, go to \u003Cstrong\u003ESettings → MCP\u003C/strong\u003E and add a new HTTP server with the endpoint URL above. Or type \u003Ccode\u003E/mcp\u003C/code\u003E in the chat.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ENow any MCP-compatible client (CoCo, Claude Desktop, custom apps) can discover and call these tools via the standard MCP protocol.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOptional: Iceberg V3 Features\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E This section is optional. It demonstrates Iceberg V3 capabilities (deletion vectors, default values) using CoCo-generated SQL. No other sections depend on it.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E CoCo may take a few attempts to generate correct SQL for Iceberg V3 features (these are newer APIs). If you see "error executing SQL," let CoCo retry — it will self-correct and the end result will work.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate a Managed Iceberg Table\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt CoCo:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Create a managed Iceberg table from RAW.ORDERS with clustering by year and month, then query it to show partition pruning"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECoCo creates the table with \u003Ccode\u003ECATALOG='SNOWFLAKE'\u003C/code\u003E (no external volume needed) and demonstrates partition pruning on filtered queries.\u003C/p\u003E\n","\u003Ch3\u003EExplore V3: Deletion Vectors\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Create an Iceberg V3 table from RAW.ORDERS (ICEBERG_VERSION=3) with merge-on-read enabled, insert 1000 rows, then update 10 of them to demonstrate deletion vectors"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECoCo creates a V3 table with \u003Ccode\u003EENABLE_ICEBERG_MERGE_ON_READ = TRUE\u003C/code\u003E, inserts data, then runs an UPDATE that uses deletion vectors instead of full file rewrites.\u003C/p\u003E\n","\u003Ch3\u003EExplore V3: Default Values\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Add a new column 'priority' with default value 'STANDARD' to the V3 table and show that existing rows get the default without a backfill"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThis demonstrates V3 schema evolution without rewriting data files.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOptional: Streaming Ingestion\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E This section is optional. The \u003Ccode\u003Esetup.sql\u003C/code\u003E script already loads all 50M orders directly. This section demonstrates how you \u003Cem\u003Ewould\u003C/em\u003E stream data in production using the Snowpipe Streaming Python SDK.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EGenerate RSA Key Pair\u003C/h3\u003E\n","\u003Cp\u003EGenerate keys for Snowpipe Streaming authentication:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Generate private key (unencrypted PEM)\nopenssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt\n\n# Generate public key\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n\n# Upload public key to your Snowflake user (replace <your-username>)\nsnow sql -q "ALTER USER <your-username> SET RSA_PUBLIC_KEY='$(grep -v -- '-----' rsa_key.pub | tr -d '\\n')'" -c hol\n\n# Verify\nsnow sql -q "DESC USER <your-username>" -c hol | grep RSA_PUBLIC_KEY_FP\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStream Data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd snowpipe-streaming-python\npip install -r requirements.txt\n\n# Copy and configure profile\ncp profile.json.template profile.json\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEdit \u003Ccode\u003Eprofile.json\u003C/code\u003E and set your \u003Ccode\u003Eaccount\u003C/code\u003E, \u003Ccode\u003Euser\u003C/code\u003E, \u003Ccode\u003Eprivate_key\u003C/code\u003E (contents of rsa_key.p8), and \u003Ccode\u003Erole\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Stream 10,000 orders\npython src/automated_intelligence_streaming.py 10000\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Data Landed\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM dash_automated_intelligence_db.staging.orders_staging;\nSELECT COUNT(*) FROM dash_automated_intelligence_db.staging.order_items_staging;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see 10,000 orders and ~50,000 order items in staging.\u003C/p\u003E\n","\u003Ch3\u003EMerge into Production\u003C/h3\u003E\n","\u003Cp\u003EUse CoCo to merge the streamed data:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Switch to the Gen2 warehouse, check how many rows are in staging, then merge them into RAW and show me the results"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built a complete AI-powered retail analytics platform on Snowflake — from streaming ingestion through conversational AI and MCP server exposure.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to stream real-time data with Snowpipe Streaming and merge with Gen2 Warehouses\u003C/li\u003E\u003Cli\u003EHow to build incremental transformation pipelines with Dynamic Tables\u003C/li\u003E\u003Cli\u003EHow to serve sub-second point lookups with Interactive Tables\u003C/li\u003E\u003Cli\u003EHow to create and query Iceberg V3 tables with deletion vectors and default values\u003C/li\u003E\u003Cli\u003EHow to monitor and fix data quality gaps with Data Metric Functions\u003C/li\u003E\u003Cli\u003EHow to build dbt analytical models on Snowflake\u003C/li\u003E\u003Cli\u003EHow to create custom CoCo skills for repeatable workflows\u003C/li\u003E\u003Cli\u003EHow to build a Cortex Agent that routes across structured (Analyst) and unstructured (Search) data\u003C/li\u003E\u003Cli\u003EHow to evaluate agent quality with ground-truth datasets\u003C/li\u003E\u003Cli\u003EHow to implement transparent row-level security through AI agents\u003C/li\u003E\u003Cli\u003EHow to expose AI capabilities as managed MCP servers\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EDocumentation:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-overview\"\u003ESnowpipe Streaming SDK\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-about\"\u003EDynamic Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/interactive\"\u003EInteractive Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/warehouses-gen2\"\u003EGen2 Warehouses\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view\"\u003ESemantic Views\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-intro\"\u003EData Metric Functions\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-row-intro\"\u003ERow Access Policies\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ESnowflake CoCo\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Build an End-to-End Application Using CoCo on Snowflake",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nIn this hands-on lab, you'll build a complete AI-powered retail analytics platform entirely within Snowflake — no external infrastructure required. Using Snowflake CoCo as your AI-assisted development environment, you'll work through the full data lifecycle: stream real-time orders via Snowpipe Streaming, MERGE them into production tables with Gen2 Warehouses, transform them through a 3-tier Dynamic Tables pipeline, and serve them with Interactive Tables for low-latency point lookups.\n\nYou'll build analytical models with dbt, monitor data quality with Data Metric Functions, and create custom CoCo skills for reusable workflows. Tie it all together with Snowflake CoWork — a conversational AI interface where a Cortex Agent orchestrates Cortex Analyst and Agentic Search to answer \"what happened\" and \"why\" from both structured and unstructured data. Finally, evaluate your agent with ground-truth datasets, implement row-level security, and expose your agent as a managed MCP server for external AI clients.\n\n### What You'll Learn\n- Accelerate development with Snowflake CoCo (AI-assisted SQL, deployment, and data exploration)\n- Stream real-time data with Snowpipe Streaming and transform with Dynamic Tables\n- Serve low-latency queries with Interactive Tables and Gen2 Warehouses\n- Build analytical models with dbt\n- Monitor data quality automatically with Data Metric Functions\n- Create and query managed Iceberg V3 tables (deletion vectors, row lineage) *(optional)*\n- Create custom CoCo skills for reusable team workflows\n- Build a Cortex Agent with Cortex Analyst (semantic view + verified queries) and Agentic Search (multi-index Cortex Search)\n- Evaluate agent quality with ground-truth datasets and LLM judges\n- Expose agents as managed MCP servers for external AI clients\n- Implement transparent row-level security with Row Access Policies\n\n### What You'll Build\n\nA production-grade AI-powered retail analytics platform on Snowflake — from raw data to conversational AI insights, entirely within a single platform. You'll create dynamic transformation pipelines, interactive low-latency tables, dbt analytical models, a Cortex Agent that answers questions across structured and unstructured data, row-level security that works transparently through AI, and an MCP server that exposes your agent to external clients.\n\n### Prerequisites\n- Access to a [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)\n- Python 3.8+ installed locally\n- Git installed locally\n- Basic familiarity with SQL and command-line tools\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\n### Install Snowflake CLI\n\nThe Snowflake CLI (`snow`) lets you run SQL, deploy apps, and manage Snowflake objects from your terminal.\n\n**macOS (using Homebrew):**\n\nIf you don't have Homebrew installed, first install it by opening Terminal and running:\n```bash\n/bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\"\n```\n\nThen install Snowflake CLI:\n```bash\nbrew install snowflake-cli\n```\n\n**Windows:**\n```powershell\npip install snowflake-cli\n```\n\n**Linux:**\n```bash\npip install snowflake-cli\n```\n\nVerify the installation:\n```bash\nsnow --version\n```\n\nYou should see output like `Snowflake CLI version: 3.x.x`.\n\n### Install Snowflake CoCo\n\nSnowflake CoCo is an AI-powered coding assistant that runs in your terminal. It helps you write SQL, build pipelines, deploy apps, and explore your data using natural language prompts.\n\n**macOS (using Homebrew):**\n```bash\nbrew install cortex\n```\n\n**Windows / Linux:**\n```bash\npip install cortex\n```\n\nVerify the installation:\n```bash\ncortex --version\n```\n\n### Alternative: Snowflake CoCo Desktop (Preview)\n\nIf you prefer a visual IDE experience, [download CoCo Desktop](https://www.snowflake.com/en/product/limited-access/cortex-code/) instead of (or alongside) the CLI. It's a native Mac/Windows app with a file editor, integrated terminal, agentic browser, and the same AI capabilities.\n\n\u003E **Note:** CoCo Desktop is currently a [Preview Feature](https://docs.snowflake.com/release-notes/preview-features) available to all accounts.\n\nOn first launch, follow the onboarding wizard:\n1. Click **Next** on the Welcome screen\n2. Add your connection (same account identifier and credentials from above) or select an existing one detected from `connections.toml`\n3. Choose **Agent** mode\n4. Pick your theme, then click **Get Started**\n\n\u003E **Tip:** If you already configured a connection with `snow connection add` (below), CoCo Desktop detects it automatically — just select it from the list.\n\nSee [CoCo Desktop documentation](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-desktop/index) for full details.\n\n### Configure Snowflake Connection\n\nBefore you can run any commands against Snowflake, you need to configure a connection. This tells the CLI which Snowflake account to connect to and how to authenticate.\n\nRun the interactive connection wizard:\n```bash\nsnow connection add\n```\n\nYou'll be prompted for the following values (enter them one at a time):\n\n| Prompt | What to enter | Example |\n|--------|---------------|---------|\n| Connection name | A short name for this connection | `hol` |\n| Account identifier | Your Snowflake account URL (without `.snowflakecomputing.com`) | `myorg-myaccount` |\n| User | Your Snowflake username | `jsmith` |\n| Password | Your Snowflake password | *(hidden)* |\n| Role | `ACCOUNTADMIN` | `ACCOUNTADMIN` |\n| Warehouse | Leave blank (will be created by setup) | *(leave empty)* |\n| Database | Leave blank (will be created by setup) | *(leave empty)* |\n\n\u003E **Tip:** Your account identifier is the part before `.snowflakecomputing.com` in your Snowflake URL. For example, if you log in at `https://myorg-myaccount.snowflakecomputing.com`, your account identifier is `myorg-myaccount`.\n\nTest that your connection works:\n```bash\nsnow connection test -c hol\n```\n\nYou should see `Status: OK`.\n\n### Clone the Lab Repository\n\n```bash\ngit clone https://github.com/Snowflake-Labs/automated-intelligence-dev-day-2026-hol.git\ncd automated-intelligence-dev-day-2026-hol\n```\n\n### Run Infrastructure Setup\n\nLaunch Snowflake CoCo and verify your connection:\n\n```bash\ncortex\n```\n\n\u003E **What to expect:** CoCo will start an interactive session in your terminal. You'll see your active connection, role, and warehouse displayed. You can type natural language prompts and CoCo will translate them into SQL or actions.\n\nThen run the core infrastructure script (this takes ~10-15 minutes):\n\n```bash\nsnow sql -f setup.sql -c hol\n```\n\nThis creates the database, schemas, warehouses, tables, Dynamic Tables pipeline, Interactive Tables, Cortex Search Services, Semantic View, seed data (10M orders, 25M order items, 2M customers), and Row Access Policy.\n\n\u003E **Using CoCo Desktop?** All \"Prompt CoCo\" instructions below work identically in both CLI and Desktop — type the same text into the chat input. Anywhere you see `cortex` as a terminal command, use the Desktop's built-in terminal or chat instead.\n\n\u003C!-- ------------------------ --\u003E\n## Explore Your Data\n\nNow that setup is complete, let's get familiar with what was created. This is your first interaction with CoCo — try asking natural language questions about your data.\n\n**Prompt CoCo:**\n\n\u003E *\"What schemas and tables are in my database?\"*\n\nCoCo queries `INFORMATION_SCHEMA` and shows the database structure: RAW (source tables + views), STAGING (streaming landing zone), DYNAMIC_TABLES (pipeline), INTERACTIVE (low-latency), and SEMANTIC (AI layer).\n\n\u003E *\"Show me 5 sample rows from the orders table\"*\n\n\u003E *\"How many orders, customers, and products do we have?\"*\n\nExpected: ~10M orders, ~25M order items, 2M customers, 10 products, 1200 reviews, 1200 support tickets.\n\n\u003E *\"What's the date range of our order data?\"*\n\nExpected: June 2025 to September 2025.\n\nThis gives you a mental model of the dataset before we start transforming and analyzing it.\n\n\u003C!-- ------------------------ --\u003E\n## Data Quality\n\n[Data Metric Functions (DMFs)](https://docs.snowflake.com/en/user-guide/data-quality-intro) let you attach automated quality checks directly to table columns. Snowflake runs them on a schedule and stores results in `SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_RESULTS`. Built-in DMFs include `NULL_COUNT`, `DUPLICATE_COUNT`, `UNIQUE_COUNT`, and `FRESHNESS` — or you can write custom ones.\n\nThe setup script injected ~200 NULL values into `orders.total_amount` and `order_items.quantity`, plus ~150 NULLs into `order_items.product_name`. DMFs detect the first two — but there's a gap.\n\n### Discover the Gap\n\n**Prompt CoCo:**\n\n\u003E *\"Check the data quality monitoring results and show me which columns have NULL violations\"*\n\nCoCo shows that `TOTAL_AMOUNT` (200 NULLs) and `QUANTITY` (200 NULLs) have violations — but `product_name` NULLs are going undetected.\n\n\u003E *\"Are there any NULL values in order_items.product_name? Is that column being monitored?\"*\n\nCoCo finds ~150 NULLs and reveals the DMF is mis-attached to `product_category` instead of `product_name`.\n\n### Fix the Coverage\n\n\u003E *\"Fix the DMF — remove the NULL check from product_category and add it to product_name instead\"*\n\n```sql\nALTER TABLE order_items DROP DATA METRIC FUNCTION SNOWFLAKE.CORE.NULL_COUNT ON (product_category);\nALTER TABLE order_items ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.NULL_COUNT ON (product_name);\n```\n\nThis demonstrates the real-world workflow: monitor, discover gaps, fix coverage.\n\n\u003C!-- ------------------------ --\u003E\n## Dynamic Tables Pipeline\n\n[Dynamic Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-about) are declarative data pipelines — you define the target state as a SQL query and Snowflake handles incremental refresh automatically. The `TARGET_LAG` parameter controls freshness: set a time-based lag (e.g., `1 minute`) for Tier 1 tables, or use `DOWNSTREAM` for tables that refresh only when their upstream sources change.\n\n**Prompt CoCo:**\n\n\u003E *\"Show me the Dynamic Tables pipeline status — names, target lag, last refresh time, and row counts for each tier\"*\n\nCoCo displays the 3-tier pipeline:\n- **Tier 1** (1-min lag): `enriched_orders` (10M rows), `enriched_order_items` (25M rows)\n- **Tier 2** (DOWNSTREAM): `fact_orders` (25M rows)\n- **Tier 3** (DOWNSTREAM): `daily_business_metrics` (118 rows), `product_performance_metrics` (4 rows)\n\n\u003E **Note:** Row counts shown are for the default `data_scale = '10M'`. If you chose `'50M'` in `setup.sql`, expect ~50M enriched_orders, ~161M order_items, and 365 daily metrics rows.\n\n### Explore Results\n\nAsk CoCo:\n\n\u003E *\"Show me a sample of the daily business metrics — top 5 days by revenue\"*\n\nExpected: Top-5 days are in September 2025 (back-to-season peak), each with ~$183M revenue and ~117K orders.\n\n\u003C!-- ------------------------ --\u003E\n## dbt Analytics\n\n[dbt-snowflake](https://docs.snowflake.com/en/user-guide/data-engineering/dbt-projects-on-snowflake) transforms raw data into analytics-ready models using SQL SELECT statements. The dbt-snowflake adapter integrates natively with Snowflake, supporting incremental models, snapshots, and built-in testing. In this HOL, dbt creates staging views and mart tables for customer lifetime value, segmentation, product performance, and support analytics.\n\n\u003E **Prerequisites:** `dbt-core` and `dbt-snowflake` must be installed (`pip install dbt-snowflake`). CoCo will handle this for you if not already installed.\n\n**Prompt CoCo:**\n\n\u003E *\"Install dbt dependencies and build all models in the dbt-analytics project\"*\n\nCoCo runs `dbt deps` then `dbt build` to create all staging views and mart tables (9+ models). CoCo automatically injects your active Snowflake connection into the dbt profile — no manual configuration needed.\n\n\u003E **Expected output:** 71 tests pass, 1 warning (the `source_not_null_raw_orders_total_amount` test detects the 200 NULLs we injected for the Data Quality exercise — this is working as designed).\n\n### Explore Results\n\n\u003E *\"Show me the customer lifetime value segments — how many customers are in each value tier?\"*\n\n\u003C!-- ------------------------ --\u003E\n## Gen2 Warehouse: Optima Indexing\n\n[Gen2 Warehouses](https://docs.snowflake.com/en/user-guide/warehouses-gen2) introduce Optima Indexing — an automatic indexing layer that prunes partitions at query time without explicit clustering keys. Gen2 warehouses learn from your query patterns and build internal indices that accelerate point lookups and filtered scans.\n\nDemonstrate Optima Indexing in action:\n\n**Prompt CoCo:**\n\n\u003E *\"Run a point lookup for customer_id 5000 on the Gen2 warehouse\"*\n\nOpen the query profile in Snowsight to see partition pruning — only a fraction of partitions scanned despite no explicit clustering key. This is Gen2's Optima Indexing in action.\n\n\u003C!-- ------------------------ --\u003E\n## Interactive Tables\n\n[Interactive Tables](https://docs.snowflake.com/en/user-guide/interactive) are purpose-built for low-latency, high-concurrency point lookups. They maintain pre-computed results with clustering optimized for equality predicates, delivering sub-second response times for dashboard filters and application queries that would otherwise require full table scans.\n\n### Point Lookups\n\nRun queries in Snowsight to observe sub-second latency:\n\n```sql\nUSE WAREHOUSE hol_interactive_wh;\nALTER SESSION SET USE_CACHED_RESULT = FALSE;\n\n-- Point lookup by customer ID\nSELECT * FROM dash_automated_intelligence_db.interactive.customer_order_analytics\nWHERE customer_id = 1;\n\n-- Point lookup by order ID\n-- Run: SELECT order_id FROM dash_automated_intelligence_db.interactive.order_lookup LIMIT 5; to get UUIDs to use\nSELECT * FROM dash_automated_intelligence_db.interactive.order_lookup\nWHERE order_id = '\u003Cany-order-uuid\u003E';\n```\n\n### Concurrency Load Test\n\n**Prompt CoCo:**\n\n\u003E *\"Run the interactive tables load test at interactive/load_test.py\"*\n\nThis fires 200 concurrent sessions (1000 queries total) against both Interactive and Standard warehouses, then compares P50/P90/P99 latencies. You should see notably lower latency and higher throughput on the Interactive warehouse.\n\nRun the load test a second time to observe the effect of warm caches. Results may vary depending on account, region, and data scale.\n\n\u003C!-- ------------------------ --\u003E\n## CoCo Custom Skill\n\n[CoCo Custom Skills](https://docs.snowflake.com/en/user-guide/cortex-code/extensibility#skills) let you package repeatable workflows into named commands that any team member can invoke. A skill is a Markdown file (`.cortex/skills/\u003Cname\u003E/SKILL.md`) that defines triggers, parameters, and step-by-step instructions CoCo follows when the skill is activated.\n\nCreate a reusable skill that automates table profiling:\n\n**Prompt CoCo:**\n\n\u003E *\"Create a custom CoCo skill called 'profile-table' that takes a table name, counts rows, checks for NULL columns, shows distinct value counts, and flags potential data quality issues\"*\n\nCoCo creates `.cortex/skills/profile-table/SKILL.md` with the skill definition, triggers, and step-by-step instructions.\n\n\u003E **Note:** After creating the skill, restart your CoCo session for the new skill to become active. **CLI:** type `/quit` then `cortex`. **Desktop:** open a new session from the sidebar.\n\n### Test It\n\n\u003E *\"$profile-table DASH_AUTOMATED_INTELLIGENCE_DB.RAW.ORDERS\"*\n\nThis demonstrates how teams package repeatable workflows as shareable CoCo skills.\n\n\u003C!-- ------------------------ --\u003E\n## Snowflake CoWork\n\n[Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents) are multi-tool AI orchestrators that route questions to the right data source. They combine [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) (text-to-SQL over [Semantic Views](https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view)) with [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview) (vector + keyword search over unstructured data) to answer both \"what happened\" and \"why\" from a single conversational interface.\n\n### Create the Agent\n\n**Prompt CoCo:**\n\n\u003E *\"Run snowflake-cowork/create_agent.sql to create the Business Insights Agent\"*\n\n### Test Agent Routing\n\nOpen the **Snowflake CoWork** interface in Snowsight: navigate to **AI & ML → CoWork** (or search \"CoWork\" in the global search bar). Select the `BUSINESS_INSIGHTS_AGENT` agent. Then try each question to demonstrate different tool routing:\n\n| Question | Tools Used |\n|----------|-----------|\n| \"Show me monthly revenue trend from June to September 2025\" | Cortex Analyst (text-to-SQL) |\n| \"Which month had the lowest revenue, and what do customer reviews say about that period?\" | Cortex Analyst + Agentic Search |\n| \"Find reviews mentioning wrong size with a rating below 3\" | Agentic Search (filtered) |\n| \"Why are customers returning ski boots?\" | Agentic Search (reviews + tickets) |\n| \"What is our total revenue and customer count by state?\" | Cortex Analyst (text-to-SQL) |\n| \"What are the top complaint themes in support tickets?\" | Agentic Search (filter + AI_AGG) |\n| \"How many reviews mention sizing issues, and which products are most affected?\" | Agentic Search (search + breakdown) |\n\nThis is the capstone moment — the agent routes across structured data (text-to-SQL) and unstructured data (Cortex Search) to answer \"what happened\" and \"why.\"\n\n\u003C!-- ------------------------ --\u003E\n## Security and Governance\n\n[Row Access Policies](https://docs.snowflake.com/en/user-guide/security-row-intro) enforce row-level security declaratively — you define a boolean expression that determines which rows are visible to which roles, and Snowflake applies it transparently to every query (including those generated by AI agents). No application code changes required.\n\nThe Row Access Policy and WEST_COAST_MANAGER role were created by `setup.sql`. Demonstrate how row-level security transparently filters data based on role:\n\n**As ACCOUNTADMIN (full access):**\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nUSE WAREHOUSE HOL_WH;\nSELECT c.state, SUM(o.total_amount) AS total_revenue, COUNT(DISTINCT c.customer_id) AS customer_count\nFROM dash_automated_intelligence_db.raw.orders o\nJOIN dash_automated_intelligence_db.raw.customers c ON o.customer_id = c.customer_id\nGROUP BY c.state ORDER BY total_revenue DESC;\n```\n\nResult: all 10 states visible.\n\n**As WEST_COAST_MANAGER (restricted):**\n\n```sql\nUSE ROLE WEST_COAST_MANAGER;\nUSE WAREHOUSE HOL_WH;\nSELECT c.state, SUM(o.total_amount) AS total_revenue, COUNT(DISTINCT c.customer_id) AS customer_count\nFROM dash_automated_intelligence_db.raw.orders o\nJOIN dash_automated_intelligence_db.raw.customers c ON o.customer_id = c.customer_id\nGROUP BY c.state ORDER BY total_revenue DESC;\n```\n\nResult: only CA, OR, WA appear — the Row Access Policy transparently filters data.\n\nKey insight: Same query, same tables — different results based on who's asking. Row-level security enforces data boundaries without changing application logic.\n\n### Verify Through CoWork\n\nSwitch to the **WEST_COAST_MANAGER** role in Snowsight, then open CoWork and ask the agent:\n\n\u003E *\"What is our total revenue and customer count by state?\"*\n\nThe agent returns results for only CA, OR, and WA — the Row Access Policy filters data transparently, even through AI-generated SQL.\n\n\u003C!-- ------------------------ --\u003E\n## Streamlit Dashboard\n\n[Streamlit in Snowflake](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit) lets you build and deploy interactive data applications directly within your Snowflake account — no external infrastructure needed. Apps run securely inside Snowflake, with native access to your data and governed by the same role-based access control.\n\n\u003E **Prerequisites:** The dbt models must be built first (see the **dbt Analytics** section). The dashboard queries `DBT_ANALYTICS` and `DBT_STAGING` tables.\n\n**Prompt CoCo:**\n\n\u003E *\"Deploy the Streamlit dashboard to Snowflake\"*\n\nCoCo runs `snow streamlit deploy` from the `streamlit-dashboard/` directory. Once deployed, open the app URL in Snowsight to explore:\n\n- **Summary** — Revenue KPIs, order trends, customer counts\n- **Customer & Product Analytics** — Lifetime value segments, product performance\n- **Pipeline Health** — Dynamic Tables refresh status, data freshness monitoring\n\n\u003C!-- ------------------------ --\u003E\n## Agent Evaluation\n\n[Agent Evaluation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#evaluating-agents) lets you measure agent quality using ground-truth datasets and LLM-as-judge scoring. You define input queries and expected answers, then Snowflake runs the agent against each question and scores responses on Answer Correctness (does the response match ground truth?) and Logical Consistency (are the reasoning steps internally coherent?).\n\nThe evaluation dataset (7 questions + ground truth) was created by `setup.sql`. Run the evaluation in Snowsight:\n\n### Run via Snowsight UI\n\n1. Switch to the **ACCOUNTADMIN** role in Snowsight (top-left role selector)\n2. Navigate to **AI and ML \u003E Agents \u003E BUSINESS_INSIGHTS_AGENT \u003E Evaluations** tab\n3. Click **New evaluation run**, name it (e.g. `hol-eval-run-1`), click **Next**\n4. Select **Create new dataset from table**\n5. Under **Source table**, set Database and schema to `DASH_AUTOMATED_INTELLIGENCE_DB.SEMANTIC`, then select `AGENT_EVALUATION_DATA`\n6. Under **New dataset location**, keep `DASH_AUTOMATED_INTELLIGENCE_DB.SEMANTIC`\n7. Set **Dataset name**: `hol_eval_dataset`\n8. Click **Next**\n9. Under **Define metrics**, confirm **Input query** = `INPUT_QUERY`\n10. Toggle on **Answer Correctness**, set **Expected answer** = `GROUND_TRUTH`\n11. Toggle on **Logical Consistency**\n12. Click **Create** — evaluation starts automatically (~3 min)\n\n### Interpret Results\n\n- **Answer Correctness** — Did the agent's response match ground truth? Scored 0-1 per question.\n- **Logical Consistency** — Were planning steps, tool calls, and response internally consistent? (Reference-free.)\n- **Per-question drill-down** — Select any row to see the full thread: planning, tool invocations, response generation.\n\n### Improve Scores (Stretch)\n\nIf questions score low on logical consistency:\n1. Click a low-scoring row and view Thread details\n2. Look for vague reasoning about tool selection in the Planning step\n3. Update the agent's instructions to be more explicit\n4. Recreate the agent and re-run the evaluation\n\n\u003C!-- ------------------------ --\u003E\n## MCP Server\n\n[Snowflake MCP Servers](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#managed-mcp-servers) expose your Cortex Agents, Semantic Views, and Search Services as tools discoverable via the open Model Context Protocol (MCP). Any MCP-compatible client (CoCo CLI, Claude Desktop, custom apps) can connect to the server endpoint and invoke tools programmatically — turning your Snowflake AI stack into a reusable service layer.\n\nExpose the Business Insights Agent as a managed MCP server:\n\n**Prompt CoCo:**\n\n\u003E *\"Create a Snowflake-managed MCP server that exposes our Business Insights Agent, semantic view, and customer feedback search as tools\"*\n\nCoCo creates the MCP server:\n\n\u003E **Note:** CoCo generates tool names (like `revenue-analytics`, `customer-feedback-search`) based on your prompt. Your names may differ — what matters is that the `type` and `identifier` point to the correct objects.\n\n```sql\nCREATE MCP SERVER business_insights_mcp\n  FROM SPECIFICATION $$\n    tools:\n      - name: \"business-insights-agent\"\n        type: \"CORTEX_AGENT_RUN\"\n        identifier: \"DASH_AUTOMATED_INTELLIGENCE_DB.SEMANTIC.BUSINESS_INSIGHTS_AGENT\"\n        description: \"AI agent that answers business questions using structured data and customer feedback\"\n        title: \"Business Insights Agent\"\n\n      - name: \"revenue-analytics\"\n        type: \"CORTEX_ANALYST_MESSAGE\"\n        identifier: \"DASH_AUTOMATED_INTELLIGENCE_DB.SEMANTIC.BUSINESS_ANALYTICS_SEMANTIC\"\n        description: \"Text-to-SQL for revenue, orders, customers, and product metrics\"\n        title: \"Revenue Analytics\"\n\n      - name: \"customer-feedback-search\"\n        type: \"CORTEX_SEARCH_SERVICE_QUERY\"\n        identifier: \"DASH_AUTOMATED_INTELLIGENCE_DB.RAW.CUSTOMER_FEEDBACK_SEARCH\"\n        description: \"Search across product reviews and support tickets\"\n        title: \"Customer Feedback Search\"\n  $$;\n```\n\n### Connect from CoCo\n\n```bash\ncortex mcp add business-insights https://\u003Caccount_url\u003E/api/v2/databases/DASH_AUTOMATED_INTELLIGENCE_DB/schemas/SEMANTIC/mcp-servers/BUSINESS-INSIGHTS-MCP --type http\n```\n\n\u003E **Desktop:** To add the MCP server in CoCo Desktop, go to **Settings → MCP** and add a new HTTP server with the endpoint URL above. Or type `/mcp` in the chat.\n\nNow any MCP-compatible client (CoCo, Claude Desktop, custom apps) can discover and call these tools via the standard MCP protocol.\n\n\u003C!-- ------------------------ --\u003E\n## Optional: Iceberg V3 Features\n\n\u003E **Note:** This section is optional. It demonstrates Iceberg V3 capabilities (deletion vectors, default values) using CoCo-generated SQL. No other sections depend on it.\n\n\u003E **Note:** CoCo may take a few attempts to generate correct SQL for Iceberg V3 features (these are newer APIs). If you see \"error executing SQL,\" let CoCo retry — it will self-correct and the end result will work.\n\n### Create a Managed Iceberg Table\n\n**Prompt CoCo:**\n\n\u003E *\"Create a managed Iceberg table from RAW.ORDERS with clustering by year and month, then query it to show partition pruning\"*\n\nCoCo creates the table with `CATALOG='SNOWFLAKE'` (no external volume needed) and demonstrates partition pruning on filtered queries.\n\n### Explore V3: Deletion Vectors\n\n\u003E *\"Create an Iceberg V3 table from RAW.ORDERS (ICEBERG_VERSION=3) with merge-on-read enabled, insert 1000 rows, then update 10 of them to demonstrate deletion vectors\"*\n\nCoCo creates a V3 table with `ENABLE_ICEBERG_MERGE_ON_READ = TRUE`, inserts data, then runs an UPDATE that uses deletion vectors instead of full file rewrites.\n\n### Explore V3: Default Values\n\n\u003E *\"Add a new column 'priority' with default value 'STANDARD' to the V3 table and show that existing rows get the default without a backfill\"*\n\nThis demonstrates V3 schema evolution without rewriting data files.\n\n\u003C!-- ------------------------ --\u003E\n## Optional: Streaming Ingestion\n\n\u003E **Note:** This section is optional. The `setup.sql` script already loads all 50M orders directly. This section demonstrates how you *would* stream data in production using the Snowpipe Streaming Python SDK.\n\n### Generate RSA Key Pair\n\nGenerate keys for Snowpipe Streaming authentication:\n\n```bash\n# Generate private key (unencrypted PEM)\nopenssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt\n\n# Generate public key\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n\n# Upload public key to your Snowflake user (replace \u003Cyour-username\u003E)\nsnow sql -q \"ALTER USER \u003Cyour-username\u003E SET RSA_PUBLIC_KEY='$(grep -v -- '-----' rsa_key.pub | tr -d '\\n')'\" -c hol\n\n# Verify\nsnow sql -q \"DESC USER \u003Cyour-username\u003E\" -c hol | grep RSA_PUBLIC_KEY_FP\n```\n\n### Stream Data\n\n```bash\ncd snowpipe-streaming-python\npip install -r requirements.txt\n\n# Copy and configure profile\ncp profile.json.template profile.json\n```\n\nEdit `profile.json` and set your `account`, `user`, `private_key` (contents of rsa_key.p8), and `role`.\n\n```bash\n# Stream 10,000 orders\npython src/automated_intelligence_streaming.py 10000\n```\n\n### Verify Data Landed\n\n```sql\nSELECT COUNT(*) FROM dash_automated_intelligence_db.staging.orders_staging;\nSELECT COUNT(*) FROM dash_automated_intelligence_db.staging.order_items_staging;\n```\n\nYou should see 10,000 orders and ~50,000 order items in staging.\n\n### Merge into Production\n\nUse CoCo to merge the streamed data:\n\n\u003E *\"Switch to the Gen2 warehouse, check how many rows are in staging, then merge them into RAW and show me the results\"*\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations! You've successfully built a complete AI-powered retail analytics platform on Snowflake — from streaming ingestion through conversational AI and MCP server exposure.\n\n### What You Learned\n- How to stream real-time data with Snowpipe Streaming and merge with Gen2 Warehouses\n- How to build incremental transformation pipelines with Dynamic Tables\n- How to serve sub-second point lookups with Interactive Tables\n- How to create and query Iceberg V3 tables with deletion vectors and default values\n- How to monitor and fix data quality gaps with Data Metric Functions\n- How to build dbt analytical models on Snowflake\n- How to create custom CoCo skills for repeatable workflows\n- How to build a Cortex Agent that routes across structured (Analyst) and unstructured (Search) data\n- How to evaluate agent quality with ground-truth datasets\n- How to implement transparent row-level security through AI agents\n- How to expose AI capabilities as managed MCP servers\n\n### Related Resources\n\nDocumentation:\n- [Snowpipe Streaming SDK](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-overview)\n- [Dynamic Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-about)\n- [Interactive Tables](https://docs.snowflake.com/en/user-guide/interactive)\n- [Gen2 Warehouses](https://docs.snowflake.com/en/user-guide/warehouses-gen2)\n- [Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents)\n- [Semantic Views](https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view)\n- [Data Metric Functions](https://docs.snowflake.com/en/user-guide/data-quality-intro)\n- [Row Access Policies](https://docs.snowflake.com/en/user-guide/security-row-intro)\n- [Snowflake CoCo](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-90e1ba5b67","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-30ee6e912e",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-0f9b102b8b","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-08",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-fb567f5d83","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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