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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 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Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-7a1956a658","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":{"layout":"SIMPLE","id":"container-07cf50a2ca",":items":{"contentfragment":{"id":"contentfragment-ec9803799a","description":"","paragraphs":["\u003Cp\u003Eauthor: Snowplow\nid: snowplow-web-analytics\ncategories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/solution-center/certification/partner-solution, snowflake-site:taxonomy/solution-center/includes/architecture, snowflake-site:taxonomy/product/analytics\nlanguage: en\nsummary: Set up real-time event tracking and analytics using Snowplow and Snowflake\nenvironments: web\nstatus :Published\nfeedback link: https://github.com/Snowflake-Labs/sfguides/issues\u003C/p\u003E\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EIn this guide, you'll learn how to set up real-time event tracking and analytics, configure the Snowflake Streaming Loader for immediate data availability, and deploy Snowplow’s Unified Digital dbt package for simplified data modeling and consumption. By the end of this tutorial, you'll have a fully functioning analytics pipeline, enabling you to make data-driven decisions swiftly and accurately.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA pipeline for behavioral data from the source to the analysis\u003C/li\u003E\u003Cli\u003EA quick analysis of that behavioral data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up Snowplow's tracking for event data collection\u003C/li\u003E\u003Cli\u003EHow to configure the Snowflake Streaming Loader to ingest data in near real-time\u003C/li\u003E\u003Cli\u003EHow to deploy Unified Digital dbt package in Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://snowplow.io/\"\u003ESnowplow Behavioral Data Platform (BDP) subscription\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EBasic knowledge of SQL and Snowflake\u003C/li\u003E\u003Cli\u003EAccess to Snowflake account\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESet up Tracking\u003C/h2\u003E\n","\u003Cp\u003EWith Snowplow, tracking capabilities are virtually limitless, offering a wide range of possibilities.\u003C/p\u003E\n","\u003Cp\u003EBasic tracking functionalities can be easily integrated into your app by following \u003Ca href=\"https://docs.snowplow.io/docs/recipes/recipe-basic-tracking-design/\"\u003Ethis guide\u003C/a\u003E, while more advanced features can be unlocked by utilizing Snowplow’s full suite of products.\u003C/p\u003E\n","\u003Ch3\u003EExample Web Tracking Implementation with Data Products and Automatic Code Generation\u003C/h3\u003E\n","\u003Cp\u003EHere's a summary of the steps to implement a basic web tracking setup using Snowplow’s Data Products and advanced Snowplow features:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ECreate a Source Application\u003C/strong\u003E:\nDefine your web app's tracking source and configure the necessary application contexts.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/source_application.png\" alt=\"alt text\"\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESet up Base Data Product\u003C/strong\u003E:\nUse the Base Web Data Product template to track standard events like page views and clicks in your app.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/base_data_product.png\" alt=\"alt text\"\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInitialize the Tracker\u003C/strong\u003E:\nInstall \u003Ca href=\"https://docs.snowplow.io/docs/collecting-data/collecting-from-own-applications/javascript-trackers/web-tracker/quick-start-guide/#quick-start-basic\"\u003ESnowplow's browser tracker\u003C/a\u003E, configure it to automatically send page views and page pings, and track user interactions.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVerify Event Data\u003C/strong\u003E:\nUse the \u003Ca href=\"https://docs.snowplow.io/docs/testing-debugging/snowplow-inspector/overview/\"\u003ESnowplow Inspector browser extension\u003C/a\u003E to confirm that page views, clicks, and other tracked events are being recorded.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/inspector.png\" alt=\"alt text\"\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECreate Custom Data Structures\u003C/strong\u003E:\nYou can define custom data structures (e.g., "new_game" and "start_game") to track specific website or app interactions and utilize Snowtype to generate code for custom event tracking. More details about that in the full documentation below.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowplow.io/docs/recipes/recipe-data-products-base-tracking/\"\u003EFor a complete setup guide/recipe, visit Snowplow's official documentation.\u003C/a\u003E\u003C/p\u003E\n","\u003Ch2\u003EConfigure Snowflake Streaming Loader\u003C/h2\u003E\n","\u003Cp\u003EIn this section, you'll learn how to set up the Snowflake Streaming Loader to ingest Snowplow event data into Snowflake in near real-time. By the end of this step, you'll have a streaming pipeline that delivers enriched event data with minimal latency.\u003C/p\u003E\n","\u003Ch3\u003EIntroduction to Snowflake's Snowpipe Streaming\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowpipe Streaming\u003C/strong\u003E is Snowflake's real-time data ingestion service, which allows for the immediate availability of data as it's ingested. This capability is crucial for businesses that rely on up-to-the-minute data to make timely, informed decisions.\u003C/p\u003E\n","\u003Ch3\u003ESnowflake Streaming Loader\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ca href=\"https://docs.snowplow.io/docs/pipeline-components-and-applications/loaders-storage-targets/snowflake-streaming-loader/\"\u003ESnowflake Streaming Loader\u003C/a\u003E is an advanced data integration solution that continuously captures and delivers real-time information to Snowflake, enabling organizations to access up-to-the-minute insights and drive informed decision-making.\u003C/p\u003E\n","\u003Ch4\u003EBenefits of using Snowflake Streaming Loader\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ELow Latency\u003C/strong\u003E: Data becomes available in Snowflake within seconds, enabling near real-time analytics.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECost-Efficiency\u003C/strong\u003E: Eliminates the need for expensive infrastructure like EMR clusters or large compute instances.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EImmediate Availability\u003C/strong\u003E: As soon as data is generated, it's ready for querying.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 1: Go to Destinations\u003C/h4\u003E\n","\u003Cp\u003EGo to your Snowplow BDP console, under infra in destinations, and select the Snowflake ( Streaming Option )\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/bdp_dests.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Set Up the Configuration\u003C/h4\u003E\n","\u003Cp\u003EComplete your details\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/snowflake_streaming_1.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eand then generate and run the script to set up everything that will be needed in Snowflake\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/snowflake_streaming_2.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Complete destination set up\u003C/h4\u003E\n","\u003Cp\u003EClick the “Test and complete destination set up” button and reach out to Snowplow Support (support@snowplow.io) to finish the process\u003C/p\u003E\n","\u003Ch2\u003EDeploy Unified Digital dbt Package\u003C/h2\u003E\n","\u003Cp\u003EIn this section, we'll explore how to deploy the Unified Digital dbt package in Snowflake to transform raw event data into modeled data and actionable insights. We will use the raw data that we previously ingested in Snowflake using the streaming loader\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to Snowplow BDP.\u003C/li\u003E\u003Cli\u003EA Snowflake account with permissions to create databases, schemas, and roles.\u003C/li\u003E\u003Cli\u003EAdmin rights in Snowplow BDP to set up connections and models.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EUnderstanding Unified Digital dbt Package\u003C/h3\u003E\n","\u003Cp\u003EThe unified data package is essential for consistent analytics across different data sources and platforms. It standardizes the structure and schema of your data, making it easier to query and derive insights.\u003C/p\u003E\n","\u003Ch4\u003EWhy it's useful\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EConsistency\u003C/strong\u003E: Ensures that all data adheres to the same structure.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESimplified Consumption\u003C/strong\u003E: Makes it easier for analysts and data scientists to work with the data.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EScalability\u003C/strong\u003E: Simplifies the addition of new data sources or event types.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EDeployment Steps\u003C/h3\u003E\n","\u003Cp\u003EWe'll use Snowplow BDP to run the standard data models in Snowflake.\u003C/p\u003E\n","\u003Ch4\u003EStep 1: Create a Warehouse Connection\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ENavigate to Data Models\u003C/strong\u003E: Log in to Snowplow BDP Console and click on the \u003Cstrong\u003EData Models\u003C/strong\u003E tab.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESet Up New Connection\u003C/strong\u003E:\n\u003Cul\u003E\u003Cli\u003EClick on \u003Cstrong\u003ESet up new connection\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EFill in the required Snowflake connection details:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EHostname\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase Name\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUser\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERole\u003C/strong\u003E (specific to data modeling)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Use a role with permissions tailored for data modeling activities.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/model_connection.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\u003C/p\u003E\n","\u003Cp\u003EIf you've already set up a warehouse connection for loading data, you will still need to create a new connection specifically for data modeling. This is required since data models often require different users, roles, and permissions to manage how data is accessed and processed.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 2: Create a Data Model\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EAdd Data Model\u003C/strong\u003E: Click on \u003Cstrong\u003EAdd data model\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESelect the Unified Digital Model\u003C/strong\u003E:\n\u003Cul\u003E\u003Cli\u003EChoose \u003Cstrong\u003EUnified Digital\u003C/strong\u003E from the list of standard models.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/available_models.png\" alt=\"alt text\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAssign Warehouse Connection\u003C/strong\u003E:\n\u003Cul\u003E\u003Cli\u003ELink the data model to the warehouse connection you just created.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESpecify Model Owners\u003C/strong\u003E:\n\u003Cul\u003E\u003Cli\u003EAdd team members who should receive alerts in case of model run issues.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/model_last_screen.png\" alt=\"alt text\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EStep 3: Adjust Data Models Configuration\u003C/h4\u003E\n","\u003Cp\u003EWe can go to the dedicated screen per model and configure the model as we want\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/unified_model_view.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESteps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ECheck the Data Models Page\u003C/strong\u003E:\n\u003Cul\u003E\u003Cli\u003ENavigate to the \u003Cstrong\u003EData Models\u003C/strong\u003E page.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchedule the Model\u003C/strong\u003E:\n\u003Cul\u003E\u003Cli\u003ESet the frequency for the data model to run (e.g., hourly, daily).\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAlerts\u003C/strong\u003E:\n\u003Cul\u003E\u003Cli\u003EOwners will receive email notifications if a model run fails.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustomize Based on Business Needs\u003C/strong\u003E:\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EAbility to configure variables.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ETips on Customizing Models\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAdjust Metrics\u003C/strong\u003E: Modify the model to include metrics that are specific to your business needs.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdd Modules\u003C/strong\u003E: Snowplow offers additional modules for Consent Reporting, Core Web Vitals, etc.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUser Mapping\u003C/strong\u003E: Utilize the User Mapping Module for advanced session stitching.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGains\u003C/strong\u003E: Deploying the Unified Digital Model simplifies your data landscape. With standardized tables and fields, your team can focus on generating insights rather than spending time and engineering resources wrangling data. Visit \u003Ca href=\"https://docs.snowplow.io/docs/modeling-your-data/modeling-your-data-with-dbt/dbt-models/dbt-unified-data-model/\"\u003EUnified documentation\u003C/a\u003E for further customization\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003EExplore Modeled with Snowflake Notebooks\u003C/h2\u003E\n","\u003Cp\u003EIn this section, we'll explore your processed behavioral derived data using Snowflake Notebooks in depth. We'll focus on the derived tables created by the Unified Digital package, such as \u003Ccode\u003Ederived.snowplow_unified_users\u003C/code\u003E, \u003Ccode\u003Ederived.snowplow_unified_sessions\u003C/code\u003E, and \u003Ccode\u003Ederived.snowplow_unified_views\u003C/code\u003E. This hands-on session will guide you through querying these tables to extract valuable insights into user behavior, session metrics, and content performance.\u003C/p\u003E\n","\u003Ch3\u003EExploring Derived Tables in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EDerived tables are the result of transforming raw event data into structured, analytical datasets. They aggregate and organize data to provide meaningful insights without the complexity of raw event logs.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Ederived.snowplow_unified_users\u003C/code\u003E\u003C/strong\u003E: Aggregated user-level data.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Ederived.snowplow_unified_sessions\u003C/code\u003E\u003C/strong\u003E: Data summarizing user sessions.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Ederived.snowplow_unified_views\u003C/code\u003E\u003C/strong\u003E: Information about page or screen views.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 1: Accessing the Derived Tables\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOpen Snowflake Notebooks\u003C/strong\u003E: Log in to your Snowflake account and navigate to the \u003Cstrong\u003ENotebooks\u003C/strong\u003E section.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate a New Notebook\u003C/strong\u003E: Click on \u003Cstrong\u003ECreate Notebook\u003C/strong\u003E and select the database and schema where your derived tables are stored (e.g., \u003Ccode\u003Esnowplow.derived\u003C/code\u003E).\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EList Available Tables\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW TABLES IN SCHEMA snowplow.derived;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis command lists all the derived tables available for analysis.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EStep 2: Analyzing User-Level Data\u003C/h4\u003E\n","\u003Cp\u003ELet's start by exploring the \u003Ccode\u003Ederived.snowplow_unified_users\u003C/code\u003E table to gain insights into your user base.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 1: View Sample User Data\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM derived.snowplow_unified_users LIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Get acquainted with the structure and fields of the user data.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnalysis\u003C/strong\u003E: Review the sample data to understand available user attributes.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 2: User Acquisition Over Time\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  DATE_TRUNC('day', START_TSTAMP) AS first_session_date,\n  COUNT(*) AS new_users\nFROM\n  derived.snowplow_unified_users\nGROUP BY\n  first_session_date\nORDER BY\n  first_session_date;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Observe trends in new user sign-ups.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVisualization\u003C/strong\u003E: Create a line chart to visualize user acquisition over time.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 3: Top Engaged Users\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\nSELECT\n  USER_ID,\n  SESSIONS,\n  ENGAGED_TIME_IN_S\nFROM derived.snowplow_unified_users\nORDER BY\n  ENGAGED_TIME_IN_S DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Identify users with the highest engagement.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnalysis\u003C/strong\u003E: These users may be valuable for feedback or loyalty programs.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 3: Examining Session Data\u003C/h4\u003E\n","\u003Cp\u003ENext, we'll delve into the \u003Ccode\u003Ederived.snowplow_unified_sessions\u003C/code\u003E table to understand user interaction patterns.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 1: Average Session Duration\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  AVG(engaged_time_in_s) AS average_session_duration\nFROM\n  derived.snowplow_unified_sessions;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Determine the typical length of user sessions.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnalysis\u003C/strong\u003E: A longer average session duration may indicate higher user engagement.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 2: Sessions Over the Last 7 Days\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  DATE_TRUNC('day', START_TSTAMP) AS session_date,\n  COUNT(*) AS session_count\nFROM\n  derived.snowplow_unified_sessions\nWHERE\n  START_TSTAMP >= DATEADD('day', -7, CURRENT_DATE)\nGROUP BY\n  session_date\nORDER BY\n  session_date;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Analyze daily session activity for the past week.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVisualization\u003C/strong\u003E: Plot a bar chart to highlight trends or spikes in user sessions.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 3: Session Counts by User\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  user_id,\n  COUNT(*) AS session_count\nFROM derived.snowplow_unified_sessions\nGROUP BY\n  user_id\nORDER BY\n  session_count DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Find users with the most sessions.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnalysis\u003C/strong\u003E: Frequent visitors are key to understanding user loyalty.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 4: Investigating Page and Screen Views\u003C/h4\u003E\n","\u003Cp\u003ENow, let's explore the \u003Ccode\u003Ederived.snowplow_unified_views\u003C/code\u003E table to assess content performance.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 1: Most Viewed Pages\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  page_urlpath,\n  COUNT(*) AS view_count\nFROM derived.snowplow_unified_views\nGROUP BY\n  page_urlpath\nORDER BY\n  view_count DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Identify the most popular pages on your site.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnalysis\u003C/strong\u003E: Use this information to optimize high-traffic pages.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 2: Average Time Spent per Page\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  page_urlpath,\n  AVG(ENGAGED_TIME_IN_S) AS average_time_spent\nFROM derived.snowplow_unified_views\nGROUP BY page_urlpath\nORDER BY average_time_spent DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Discover which pages retain user attention the longest.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnalysis\u003C/strong\u003E: Pages with higher engagement may serve as models for other content.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 3: Page Views by Device Type\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  DEVICE_CATEGORY,\n  COUNT(*) AS view_count\nFROM derived.snowplow_unified_views\nGROUP BY DEVICE_CATEGORY\nORDER BY view_count DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Understand the distribution of device types accessing your content.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnalysis\u003C/strong\u003E: Optimize user experience for the most commonly used devices.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 5: Understanding Geographic Distribution\u003C/h4\u003E\n","\u003Cp\u003EAnalyzing geographic data helps tailor content and marketing strategies to your audience locations.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 1: Sessions by Country\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  FIRST_GEO_COUNTRY,\n  COUNT(*) AS session_count\nFROM derived.snowplow_unified_sessions\nGROUP BY FIRST_GEO_COUNTRY\nORDER BY session_count DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Identify key markets based on user sessions.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVisualization\u003C/strong\u003E: Create a map visualization to see the geographic spread.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 2: New Users by Region\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  FIRST_GEO_REGION_NAME,\n  COUNT(*) AS new_user_count\nFROM derived.snowplow_unified_users\nGROUP BY FIRST_GEO_REGION_NAME\nORDER BY new_user_count DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Understand where new users are signing up.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnalysis\u003C/strong\u003E: Assess the effectiveness of regional marketing efforts.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 6: Segmenting Users for Deeper Insights\u003C/h4\u003E\n","\u003Cp\u003EUser segmentation allows for personalized marketing and improved user experiences.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery 1: High-Value User Segment\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  user_id,\n  ENGAGED_TIME_IN_S\nFROM derived.snowplow_unified_users\nWHERE\n  ENGAGED_TIME_IN_S > (\n    SELECT AVG(ENGAGED_TIME_IN_S) FROM derived.snowplow_unified_users\n  )\nORDER BY  ENGAGED_TIME_IN_S DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Identify users who are more engaged than average.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnalysis\u003C/strong\u003E: Target these users with special offers or exclusive content.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 7: Collaborating with Snowflake Notebooks\u003C/h4\u003E\n","\u003Cp\u003EMaximize the value of your analysis by collaborating within your team.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESharing Notebooks\u003C/strong\u003E: Click the \u003Cstrong\u003EShare\u003C/strong\u003E button to invite colleagues to view or edit your notebook.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECommenting\u003C/strong\u003E: Use inline comments to discuss specific queries or results.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVersion Control\u003C/strong\u003E: Keep track of changes to maintain the integrity of your analyses.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EBringing It All Together\u003C/h3\u003E\n","\u003Cp\u003EBy systematically exploring the derived tables, you've uncovered:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EUser Acquisition Trends\u003C/strong\u003E: Understanding how and when users join your platform.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEngagement Metrics\u003C/strong\u003E: Identifying your most active users and sessions.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EContent Performance\u003C/strong\u003E: Recognizing which pages and content types resonate with your audience.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGeographical Insights\u003C/strong\u003E: Tailoring strategies to regions with the highest impact.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUser Segmentation\u003C/strong\u003E: Personalizing experiences for different user groups.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps and Advanced Analyses\u003C/h3\u003E\n","\u003Cp\u003EWith a solid understanding of your data, consider:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EBuilding Custom Dashboards\u003C/strong\u003E: Create tailored views for stakeholders.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EImplementing Machine Learning Models\u003C/strong\u003E: Predict user behavior and personalize content.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConducting A/B Testing Analyses\u003C/strong\u003E: Evaluate the effectiveness of changes to your platform.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EBy focusing on the derived tables, you've efficiently navigated through high-level insights, enabling strategic decision-making without getting frustrated by raw data complexity. Continue to explore and customize your analyses to align with your business objectives.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ENavigated and queried key derived tables (\u003Ccode\u003Ederived.users\u003C/code\u003E, \u003Ccode\u003Ederived.snowplow_unified_sessions\u003C/code\u003E, \u003Ccode\u003Ederived.snowplow_unified_views\u003C/code\u003E).\u003C/li\u003E\u003Cli\u003EExtracted insights into user acquisition, engagement, and retention.\u003C/li\u003E\u003Cli\u003EAssessed content performance and user interaction patterns.\u003C/li\u003E\u003Cli\u003ELeveraged Snowflake Notebooks for collaborative data exploration.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003EAt the end of this guide, you've successfully set up a near real-time data ingestion pipeline using Snowplow and Snowflake and deployed the Unified Digital dbt package for streamlined analytics. This powerful combination allows your business to harness real-time insights and make data-driven decisions efficiently.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EConfigured the Snowflake Streaming Loader for real-time data ingestion.\u003C/li\u003E\u003Cli\u003EDeployed Unified Data package in Snowflake using Snowplow BDP.\u003C/li\u003E\u003Cli\u003ECustomized data models to fit specific business needs.\u003C/li\u003E\u003Cli\u003EExplored the results.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/snowplow/snowplow\"\u003ESnowplow GitHub Repository\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowplow.io/\"\u003ESnowplow Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-overview\"\u003ESnowflake Snowpipe Streaming Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/content/dam/snowflake-site/developers/2024/10/Next-Gen-Digital-Analytics-with-Snowplow.pdf\"\u003EDownload Reference Architecture\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://medium.com/snowflake/snowflake-hybrid-connected-native-apps-featuring-snowplow-e704314bcc90\"\u003ERead the Blog\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://youtu.be/mk2LrJ6JikM?list=TLGGuTqHXnTLVtcyNTA5MjAyNQ\"\u003EWatch the Demo\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EHappy Streaming and Analyzing!\u003C/p\u003E"],"title":"Snowplow Digital Analytics with Snowflake","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"author: Snowplow\r\nid: snowplow-web-analytics\r\ncategories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/solution-center/certification/partner-solution, snowflake-site:taxonomy/solution-center/includes/architecture, snowflake-site:taxonomy/product/analytics\r\nlanguage: en\r\nsummary: Set up real-time event tracking and analytics using Snowplow and Snowflake\r\nenvironments: web\r\nstatus :Published\r\nfeedback link: https://github.com/Snowflake-Labs/sfguides/issues\r\n## Overview\r\n\r\n### Overview\r\n\r\nIn this guide, you'll learn how to set up real-time event tracking and analytics, configure the Snowflake Streaming Loader for immediate data availability, and deploy Snowplow’s Unified Digital dbt package for simplified data modeling and consumption. By the end of this tutorial, you'll have a fully functioning analytics pipeline, enabling you to make data-driven decisions swiftly and accurately.\r\n\r\n### What You Will Build\r\n\r\n- A pipeline for behavioral data from the source to the analysis\r\n- A quick analysis of that behavioral data\r\n\r\n### What You Will Learn\r\n\r\n- How to set up Snowplow's tracking for event data collection\r\n- How to configure the Snowflake Streaming Loader to ingest data in near real-time\r\n- How to deploy Unified Digital dbt package in Snowflake\r\n\r\n### Prerequisites\r\n\r\n- [Snowplow Behavioral Data Platform (BDP) subscription](https://snowplow.io/)\r\n- Basic knowledge of SQL and Snowflake\r\n- Access to Snowflake account\r\n\r\n\r\n## Set up Tracking \r\n\r\nWith Snowplow, tracking capabilities are virtually limitless, offering a wide range of possibilities. \r\n\r\nBasic tracking functionalities can be easily integrated into your app by following [this guide](https://docs.snowplow.io/docs/recipes/recipe-basic-tracking-design/), while more advanced features can be unlocked by utilizing Snowplow’s full suite of products.\r\n\r\n\r\n### Example Web Tracking Implementation with Data Products and Automatic Code Generation\r\n\r\nHere's a summary of the steps to implement a basic web tracking setup using Snowplow’s Data Products and advanced Snowplow features:\r\n\r\n1. **Create a Source Application**: \r\n   Define your web app's tracking source and configure the necessary application contexts.\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/source_application.png)\r\n2. **Set up Base Data Product**: \r\n   Use the Base Web Data Product template to track standard events like page views and clicks in your app.\r\n   ![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/base_data_product.png)\r\n3. **Initialize the Tracker**: \r\n   Install [Snowplow's browser tracker](https://docs.snowplow.io/docs/collecting-data/collecting-from-own-applications/javascript-trackers/web-tracker/quick-start-guide/#quick-start-basic), configure it to automatically send page views and page pings, and track user interactions.\r\n4. **Verify Event Data**: \r\n   Use the [Snowplow Inspector browser extension](https://docs.snowplow.io/docs/testing-debugging/snowplow-inspector/overview/) to confirm that page views, clicks, and other tracked events are being recorded.\r\n   ![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/inspector.png)\r\n5. **Create Custom Data Structures**: \r\n   You can define custom data structures (e.g., \"new_game\" and \"start_game\") to track specific website or app interactions and utilize Snowtype to generate code for custom event tracking. More details about that in the full documentation below.\r\n\r\n[For a complete setup guide/recipe, visit Snowplow's official documentation.](https://docs.snowplow.io/docs/recipes/recipe-data-products-base-tracking/)\r\n\r\n\r\n## Configure Snowflake Streaming Loader\r\n\r\nIn this section, you'll learn how to set up the Snowflake Streaming Loader to ingest Snowplow event data into Snowflake in near real-time. By the end of this step, you'll have a streaming pipeline that delivers enriched event data with minimal latency.\r\n\r\n### Introduction to Snowflake's Snowpipe Streaming\r\n\r\n**Snowpipe Streaming** is Snowflake's real-time data ingestion service, which allows for the immediate availability of data as it's ingested. This capability is crucial for businesses that rely on up-to-the-minute data to make timely, informed decisions.\r\n\r\n### Snowflake Streaming Loader\r\n\r\nThe [Snowflake Streaming Loader](https://docs.snowplow.io/docs/pipeline-components-and-applications/loaders-storage-targets/snowflake-streaming-loader/) is an advanced data integration solution that continuously captures and delivers real-time information to Snowflake, enabling organizations to access up-to-the-minute insights and drive informed decision-making.\r\n\r\n#### Benefits of using Snowflake Streaming Loader\r\n\r\n- **Low Latency**: Data becomes available in Snowflake within seconds, enabling near real-time analytics.\r\n- **Cost-Efficiency**: Eliminates the need for expensive infrastructure like EMR clusters or large compute instances.\r\n- **Immediate Availability**: As soon as data is generated, it's ready for querying.\r\n\r\n#### Step 1: Go to Destinations\r\n\r\nGo to your Snowplow BDP console, under infra in destinations, and select the Snowflake ( Streaming Option )\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/bdp_dests.png)\r\n\r\n#### Step 2: Set Up the Configuration\r\n\r\nComplete your details\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/snowflake_streaming_1.png)\r\n\r\nand then generate and run the script to set up everything that will be needed in Snowflake\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/snowflake_streaming_2.png)\r\n\r\n#### Step 3: Complete destination set up\r\n\r\nClick the “Test and complete destination set up” button and reach out to Snowplow Support (support@snowplow.io) to finish the process\r\n\r\n## Deploy Unified Digital dbt Package\r\n\r\nIn this section, we'll explore how to deploy the Unified Digital dbt package in Snowflake to transform raw event data into modeled data and actionable insights. We will use the raw data that we previously ingested in Snowflake using the streaming loader\r\n\r\n### Prerequisites\r\n\r\n- Access to Snowplow BDP.\r\n- A Snowflake account with permissions to create databases, schemas, and roles.\r\n- Admin rights in Snowplow BDP to set up connections and models.\r\n\r\n### Understanding Unified Digital dbt Package\r\nThe unified data package is essential for consistent analytics across different data sources and platforms. It standardizes the structure and schema of your data, making it easier to query and derive insights.\r\n\r\n#### Why it's useful\r\n\r\n- **Consistency**: Ensures that all data adheres to the same structure.\r\n- **Simplified Consumption**: Makes it easier for analysts and data scientists to work with the data.\r\n- **Scalability**: Simplifies the addition of new data sources or event types.\r\n\r\n### Deployment Steps\r\n\r\nWe'll use Snowplow BDP to run the standard data models in Snowflake.\r\n\r\n#### Step 1: Create a Warehouse Connection\r\n\r\n1. **Navigate to Data Models**: Log in to Snowplow BDP Console and click on the **Data Models** tab.\r\n2. **Set Up New Connection**:\r\n   - Click on **Set up new connection**.\r\n   - Fill in the required Snowflake connection details:\r\n     - **Hostname**\r\n     - **Database Name**\r\n     - **Warehouse**\r\n     - **User**\r\n     - **Role** (specific to data modeling)\r\n   - **Note**: Use a role with permissions tailored for data modeling activities.\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/model_connection.png)\r\n\r\n\u003E aside negative\r\n\u003E \r\n\u003E If you've already set up a warehouse connection for loading data, you will still need to create a new connection specifically for data modeling. This is required since data models often require different users, roles, and permissions to manage how data is accessed and processed.\r\n\r\n\r\n#### Step 2: Create a Data Model\r\n\r\n1. **Add Data Model**: Click on **Add data model**.\r\n2. **Select the Unified Digital Model**:\r\n   - Choose **Unified Digital** from the list of standard models.\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/available_models.png)\r\n3. **Assign Warehouse Connection**:\r\n   - Link the data model to the warehouse connection you just created.\r\n4. **Specify Model Owners**:\r\n   - Add team members who should receive alerts in case of model run issues.\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/model_last_screen.png)\r\n\r\n\r\n#### Step 3: Adjust Data Models Configuration\r\n\r\nWe can go to the dedicated screen per model and configure the model as we want\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowplow-web-analytics/unified_model_view.png)\r\n\r\nSteps:\r\n\r\n1. **Check the Data Models Page**:\r\n   - Navigate to the **Data Models** page.\r\n2. **Schedule the Model**:\r\n   - Set the frequency for the data model to run (e.g., hourly, daily).\r\n3. **Alerts**:\r\n   - Owners will receive email notifications if a model run fails.\r\n4.  **Customize Based on Business Needs**:\r\n   - Ability to configure variables.\r\n\r\n### Tips on Customizing Models\r\n\r\n- **Adjust Metrics**: Modify the model to include metrics that are specific to your business needs.\r\n- **Add Modules**: Snowplow offers additional modules for Consent Reporting, Core Web Vitals, etc.\r\n- **User Mapping**: Utilize the User Mapping Module for advanced session stitching.\r\n\r\n\u003E aside positive\r\n\u003E \r\n\u003E **Gains**: Deploying the Unified Digital Model simplifies your data landscape. With standardized tables and fields, your team can focus on generating insights rather than spending time and engineering resources wrangling data. Visit [Unified documentation](https://docs.snowplow.io/docs/modeling-your-data/modeling-your-data-with-dbt/dbt-models/dbt-unified-data-model/) for further customization \r\n\r\n## Explore Modeled with Snowflake Notebooks\r\n\r\nIn this section, we'll explore your processed behavioral derived data using Snowflake Notebooks in depth. We'll focus on the derived tables created by the Unified Digital package, such as `derived.snowplow_unified_users`, `derived.snowplow_unified_sessions`, and `derived.snowplow_unified_views`. This hands-on session will guide you through querying these tables to extract valuable insights into user behavior, session metrics, and content performance.\r\n\r\n### Exploring Derived Tables in Snowflake\r\n\r\nDerived tables are the result of transforming raw event data into structured, analytical datasets. They aggregate and organize data to provide meaningful insights without the complexity of raw event logs.\r\n\r\n- **`derived.snowplow_unified_users`**: Aggregated user-level data.\r\n- **`derived.snowplow_unified_sessions`**: Data summarizing user sessions.\r\n- **`derived.snowplow_unified_views`**: Information about page or screen views.\r\n\r\n#### Step 1: Accessing the Derived Tables\r\n\r\n1. **Open Snowflake Notebooks**: Log in to your Snowflake account and navigate to the **Notebooks** section.\r\n2. **Create a New Notebook**: Click on **Create Notebook** and select the database and schema where your derived tables are stored (e.g., `snowplow.derived`).\r\n3. **List Available Tables**:\r\n\r\n   ```sql\r\n   SHOW TABLES IN SCHEMA snowplow.derived;\r\n   ```\r\n\r\n   This command lists all the derived tables available for analysis.\r\n\r\n#### Step 2: Analyzing User-Level Data\r\n\r\nLet's start by exploring the `derived.snowplow_unified_users` table to gain insights into your user base.\r\n\r\n**Query 1: View Sample User Data**\r\n\r\n```sql\r\nSELECT * FROM derived.snowplow_unified_users LIMIT 5;\r\n```\r\n\r\n- **Purpose**: Get acquainted with the structure and fields of the user data.\r\n- **Analysis**: Review the sample data to understand available user attributes.\r\n\r\n**Query 2: User Acquisition Over Time**\r\n\r\n```sql\r\nSELECT\r\n  DATE_TRUNC('day', START_TSTAMP) AS first_session_date,\r\n  COUNT(*) AS new_users\r\nFROM\r\n  derived.snowplow_unified_users\r\nGROUP BY\r\n  first_session_date\r\nORDER BY\r\n  first_session_date;\r\n```\r\n\r\n- **Purpose**: Observe trends in new user sign-ups.\r\n- **Visualization**: Create a line chart to visualize user acquisition over time.\r\n\r\n**Query 3: Top Engaged Users**\r\n\r\n```sql\r\n\r\nSELECT\r\n  USER_ID,\r\n  SESSIONS,\r\n  ENGAGED_TIME_IN_S\r\nFROM derived.snowplow_unified_users\r\nORDER BY\r\n  ENGAGED_TIME_IN_S DESC\r\nLIMIT 10;\r\n```\r\n\r\n- **Purpose**: Identify users with the highest engagement.\r\n- **Analysis**: These users may be valuable for feedback or loyalty programs.\r\n\r\n#### Step 3: Examining Session Data\r\n\r\nNext, we'll delve into the `derived.snowplow_unified_sessions` table to understand user interaction patterns.\r\n\r\n**Query 1: Average Session Duration**\r\n\r\n```sql\r\nSELECT\r\n  AVG(engaged_time_in_s) AS average_session_duration\r\nFROM\r\n  derived.snowplow_unified_sessions;\r\n```\r\n\r\n- **Purpose**: Determine the typical length of user sessions.\r\n- **Analysis**: A longer average session duration may indicate higher user engagement.\r\n\r\n**Query 2: Sessions Over the Last 7 Days**\r\n\r\n```sql\r\nSELECT\r\n  DATE_TRUNC('day', START_TSTAMP) AS session_date,\r\n  COUNT(*) AS session_count\r\nFROM\r\n  derived.snowplow_unified_sessions\r\nWHERE\r\n  START_TSTAMP \u003E= DATEADD('day', -7, CURRENT_DATE)\r\nGROUP BY\r\n  session_date\r\nORDER BY\r\n  session_date;\r\n```\r\n\r\n- **Purpose**: Analyze daily session activity for the past week.\r\n- **Visualization**: Plot a bar chart to highlight trends or spikes in user sessions.\r\n\r\n**Query 3: Session Counts by User**\r\n\r\n```sql\r\nSELECT\r\n  user_id,\r\n  COUNT(*) AS session_count\r\nFROM derived.snowplow_unified_sessions\r\nGROUP BY\r\n  user_id\r\nORDER BY\r\n  session_count DESC\r\nLIMIT 10;\r\n```\r\n\r\n- **Purpose**: Find users with the most sessions.\r\n- **Analysis**: Frequent visitors are key to understanding user loyalty.\r\n\r\n#### Step 4: Investigating Page and Screen Views\r\n\r\nNow, let's explore the `derived.snowplow_unified_views` table to assess content performance.\r\n\r\n**Query 1: Most Viewed Pages**\r\n\r\n```sql\r\nSELECT\r\n  page_urlpath,\r\n  COUNT(*) AS view_count\r\nFROM derived.snowplow_unified_views\r\nGROUP BY\r\n  page_urlpath\r\nORDER BY\r\n  view_count DESC\r\nLIMIT 10;\r\n```\r\n\r\n- **Purpose**: Identify the most popular pages on your site.\r\n- **Analysis**: Use this information to optimize high-traffic pages.\r\n\r\n**Query 2: Average Time Spent per Page**\r\n\r\n```sql\r\nSELECT\r\n  page_urlpath,\r\n  AVG(ENGAGED_TIME_IN_S) AS average_time_spent\r\nFROM derived.snowplow_unified_views\r\nGROUP BY page_urlpath\r\nORDER BY average_time_spent DESC\r\nLIMIT 10;\r\n```\r\n\r\n- **Purpose**: Discover which pages retain user attention the longest.\r\n- **Analysis**: Pages with higher engagement may serve as models for other content.\r\n\r\n**Query 3: Page Views by Device Type**\r\n\r\n```sql\r\nSELECT\r\n  DEVICE_CATEGORY,\r\n  COUNT(*) AS view_count\r\nFROM derived.snowplow_unified_views\r\nGROUP BY DEVICE_CATEGORY\r\nORDER BY view_count DESC;\r\n```\r\n\r\n- **Purpose**: Understand the distribution of device types accessing your content.\r\n- **Analysis**: Optimize user experience for the most commonly used devices.\r\n\r\n#### Step 5: Understanding Geographic Distribution\r\n\r\nAnalyzing geographic data helps tailor content and marketing strategies to your audience locations.\r\n\r\n**Query 1: Sessions by Country**\r\n\r\n```sql\r\nSELECT\r\n  FIRST_GEO_COUNTRY,\r\n  COUNT(*) AS session_count\r\nFROM derived.snowplow_unified_sessions\r\nGROUP BY FIRST_GEO_COUNTRY\r\nORDER BY session_count DESC;\r\n```\r\n\r\n- **Purpose**: Identify key markets based on user sessions.\r\n- **Visualization**: Create a map visualization to see the geographic spread.\r\n\r\n**Query 2: New Users by Region**\r\n\r\n```sql\r\nSELECT\r\n  FIRST_GEO_REGION_NAME,\r\n  COUNT(*) AS new_user_count\r\nFROM derived.snowplow_unified_users\r\nGROUP BY FIRST_GEO_REGION_NAME\r\nORDER BY new_user_count DESC;\r\n```\r\n\r\n- **Purpose**: Understand where new users are signing up.\r\n- **Analysis**: Assess the effectiveness of regional marketing efforts.\r\n\r\n#### Step 6: Segmenting Users for Deeper Insights\r\n\r\nUser segmentation allows for personalized marketing and improved user experiences.\r\n\r\n**Query 1: High-Value User Segment**\r\n\r\n```sql\r\nSELECT\r\n  user_id,\r\n  ENGAGED_TIME_IN_S\r\nFROM derived.snowplow_unified_users\r\nWHERE\r\n  ENGAGED_TIME_IN_S \u003E (\r\n    SELECT AVG(ENGAGED_TIME_IN_S) FROM derived.snowplow_unified_users\r\n  )\r\nORDER BY  ENGAGED_TIME_IN_S DESC;\r\n```\r\n\r\n- **Purpose**: Identify users who are more engaged than average.\r\n- **Analysis**: Target these users with special offers or exclusive content.\r\n\r\n#### Step 7: Collaborating with Snowflake Notebooks\r\n\r\nMaximize the value of your analysis by collaborating within your team.\r\n\r\n- **Sharing Notebooks**: Click the **Share** button to invite colleagues to view or edit your notebook.\r\n- **Commenting**: Use inline comments to discuss specific queries or results.\r\n- **Version Control**: Keep track of changes to maintain the integrity of your analyses.\r\n\r\n### Bringing It All Together\r\n\r\nBy systematically exploring the derived tables, you've uncovered:\r\n\r\n- **User Acquisition Trends**: Understanding how and when users join your platform.\r\n- **Engagement Metrics**: Identifying your most active users and sessions.\r\n- **Content Performance**: Recognizing which pages and content types resonate with your audience.\r\n- **Geographical Insights**: Tailoring strategies to regions with the highest impact.\r\n- **User Segmentation**: Personalizing experiences for different user groups.\r\n\r\n### Next Steps and Advanced Analyses\r\n\r\nWith a solid understanding of your data, consider:\r\n\r\n- **Building Custom Dashboards**: Create tailored views for stakeholders.\r\n- **Implementing Machine Learning Models**: Predict user behavior and personalize content.\r\n- **Conducting A/B Testing Analyses**: Evaluate the effectiveness of changes to your platform.\r\n\r\nBy focusing on the derived tables, you've efficiently navigated through high-level insights, enabling strategic decision-making without getting frustrated by raw data complexity. Continue to explore and customize your analyses to align with your business objectives.\r\n\r\n\r\n### What You Learned\r\n\r\n- Navigated and queried key derived tables (`derived.users`, `derived.snowplow_unified_sessions`, `derived.snowplow_unified_views`).\r\n- Extracted insights into user acquisition, engagement, and retention.\r\n- Assessed content performance and user interaction patterns.\r\n- Leveraged Snowflake Notebooks for collaborative data exploration.\r\n\r\n## Conclusion and Resources\r\n\r\n### Conclusion\r\nAt the end of this guide, you've successfully set up a near real-time data ingestion pipeline using Snowplow and Snowflake and deployed the Unified Digital dbt package for streamlined analytics. This powerful combination allows your business to harness real-time insights and make data-driven decisions efficiently.\r\n\r\n### What You Learned\r\n\r\n- Configured the Snowflake Streaming Loader for real-time data ingestion.\r\n- Deployed Unified Data package in Snowflake using Snowplow BDP.\r\n- Customized data models to fit specific business needs.\r\n- Explored the results.\r\n\r\n### Related Resources\r\n\r\n- [Snowplow GitHub Repository](https://github.com/snowplow/snowplow)\r\n- [Snowplow Documentation](https://docs.snowplow.io/)\r\n- [Snowflake Snowpipe Streaming Documentation](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-overview)\r\n- [Download Reference Architecture](/content/dam/snowflake-site/developers/2024/10/Next-Gen-Digital-Analytics-with-Snowplow.pdf)\r\n- [Read the Blog](https://medium.com/snowflake/snowflake-hybrid-connected-native-apps-featuring-snowplow-e704314bcc90)\r\n- [Watch the Demo](https://youtu.be/mk2LrJ6JikM?list=TLGGuTqHXnTLVtcyNTA5MjAyNQ)\r\n\r\nHappy Streaming and Analyzing!","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-79505b655e","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-820caa64d9",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-03fac6d508","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-03-04",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-d5e60f5f8e","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. It may be out of date with current Snowflake 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