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You'll work with a realistic Smart Fleet IoT Analytics use case that encompasses streaming semi-structured data, JSON log analysis, time-series analytics with nanosecond precision, and geospatial intelligence.\u003C/p\u003E\n","\u003Cp\u003EApache Iceberg V3 tables in Snowflake support the VARIANT data type, enabling efficient storage and querying of semi-structured data while maintaining the open table format's interoperability benefits. Combined with Snowflake's enterprise features for governance, AI/ML, and business continuity, you can build a truly modern lakehouse architecture.\u003C/p\u003E\n","\u003Ch3\u003EUse Case: Smart Fleet IoT Analytics\u003C/h3\u003E\n","\u003Cp\u003EYou'll build an analytics platform for a fleet management company that:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EStreams real-time vehicle telemetry\u003C/strong\u003E - Speed, location, engine diagnostics, fuel consumption as semi-structured VARIANT data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIngests maintenance logs\u003C/strong\u003E - JSON-formatted diagnostic reports and service records from cloud storage\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnalyzes time-series sensor data\u003C/strong\u003E - High-precision timestamp data for trend analysis and forecasting\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPerforms geospatial analytics\u003C/strong\u003E - Route optimization, geofencing, location-based insights\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPowers AI agents\u003C/strong\u003E - Natural language querying of fleet analytics via Snowflake CoWork\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECreating and managing Iceberg V3 tables with Snowflake Horizon Catalog\u003C/li\u003E\u003Cli\u003EStreaming semi-structured data into VARIANT columns in Iceberg tables using Snowpipe Streaming\u003C/li\u003E\u003Cli\u003EIngesting data from web APIs and batch loading JSON logs\u003C/li\u003E\u003Cli\u003EBuilding declarative transformation pipelines with Dynamic Iceberg Tables, powered by row lineage\u003C/li\u003E\u003Cli\u003EApplying enterprise governance: PII detection, masking policies, data quality\u003C/li\u003E\u003Cli\u003EPerforming time-series analysis, forecasting, and geospatial analytics with Iceberg v3 data types\u003C/li\u003E\u003Cli\u003EEnabling AI agents on Iceberg data with Snowflake CoWork\u003C/li\u003E\u003Cli\u003ESetting up cross-region replication for business continuity on v3 Iceberg tables\u003C/li\u003E\u003Cli\u003EQuerying Iceberg tables from Apache Spark via Iceberg REST API with vended storage credentials and Horizon fine-grained access controls\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E6+ Iceberg V3 tables\u003C/strong\u003E covering streaming events, logs, time-series, and geospatial data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDynamic Iceberg Tables\u003C/strong\u003E for automated incremental transformations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStreaming pipeline\u003C/strong\u003E using Snowpipe Streaming with Python SDK\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGovernance framework\u003C/strong\u003E with masking policies and data quality functions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EML forecasting model\u003C/strong\u003E for predictive maintenance\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake CoWork agent\u003C/strong\u003E for natural language analytics\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESpark notebooks\u003C/strong\u003E demonstrating interoperability with enforced access controls\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake Requirements:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role access\u003C/li\u003E\u003Cli\u003EStandard Edition or higher (Enterprise features noted where applicable)\u003C/li\u003E\u003Cli\u003EAbility to create databases, schemas, warehouses, and Iceberg tables\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERequired Tools:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation\"\u003ESnowflake CLI\u003C/a\u003E installed with a \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/specify-credentials\"\u003Econfigured connection\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EPython 3.9+ with pip\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake CLI Connection\u003C/strong\u003E: Before running the setup, configure a connection named \u003Ccode\u003Edefault\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow connection add --connection-name default --account <your-account> --user <your-user>\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor Snowflake-managed Iceberg storage (default in this lab):\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENo customer-owned bucket or external volume is required. Snowflake stores Iceberg files using the reserved volume \u003Ccode\u003ESNOWFLAKE_MANAGED\u003C/code\u003E; see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage\"\u003ESnowflake storage for Apache Iceberg tables\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EThis storage option is available on accounts hosted on \u003Cstrong\u003EAWS\u003C/strong\u003E or \u003Cstrong\u003EAzure\u003C/strong\u003E (not Google Cloud).\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOptional — customer-managed object storage:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIf you set \u003Ccode\u003EUSE_SNOWFLAKE_STORAGE=false\u003C/code\u003E in \u003Ccode\u003Econfig.env\u003C/code\u003E, you need AWS S3, Google Cloud Storage, or Azure Storage (Blob, ADLS Gen2, or OneLake) and the appropriate IAM roles or service principals.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor Spark Interoperability Section:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EConda package manager installed (for isolated PySpark environment)\u003C/li\u003E\u003Cli\u003EApache Spark 4.0+ (required for VARIANT support in Iceberg V3)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: At the time of writing, Spark does not support nanosecond timestamps or geospatial data types in Iceberg tables.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EObjects Created in This Guide\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EObject Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EName\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDatabase\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFLEET_ANALYTICS_DB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EContains all demo objects\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWarehouse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFLEET_ANALYTICS_WH\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompute for all operations\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIceberg table storage\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake-managed (\u003Ccode\u003ESNOWFLAKE_MANAGED\u003C/code\u003E)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDefault: Snowflake stores Iceberg files; no external volume object. Optional: customer volume \u003Ccode\u003EFLEET_ICEBERG_VOL\u003C/code\u003E if \u003Ccode\u003EUSE_SNOWFLAKE_STORAGE=false\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIceberg Table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVEHICLE_TELEMETRY_STREAM\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReal-time streaming events (VARIANT)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIceberg Table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMAINTENANCE_LOGS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBatch-loaded JSON logs (VARIANT)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIceberg Table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESENSOR_READINGS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETime-series sensor data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIceberg Table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVEHICLE_LOCATIONS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGeospatial vehicle positions\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIceberg Table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVEHICLE_REGISTRY\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVehicle master data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIceberg Table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAPI_WEATHER_DATA\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWeather data from public API (VARIANT)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDynamic Table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETELEMETRY_ENRICHED\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EJoined and enriched telemetry\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDynamic Table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDAILY_FLEET_SUMMARY\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAggregated fleet analytics\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMasking Policy\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPII_MASK\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMasks sensitive driver information\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData Metric Function\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECHECK_VALID_COORDINATES\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EValidates geospatial data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStage\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELOGS_STAGE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInternal stage for JSON files\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExternal Access Integration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOPEN_METEO_ACCESS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAllows notebook API calls\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENetwork Policy (optional)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFLEET_STREAMING_POLICY\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAllows streaming script connections\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Agent\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFLEET_ANALYTICS_AGENT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENatural language querying of fleet data\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEnvironment Setup\u003C/h2\u003E\n","\u003Cp\u003EThis section guides you through setting up your environment. The setup is designed to be as simple as possible: clone the repo, configure a few variables, and run the setup script.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Download the Assets\u003C/h3\u003E\n","\u003Cp\u003EDownload or clone the guide assets from the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/iceberg-v3-tables-comprehensive-guide/assets\"\u003Eassets folder\u003C/a\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Clone the sfquickstarts repo and navigate to the assets\ngit clone https://github.com/Snowflake-Labs/sfquickstarts.git\ncd sfquickstarts/site/sfguides/src/iceberg-v3-tables-comprehensive-guide/assets\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Configure Variables\u003C/h3\u003E\n","\u003Cp\u003EEdit the \u003Ccode\u003Econfig.env\u003C/code\u003E file in the repository root:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Required: Snowflake Account Information\nSNOWFLAKE_ACCOUNT="your_account_identifier"\nSNOWFLAKE_USER="your_username"\nSNOWFLAKE_ROLE="ACCOUNTADMIN"\nSNOWFLAKE_WAREHOUSE="FLEET_ANALYTICS_WH"\nSNOWFLAKE_DATABASE="FLEET_ANALYTICS_DB"\n\n# Iceberg storage — default is Snowflake-managed (no bucket or external volume).\n# See: https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage\nUSE_SNOWFLAKE_STORAGE=true\n\n# --- Only if USE_SNOWFLAKE_STORAGE=false (customer-managed storage) ---\n# OPTION A: Use an existing external volume\nUSE_EXISTING_VOLUME=false\nEXISTING_VOLUME_NAME=""\n\n# OPTION B: Create a new external volume named FLEET_ICEBERG_VOL\nSTORAGE_PROVIDER="S3"  # Options: S3, GCS, AZURE\n\n# --- AWS S3 ---\nS3_BUCKET_NAME="your-bucket-name"\nS3_PATH="iceberg/fleet-analytics/"\nAWS_ROLE_ARN=""  # Your IAM role ARN (see AWS S3 Setup below)\n\n# --- Google Cloud Storage ---\nGCS_BUCKET_NAME="your-bucket-name"\nGCS_PATH="iceberg/fleet-analytics/"\n\n# --- Azure Storage (Blob, ADLS Gen2, or OneLake) ---\n# See: https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-external-volume-azure\nAZURE_TENANT_ID="your-tenant-id"\nAZURE_STORAGE_ACCOUNT="your-storage-account"\nAZURE_CONTAINER="your-container"\nAZURE_PATH="iceberg/fleet-analytics/"\n# For OneLake, set USE_ONELAKE=true and provide workspace/lakehouse GUIDs\nUSE_ONELAKE=false\nONELAKE_WORKSPACE_GUID=""\nONELAKE_LAKEHOUSE_GUID=""\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Run Setup Script\u003C/h3\u003E\n","\u003Cp\u003EThe setup script will create all necessary Snowflake objects.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Make the script executable\nchmod +x setup.sh\n\n# Run the setup\n./setup.sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat the setup script does:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreates the database, schema, and warehouse\u003C/li\u003E\u003Cli\u003ESets Iceberg storage to Snowflake-managed by default (\u003Ccode\u003ESNOWFLAKE_MANAGED\u003C/code\u003E), or creates/uses a customer external volume when \u003Ccode\u003EUSE_SNOWFLAKE_STORAGE=false\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECreates all Iceberg V3 tables with appropriate schemas\u003C/li\u003E\u003Cli\u003ECreates Dynamic Iceberg Tables for transformation pipelines\u003C/li\u003E\u003Cli\u003ECreates governance objects (masking policies, data quality functions) - \u003Cem\u003EEnterprise edition only\u003C/em\u003E\u003C/li\u003E\u003Cli\u003EUploads sample JSON log files to the internal stage\u003C/li\u003E\u003Cli\u003ECreates the Snowflake CoWork agent configuration\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 4: Import the Snowflake Notebook\u003C/h3\u003E\n","\u003Cp\u003EImport the pre-populated notebook into Snowflake to follow along with the live demo:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESign in to \u003Ca href=\"https://app.snowflake.com\"\u003ESnowsight\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EIn the navigation menu, select \u003Cstrong\u003EProjects » Notebooks\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick the down arrow next to \u003Cstrong\u003E+ Notebook\u003C/strong\u003E and select \u003Cstrong\u003EImport .ipynb file\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ENavigate to the cloned repository and select \u003Ccode\u003Eassets/fleet_analytics_notebook.ipynb\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EIn the import dialog:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ENotebook location\u003C/strong\u003E: Select \u003Ccode\u003EFLEET_ANALYTICS_DB\u003C/code\u003E and \u003Ccode\u003ERAW\u003C/code\u003E schema\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EQuery warehouse\u003C/strong\u003E: Select \u003Ccode\u003EFLEET_ANALYTICS_WH\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate\u003C/strong\u003E to import the notebook\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: The notebook is pre-configured with SQL and Python cells that should import with the correct cell types.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAfter importing\u003C/strong\u003E, enable external API access for the weather data section:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003ENotebook settings\u003C/strong\u003E (⋮ icon, top right)\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EExternal access\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EToggle ON \u003Cstrong\u003EOPEN_METEO_ACCESS\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EFor more details on creating notebooks, see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-create#create-a-new-notebook\"\u003ECreate a notebook\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EExternal volume setup (customer-managed storage only)\u003C/h3\u003E\n","\u003Cp\u003EWith the default \u003Ccode\u003EUSE_SNOWFLAKE_STORAGE=true\u003C/code\u003E, Iceberg tables use \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage\"\u003ESnowflake storage for Apache Iceberg tables\u003C/a\u003E (\u003Ccode\u003EEXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\u003C/code\u003E). You can skip this entire subsection.\u003C/p\u003E\n","\u003Cp\u003EIf you set \u003Ccode\u003EUSE_SNOWFLAKE_STORAGE=false\u003C/code\u003E, Iceberg tables use a \u003Cstrong\u003Ecustomer-managed external volume\u003C/strong\u003E pointing to your object storage. Configure \u003Ccode\u003Econfig.env\u003C/code\u003E (existing volume or new \u003Ccode\u003EFLEET_ICEBERG_VOL\u003C/code\u003E via \u003Ccode\u003ESTORAGE_PROVIDER\u003C/code\u003E) and complete provider-specific setup before running \u003Ccode\u003Esetup.sh\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EYou can also create external volumes from Snowflake's web UI by navigating to \u003Cstrong\u003ECatalog > External data > External volumes tab\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/create_external_volume.png?v=8cbd55b6\" alt=\"Create External Volume\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAWS S3 Setup:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate an IAM policy granting Snowflake access to your S3 bucket:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n    "Version": "2012-10-17",\n    "Statement": [\n        {\n            "Effect": "Allow",\n            "Action": [\n                "s3:GetObject",\n                "s3:GetObjectVersion",\n                "s3:PutObject",\n                "s3:DeleteObject",\n                "s3:ListBucket",\n                "s3:GetBucketLocation"\n            ],\n            "Resource": [\n                "arn:aws:s3:::your-bucket-name/*",\n                "arn:aws:s3:::your-bucket-name"\n            ]\n        }\n    ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003ECreate an IAM role with this policy and update the trust relationship after running \u003Ccode\u003EDESCRIBE EXTERNAL VOLUME\u003C/code\u003E to get the Snowflake AWS IAM user ARN.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGCS Setup:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate a Cloud Storage service account\u003C/li\u003E\u003Cli\u003EGrant \u003Ccode\u003Estorage.objectAdmin\u003C/code\u003E role on your bucket\u003C/li\u003E\u003Cli\u003EAfter running \u003Ccode\u003EDESCRIBE EXTERNAL VOLUME\u003C/code\u003E, grant the Snowflake service account access\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAzure Setup:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate an Azure AD application registration\u003C/li\u003E\u003Cli\u003EGrant \u003Ccode\u003EStorage Blob Data Contributor\u003C/code\u003E role on your container\u003C/li\u003E\u003Cli\u003EConfigure the application for Snowflake access\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe setup script will provide specific instructions for your chosen provider.\u003C/p\u003E\n","\u003Ch3\u003EVerify Setup\u003C/h3\u003E\n","\u003Cp\u003EAfter running the setup script, verify everything was created:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Connect to Snowflake and run:\nUSE DATABASE FLEET_ANALYTICS_DB;\nSHOW ICEBERG TABLES;\nSHOW DYNAMIC TABLES;\nSHOW MASKING POLICIES;\nSHOW DATA METRIC FUNCTIONS;\nLIST @LOGS_STAGE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see all the Iceberg tables, dynamic tables, and governance objects listed.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStreaming and Ingest\u003C/h2\u003E\n","\u003Cp\u003EThis section demonstrates various methods Snowflake supports for loading semi-structured data into Iceberg V3 tables with the VARIANT data type.\u003C/p\u003E\n","\u003Ch3\u003ESnowpipe Streaming: Real-Time Vehicle Telemetry\u003C/h3\u003E\n","\u003Cp\u003ESnowpipe Streaming enables low-latency ingestion of streaming data directly into Iceberg tables. The high-performance architecture provides sub-second latency for getting data queryable in Snowflake.\u003C/p\u003E\n","\u003Ch4\u003EReview the Streaming Target Table\u003C/h4\u003E\n","\u003Cp\u003EThe setup script created an Iceberg table designed to receive streaming telemetry:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE FLEET_ANALYTICS_DB;\n\n-- View the table structure\nDESCRIBE TABLE VEHICLE_TELEMETRY_STREAM;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Confirm the table exists but is empty\nSELECT COUNT(*) FROM VEHICLE_TELEMETRY_STREAM;\n-- Returns: 0\n\n-- View the table properties showing it's an Iceberg V3 table with VARIANT column\nSELECT \n    GET_DDL('TABLE', 'VEHICLE_TELEMETRY_STREAM');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou'll see a VARIANT column called \u003Ccode\u003ETELEMETRY_DATA\u003C/code\u003E designed to hold the flexible schema vehicle telemetry payload.\u003C/p\u003E\n","\u003Ch4\u003EStart the Streaming Simulation\u003C/h4\u003E\n","\u003Cp\u003EOpen a terminal and navigate to the cloned repository:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd iceberg-v3-tables-comprehensive-guide/assets\n\n# Activate the Python environment (created by setup.sh)\nsource iceberg_v3_demo_venv/bin/activate\n\n# Start the streaming simulation (runs for 5 minutes max)\npython stream_telemetry.py\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETroubleshooting Connection Issues\u003C/strong\u003E: If you're on a VPN or corporate network and get connection errors, you may need a network policy to allow inbound connections. Set \u003Ccode\u003EENABLE_NETWORK_POLICY=true\u003C/code\u003E in \u003Ccode\u003Econfig.env\u003C/code\u003E and re-run setup, or manually run \u003Ccode\u003E08_network_policy.sql\u003C/code\u003E in Snowsight, then apply the policy to your user:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER USER <your_username> SET NETWORK_POLICY = FLEET_STREAMING_POLICY;\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe Python script uses the Snowpipe Streaming SDK to simulate vehicle telemetry events:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Example of what the streaming script sends\n{\n    "vehicle_id": "VH-2847",\n    "event_timestamp": "2026-02-03T14:32:15.847293847",  # Nanosecond precision\n    "telemetry_data": {\n        "location": {"lat": 37.7749, "lon": -122.4194},\n        "speed_mph": 65.3,\n        "heading": 285,\n        "engine": {\n            "rpm": 3200,\n            "temperature_f": 195,\n            "oil_pressure_psi": 42,\n            "fuel_level_pct": 67.5\n        },\n        "diagnostics": {\n            "check_engine": false,\n            "tire_pressure_warning": false,\n            "codes": []\n        },\n        "driver_behavior": {\n            "hard_acceleration_count": 2,\n            "hard_brake_count": 1,\n            "sharp_turn_count": 0\n        }\n    }\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EObserve Data Flowing In\u003C/h4\u003E\n","\u003Cp\u003EWhile the streaming script runs, watch data appear in Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check current count of streaming events\nSELECT COUNT(*) AS event_count FROM VEHICLE_TELEMETRY_STREAM;\n\n-- View the latest streaming events with VARIANT extraction\nSELECT \n    VEHICLE_ID,\n    EVENT_TIMESTAMP,\n    TELEMETRY_DATA:location:lat::FLOAT AS latitude,\n    TELEMETRY_DATA:location:lon::FLOAT AS longitude,\n    TELEMETRY_DATA:speed_mph::FLOAT AS speed_mph,\n    TELEMETRY_DATA:engine:temperature_f::INT AS engine_temp,\n    TELEMETRY_DATA:engine:fuel_level_pct::FLOAT AS fuel_level\nFROM VEHICLE_TELEMETRY_STREAM\nORDER BY EVENT_TIMESTAMP DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe streaming data is immediately queryable with full SQL capabilities on the VARIANT column!\u003C/p\u003E\n","\u003Ch3\u003EPull from a Web API: Weather Data\u003C/h3\u003E\n","\u003Cp\u003EInstead of multi-step file-based ingestion, Snowflake can connect directly to web APIs and ingest semi-structured responses into Iceberg tables with VARIANT.\u003C/p\u003E\n","\u003Ch4\u003EEnable External Access in Your Notebook\u003C/h4\u003E\n","\u003Cp\u003ESnowflake Notebooks require an \u003Cstrong\u003EExternal Access Integration\u003C/strong\u003E to call external APIs. The setup script created an integration for the Open-Meteo weather API.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBefore running the Python cell:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn your Snowflake Notebook, click the \u003Cstrong\u003ENotebook settings\u003C/strong\u003E (⋮ icon, top right)\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EExternal access\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EToggle ON \u003Cstrong\u003EOPEN_METEO_ACCESS\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: If you don't see \u003Ccode\u003EOPEN_METEO_ACCESS\u003C/code\u003E, run this SQL first:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE NETWORK RULE FLEET_ANALYTICS_DB.RAW.OPEN_METEO_NETWORK_RULE\n    MODE = EGRESS TYPE = HOST_PORT VALUE_LIST = ('api.open-meteo.com:443');\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION OPEN_METEO_ACCESS\n    ALLOWED_NETWORK_RULES = (FLEET_ANALYTICS_DB.RAW.OPEN_METEO_NETWORK_RULE) ENABLED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EView the Target Table\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- The table was created during setup\nDESCRIBE TABLE API_WEATHER_DATA;\n\n-- Confirm it's empty\nSELECT COUNT(*) AS current_count FROM API_WEATHER_DATA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ECreate and Run the API Ingestion Function\u003C/h4\u003E\n","\u003Cp\u003EIn your Snowflake Notebook (or worksheet), create a Python function to fetch weather data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# This is pre-populated in the Snowflake Notebook\nimport snowflake.snowpark as snowpark\nfrom snowflake.snowpark.functions import col, parse_json, current_timestamp\nimport requests\n\ndef fetch_weather_data(session: snowpark.Session, cities: list) -> str:\n    """\n    Fetches weather data from Open-Meteo API and loads into Iceberg table.\n    Open-Meteo is a free, open-source weather API requiring no API key.\n    """\n    \n    weather_records = []\n    \n    for city in cities:\n        # Using Open-Meteo free API\n        url = f"https://api.open-meteo.com/v1/forecast"\n        params = {\n            "latitude": city["lat"],\n            "longitude": city["lon"],\n            "current": "temperature_2m,wind_speed_10m,precipitation",\n            "hourly": "temperature_2m,precipitation_probability"\n        }\n        \n        response = requests.get(url, params=params)\n        if response.status_code == 200:\n            weather_records.append({\n                "city_name": city["name"],\n                "latitude": city["lat"],\n                "longitude": city["lon"],\n                "api_response": response.text,\n                "fetched_at": datetime.now().isoformat()\n            })\n    \n    # Create DataFrame and write to Iceberg table\n    df = session.create_dataframe(weather_records)\n    df = df.with_column("WEATHER_DATA", parse_json(col("API_RESPONSE")))\n    df.select(\n        col("CITY_NAME"),\n        col("LATITUDE"),\n        col("LONGITUDE"), \n        col("WEATHER_DATA"),\n        current_timestamp().alias("INGESTED_AT")\n    ).write.mode("append").save_as_table("API_WEATHER_DATA")\n    \n    return f"Loaded weather data for {len(weather_records)} cities"\n\n# Define cities for fleet operations\nfleet_cities = [\n    {"name": "San Francisco", "lat": 37.7749, "lon": -122.4194},\n    {"name": "Los Angeles", "lat": 34.0522, "lon": -118.2437},\n    {"name": "Seattle", "lat": 47.6062, "lon": -122.3321},\n    {"name": "Denver", "lat": 39.7392, "lon": -104.9903},\n    {"name": "Chicago", "lat": 41.8781, "lon": -87.6298}\n]\n\n# Execute the function\nresult = fetch_weather_data(session, fleet_cities)\nprint(result)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EQuery the Weather Data\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- View the loaded weather data\nSELECT \n    CITY_NAME,\n    WEATHER_DATA:current:temperature_2m::FLOAT AS current_temp_c,\n    WEATHER_DATA:current:wind_speed_10m::FLOAT AS wind_speed_kmh,\n    WEATHER_DATA:current:precipitation::FLOAT AS precipitation_mm,\n    INGESTED_AT\nFROM API_WEATHER_DATA;\n\n-- Extract hourly forecast for a city\nSELECT \n    CITY_NAME,\n    f.value::FLOAT AS hourly_temp\nFROM API_WEATHER_DATA,\nLATERAL FLATTEN(input => WEATHER_DATA:hourly:temperature_2m) f\nWHERE CITY_NAME = 'San Francisco'\nLIMIT 24;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip\u003C/strong\u003E: This ingestion pattern can be orchestrated natively in Snowflake using \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro\"\u003ETasks\u003C/a\u003E or \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-serverless\"\u003EServerless Tasks\u003C/a\u003E for scheduled API pulls.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EBatch Ingest JSON Logs\u003C/h3\u003E\n","\u003Cp\u003ESnowflake handles batch or continuous ingestion of semi-structured data stored in files, such as JSON diagnostic logs.\u003C/p\u003E\n","\u003Ch4\u003EView the Staged Files\u003C/h4\u003E\n","\u003Cp\u003EThe setup script uploaded 10 sample JSON files mimicking maintenance diagnostic logs:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- List the staged JSON files\nLIST @LOGS_STAGE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see files like:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Emaintenance_log_001.json\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Emaintenance_log_002.json\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E... through \u003Ccode\u003Emaintenance_log_010.json\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ELoad JSON Logs into Iceberg Table\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Load JSON files into Iceberg table\nCOPY INTO MAINTENANCE_LOGS (LOG_ID, VEHICLE_ID, LOG_TIMESTAMP, LOG_DATA, SOURCE_FILE)\nFROM (\n    SELECT \n        $1:log_id::VARCHAR,\n        $1:vehicle_id::VARCHAR,\n        $1:log_timestamp::TIMESTAMP_NTZ,\n        $1,\n        METADATA$FILENAME\n    FROM @LOGS_STAGE\n)\nFILE_FORMAT = (TYPE = 'JSON' STRIP_OUTER_ARRAY = TRUE)\nPATTERN = '.*maintenance_log.*\\.json';\n\n-- View the loaded maintenance logs with VARIANT data\nSELECT \n    LOG_ID,\n    VEHICLE_ID,\n    LOG_TIMESTAMP,\n    LOG_DATA:event_type::STRING AS event_type,\n    LOG_DATA:severity::STRING AS severity,\n    LOG_DATA:description::STRING AS description,\n    LOG_DATA:total_cost::FLOAT AS total_cost\nFROM MAINTENANCE_LOGS\nORDER BY LOG_TIMESTAMP DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETransformation\u003C/h2\u003E\n","\u003Cp\u003EThis section demonstrates how Snowflake powers transformation pipelines on Iceberg tables using Dynamic Tables. Dynamic Tables provide a declarative approach to data transformation—you define what the output should look like, and Snowflake handles the incremental refresh automatically.\u003C/p\u003E\n","\u003Cp\u003ERow lineage is used under the hood of Dynamic Iceberg V3 Tables to power change data capture (CDC) and incremental processing.\u003C/p\u003E\n","\u003Ch3\u003EDynamic Iceberg Tables Overview\u003C/h3\u003E\n","\u003Cp\u003EThe setup script created several Dynamic Iceberg Tables that form a transformation pipeline:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- View all dynamic tables in the pipeline\nSHOW DYNAMIC TABLES IN DATABASE FLEET_ANALYTICS_DB;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThese tables automatically refresh based on their TARGET_LAG setting, processing only incremental changes from source tables.\u003C/p\u003E\n","\u003Ch4\u003EExplore the Pipeline in the UI\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003ETransformation\u003C/strong\u003E → \u003Cstrong\u003EDynamic Tables\u003C/strong\u003E in the left pane\u003C/li\u003E\u003Cli\u003EFilter by database: \u003Cstrong\u003EFLEET_ANALYTICS_DB\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick on any dynamic table (e.g., \u003Ccode\u003ETELEMETRY_ENRICHED\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EExplore the tabs:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EGraph\u003C/strong\u003E: Visualize the full pipeline and dependencies\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERefresh History\u003C/strong\u003E: See incremental refresh operations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDefinition\u003C/strong\u003E: Review the declarative SQL that defines the transformation\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EView the Dynamic Table Graph\u003C/h4\u003E\n","\u003Cp\u003EThe transformation pipeline looks like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/dit_graph.png?v=8cbd55b6\" alt=\"Dynamic Iceberg Table Graph\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EMonitor Incremental Refreshes\u003C/h4\u003E\n","\u003Cp\u003EAs streaming data flows in, watch the dynamic tables refresh:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/dit_incremental_refresh.png?v=8cbd55b6\" alt=\"Dynamic Iceberg Table Incremental Refresh\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EREFRESH_MODE = INCREMENTAL\u003C/code\u003E setting ensures only new or changed data is processed, making the pipeline highly efficient.\u003C/p\u003E\n","\u003Ch4\u003EQuery Transformed Data\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Query enriched telemetry with vehicle details\nSELECT \n    VEHICLE_ID,\n    MAKE,\n    MODEL,\n    DRIVER_NAME,\n    FLEET_REGION,\n    speed_mph,\n    engine_temp_f,\n    engine_health_status,\n    driving_behavior,\n    EVENT_TIMESTAMP\nFROM CURATED.TELEMETRY_ENRICHED\nWHERE engine_health_status != 'NORMAL' OR driving_behavior = 'AGGRESSIVE'\nORDER BY EVENT_TIMESTAMP DESC\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESecurity and Governance\u003C/h2\u003E\n","\u003Cp\u003EThis section highlights Horizon Catalog as the central, unified, interoperable catalog with enterprise-grade security features. All governance capabilities work seamlessly with Iceberg V3 tables.\u003C/p\u003E\n","\u003Ch3\u003EPII Detection with Classification\u003C/h3\u003E\n","\u003Cp\u003ESnowflake can automatically detect and classify sensitive data across your entire database, including Iceberg tables.\u003C/p\u003E\n","\u003Ch4\u003EClassify the Schema\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EData\u003C/strong\u003E → \u003Cstrong\u003EDatabases\u003C/strong\u003E → \u003Cstrong\u003EFLEET_ANALYTICS_DB\u003C/strong\u003E → \u003Cstrong\u003ERAW\u003C/strong\u003E (the schema)\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003Ethree dots menu\u003C/strong\u003E (⋮) in the top right corner\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EClassify and Tag Sensitive Data\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect all Iceberg tables in the schema\u003C/li\u003E\u003Cli\u003EToggle \u003Cstrong\u003EAutomatically tag data\u003C/strong\u003E ON\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EClassify and Tag Sensitive Data\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/classify_sensitive_data.png?v=8cbd55b6\" alt=\"Classify and Tag Sensitive Data\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EView Applied Tags\u003C/h4\u003E\n","\u003Cp\u003EAfter classification completes:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to any table (e.g., \u003Ccode\u003EVEHICLE_REGISTRY\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003EColumns\u003C/strong\u003E tab\u003C/li\u003E\u003Cli\u003ENotice tags applied to sensitive columns like \u003Ccode\u003EDRIVER_NAME\u003C/code\u003E, \u003Ccode\u003EDRIVER_EMAIL\u003C/code\u003E, \u003Ccode\u003EDRIVER_PHONE\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/classification_tag.png?v=8cbd55b6\" alt=\"Tagged Sensitive Data\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECommon tags that may be applied:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003ESEMANTIC_CATEGORY:NAME\u003C/code\u003E - Personal names\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESEMANTIC_CATEGORY:EMAIL\u003C/code\u003E - Email addresses\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESEMANTIC_CATEGORY:PHONE_NUMBER\u003C/code\u003E - Phone numbers\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESEMANTIC_CATEGORY:US_SSN\u003C/code\u003E - Social Security Numbers\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip\u003C/strong\u003E: You can also create \u003Ca href=\"https://docs.snowflake.com/en/user-guide/object-tagging/work#define-a-tag-that-will-automatically-propagate\"\u003Ecustom tags that automatically propagate\u003C/a\u003E to existing and new downstream objects.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EFine-Grained Access Control with Masking Policies\u003C/h3\u003E\n","\u003Cp\u003EThe setup script created masking policies to protect sensitive information. Now apply them to your Iceberg tables.\u003C/p\u003E\n","\u003Ch4\u003EView Created Masking Policies\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- View all masking policies in the database\nSHOW MASKING POLICIES IN DATABASE FLEET_ANALYTICS_DB;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EPII_MASK\u003C/code\u003E policy was created:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- View the policy definition\nSELECT GET_DDL('MASKING_POLICY', 'PII_MASK');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE MASKING POLICY PII_MASK AS (val STRING) \nRETURNS STRING ->\n    CASE\n        WHEN CURRENT_ROLE() IN ('FLEET_ADMIN', 'ACCOUNTADMIN') THEN val\n        WHEN CURRENT_ROLE() = 'FLEET_ANALYST' THEN \n            CONCAT(LEFT(val, 2), '****', RIGHT(val, 2))\n        ELSE '********'\n    END;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EApply Masking Policies via UI\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EData\u003C/strong\u003E → \u003Cstrong\u003EDatabases\u003C/strong\u003E → \u003Cstrong\u003EFLEET_ANALYTICS_DB\u003C/strong\u003E → \u003Cstrong\u003ERAW\u003C/strong\u003E → \u003Cstrong\u003EVEHICLE_REGISTRY\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003EColumns\u003C/strong\u003E tab\u003C/li\u003E\u003Cli\u003EFind the \u003Ccode\u003EDRIVER_NAME\u003C/code\u003E column\u003C/li\u003E\u003Cli\u003EIn the \u003Cstrong\u003EPolicies\u003C/strong\u003E column, click the \u003Cstrong\u003E+\u003C/strong\u003E button\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003EPII_NAME_MASK\u003C/code\u003E and click \u003Cstrong\u003EDone\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ERepeat for \u003Ccode\u003EDRIVER_EMAIL\u003C/code\u003E and \u003Ccode\u003EDRIVER_PHONE\u003C/code\u003E with \u003Ccode\u003EPII_EMAIL_MASK\u003C/code\u003E and \u003Ccode\u003EPII_PHONE_MASK\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/tag_mask.png?v=8cbd55b6\" alt=\"Mask Tagged Columns\"\u003E\u003C/p\u003E\n","\u003Ch4\u003ETest Masking Behavior\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- As ACCOUNTADMIN, see full PII values (unmasked)\nSELECT VEHICLE_ID, DRIVER_NAME, DRIVER_EMAIL, DRIVER_PHONE\nFROM VEHICLE_REGISTRY LIMIT 5;\n\n-- Create a test analyst role and grant it to the current user\nCREATE ROLE IF NOT EXISTS FLEET_ANALYST;\nGRANT USAGE ON DATABASE FLEET_ANALYTICS_DB TO ROLE FLEET_ANALYST;\nGRANT USAGE ON SCHEMA FLEET_ANALYTICS_DB.RAW TO ROLE FLEET_ANALYST;\nGRANT SELECT ON ALL TABLES IN SCHEMA FLEET_ANALYTICS_DB.RAW TO ROLE FLEET_ANALYST;\nGRANT USAGE ON WAREHOUSE FLEET_ANALYTICS_WH TO ROLE FLEET_ANALYST;\n\n-- Grant the role to the current user so we can switch to it\nSET MY_USER = CURRENT_USER();\nGRANT ROLE FLEET_ANALYST TO USER IDENTIFIER($MY_USER);\n\n-- Switch to analyst role and observe MASKED values\nUSE ROLE FLEET_ANALYST;\nSELECT VEHICLE_ID, DRIVER_NAME, DRIVER_EMAIL, DRIVER_PHONE\nFROM VEHICLE_REGISTRY LIMIT 5;\n-- Results show masked values: "Jo****hn", "jo****om"\n\n-- Switch back to ACCOUNTADMIN for remaining operations\nUSE ROLE ACCOUNTADMIN;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EData Quality Monitoring\u003C/h3\u003E\n","\u003Cp\u003EThe setup script created Data Metric Functions (DMFs) to monitor data quality on Iceberg tables.\u003C/p\u003E\n","\u003Ch4\u003EView Data Quality Setup\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Show data metric functions\nSHOW DATA METRIC FUNCTIONS IN DATABASE FLEET_ANALYTICS_DB;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExample DMF for validating geospatial coordinates:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DATA METRIC FUNCTION CHECK_VALID_COORDINATES(\n    arg_t TABLE(lat FLOAT, lon FLOAT)\n)\nRETURNS NUMBER\nAS\n$$\n    SELECT COUNT(*) \n    FROM arg_t\n    WHERE lat < -90 OR lat > 90 \n       OR lon < -180 OR lon > 180\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EReview Data Quality Results\u003C/h4\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: The Data Quality dashboard will initially be empty. DMFs run on a schedule (default: when triggered by Snowflake). To see results sooner, set a shorter schedule and insert new data to trigger the DMF.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ETo configure DMF scheduling and verify results:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Set DMFs to trigger when data changes\nALTER ICEBERG TABLE RAW.VEHICLE_LOCATIONS SET DATA_METRIC_SCHEDULE = 'TRIGGER_ON_CHANGES';\nALTER ICEBERG TABLE RAW.SENSOR_READINGS SET DATA_METRIC_SCHEDULE = 'TRIGGER_ON_CHANGES';\n\n-- Or set a specific schedule (e.g., every 5 minutes)\n-- ALTER ICEBERG TABLE RAW.VEHICLE_LOCATIONS SET DATA_METRIC_SCHEDULE = '5 MINUTE';\n\n-- Check which DMFs are attached to VEHICLE_LOCATIONS\nSELECT * FROM TABLE(INFORMATION_SCHEMA.DATA_METRIC_FUNCTION_REFERENCES(\n    REF_ENTITY_NAME => 'FLEET_ANALYTICS_DB.RAW.VEHICLE_LOCATIONS',\n    REF_ENTITY_DOMAIN => 'TABLE'\n));\n\n-- Insert a row to trigger the DMF (if using TRIGGER_ON_CHANGES)\nINSERT INTO RAW.VEHICLE_LOCATIONS (LOCATION_ID, VEHICLE_ID, LOCATION_TIMESTAMP, LATITUDE, LONGITUDE, LOCATION_POINT, SPEED_MPH, HEADING_DEGREES, FLEET_REGION)\nSELECT UUID_STRING(), VEHICLE_ID, CURRENT_TIMESTAMP(), LATITUDE, LONGITUDE, LOCATION_POINT, SPEED_MPH, HEADING_DEGREES, FLEET_REGION\nFROM RAW.VEHICLE_LOCATIONS LIMIT 1;\n\n-- Check DMF results (may take a minute to populate after data insert)\nSELECT \n    TABLE_NAME,\n    METRIC_NAME,\n    VALUE,\n    MEASUREMENT_TIME\nFROM SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_RESULTS\nWHERE TABLE_DATABASE = 'FLEET_ANALYTICS_DB'\nORDER BY MEASUREMENT_TIME DESC\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip\u003C/strong\u003E: Insert or modify data in the table to trigger the DMF if using \u003Ccode\u003ETRIGGER_ON_CHANGES\u003C/code\u003E. The Data Quality tab will populate after the next scheduled run.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ETo view the Data Quality dashboard:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EData\u003C/strong\u003E → \u003Cstrong\u003EDatabases\u003C/strong\u003E → \u003Cstrong\u003EFLEET_ANALYTICS_DB\u003C/strong\u003E → \u003Cstrong\u003ERAW\u003C/strong\u003E → \u003Cstrong\u003EVEHICLE_LOCATIONS\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick on the \u003Cstrong\u003EData Quality\u003C/strong\u003E tab\u003C/li\u003E\u003Cli\u003EAfter DMFs have run, you'll see:\n\u003Cul\u003E\u003Cli\u003EQuality check results over time\u003C/li\u003E\u003Cli\u003EFailed record counts\u003C/li\u003E\u003Cli\u003EData quality trends by dimension (Freshness, Volume, Accuracy, etc.)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EFor more details, see \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-data-quality-in-snowflake/#review\"\u003EGetting Started with Data Quality in Snowflake\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EData Lineage\u003C/h3\u003E\n","\u003Cp\u003ESnowflake automatically tracks lineage for all objects, including Iceberg tables and Dynamic Tables.\u003C/p\u003E\n","\u003Ch4\u003EView Lineage Graph\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EData\u003C/strong\u003E → \u003Cstrong\u003EDatabases\u003C/strong\u003E → \u003Cstrong\u003EFLEET_ANALYTICS_DB\u003C/strong\u003E → \u003Cstrong\u003ERAW\u003C/strong\u003E → \u003Cstrong\u003EMAINTENANCE_LOGS\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick on the \u003Cstrong\u003ELineage\u003C/strong\u003E tab\u003C/li\u003E\u003Cli\u003EExplore the lineage graph showing both upstream and downstream tables, including upstream lineage from an external telemetry messaging service\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe lineage graph is valuable for:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EImpact analysis\u003C/strong\u003E: Understanding what's affected when source data changes\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData quality investigation\u003C/strong\u003E: Tracing issues back to their source\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESecurity auditing\u003C/strong\u003E: Understanding data flow for sensitive information\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/lineage.png?v=8cbd55b6\" alt=\"Data Lineage\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAnalytics and AI\u003C/h2\u003E\n","\u003Cp\u003EThis section demonstrates Snowflake's analytical capabilities on Iceberg V3 tables, including semi-structured data querying, time-series analysis, geospatial functions, and AI agents.\u003C/p\u003E\n","\u003Ch3\u003ESemi-Structured Data Analytics\u003C/h3\u003E\n","\u003Cp\u003ESnowflake efficiently queries VARIANT data in Iceberg tables with automatic pruning and optimization.\u003C/p\u003E\n","\u003Ch4\u003EQuery Nested Variant Data\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Extract nested fields from VARIANT with dot notation\nSELECT \n    VEHICLE_ID,\n    TELEMETRY_DATA:speed_mph::FLOAT AS speed,\n    TELEMETRY_DATA:engine:rpm::INT AS engine_rpm,\n    TELEMETRY_DATA:engine:temperature_f::INT AS engine_temp,\n    TELEMETRY_DATA:diagnostics:check_engine::BOOLEAN AS check_engine_light,\n    TELEMETRY_DATA:driver_behavior:hard_brake_count::INT AS hard_brakes\nFROM RAW.VEHICLE_TELEMETRY_STREAM\nWHERE TELEMETRY_DATA:speed_mph::FLOAT > 50\nLIMIT 20;\n\n-- Aggregate on variant fields - find vehicles with check engine warnings\nSELECT \n    TELEMETRY_DATA:diagnostics:check_engine::BOOLEAN AS check_engine,\n    COUNT(*) AS event_count,\n    ROUND(AVG(TELEMETRY_DATA:speed_mph::FLOAT), 1) AS avg_speed,\n    COUNT(DISTINCT VEHICLE_ID) AS vehicle_count\nFROM RAW.VEHICLE_TELEMETRY_STREAM\nGROUP BY 1\nORDER BY event_count DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EObserve Query Pruning\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Run with profile to see pruning efficiency\nSELECT \n    VEHICLE_ID,\n    EVENT_TIMESTAMP,\n    TELEMETRY_DATA:speed_mph::FLOAT AS speed\nFROM VEHICLE_TELEMETRY_STREAM\nWHERE VEHICLE_ID = 'VH-1234'\n  AND TELEMETRY_DATA:engine:temperature_f::INT > 200;\n\n-- Check query profile in History tab to see partition pruning statistics\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETime-Series Analytics and Forecasting\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003ESENSOR_READINGS\u003C/code\u003E table contains high-precision time-series data for analysis.\u003C/p\u003E\n","\u003Ch4\u003EAS OF Join for Point-in-Time Analysis\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- AS OF join: Find closest sensor reading before each maintenance event\nSELECT \n    m.VEHICLE_ID,\n    m.LOG_TIMESTAMP AS maintenance_time,\n    m.LOG_DATA:event_type::STRING AS maintenance_type,\n    s.READING_TIMESTAMP AS closest_reading_time,\n    s.ENGINE_TEMP_F,\n    s.OIL_PRESSURE_PSI\nFROM RAW.MAINTENANCE_LOGS m\nASOF JOIN RAW.SENSOR_READINGS s\n    MATCH_CONDITION (m.LOG_TIMESTAMP >= s.READING_TIMESTAMP)\n    ON m.VEHICLE_ID = s.VEHICLE_ID\nWHERE m.LOG_DATA:severity::STRING IN ('CRITICAL', 'HIGH')\nORDER BY m.LOG_TIMESTAMP DESC\nLIMIT 15;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ETime-Series Aggregation with\u003C/h4\u003E\n","\u003Cp\u003EWindow functions enable powerful time-series analysis directly in SQL:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ERolling averages\u003C/strong\u003E to smooth out noise\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERunning totals\u003C/strong\u003E for cumulative metrics\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELag/Lead\u003C/strong\u003E for period-over-period comparisons\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Calculate rolling averages for engine temperature\nSELECT \n    VEHICLE_ID,\n    READING_TIMESTAMP,\n    ENGINE_TEMP_F,\n    AVG(ENGINE_TEMP_F) OVER (\n        PARTITION BY VEHICLE_ID \n        ORDER BY READING_TIMESTAMP \n        RANGE BETWEEN INTERVAL '1 HOUR' PRECEDING AND CURRENT ROW\n    ) AS rolling_avg_temp,\n    MAX(ENGINE_TEMP_F) OVER (\n        PARTITION BY VEHICLE_ID \n        ORDER BY READING_TIMESTAMP \n        RANGE BETWEEN INTERVAL '1 HOUR' PRECEDING AND CURRENT ROW\n    ) AS rolling_max_temp\nFROM RAW.SENSOR_READINGS\nORDER BY VEHICLE_ID, READING_TIMESTAMP\nLIMIT 50;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EML Forecasting for Predictive Maintenance\u003C/h4\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E⏱️ \u003Cstrong\u003ETraining Time\u003C/strong\u003E: Model training may take \u003Cstrong\u003E2-5 minutes\u003C/strong\u003E depending on data volume and warehouse size. To speed up training:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUse a \u003Cstrong\u003Elarger warehouse\u003C/strong\u003E (e.g., MEDIUM or LARGE) - training scales with compute\u003C/li\u003E\u003Cli\u003EReduce the number of time series by filtering to specific vehicles\u003C/li\u003E\u003Cli\u003ELimit historical data range (e.g., 14 days instead of 30)\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create a forecast model for fuel consumption\n-- For Iceberg tables, we must use SYSTEM$QUERY_REFERENCE instead of TABLE reference\nCREATE OR REPLACE SNOWFLAKE.ML.FORECAST fuel_consumption_forecast(\n    INPUT_DATA => SYSTEM$QUERY_REFERENCE('\n        SELECT \n            VEHICLE_ID,\n            READING_TIMESTAMP,\n            FUEL_CONSUMPTION_GPH\n        FROM RAW.SENSOR_READINGS\n        WHERE READING_TIMESTAMP > DATEADD(''day'', -30, CURRENT_TIMESTAMP())\n    '),\n    TIMESTAMP_COLNAME => 'READING_TIMESTAMP',\n    TARGET_COLNAME => 'FUEL_CONSUMPTION_GPH',\n    SERIES_COLNAME => 'VEHICLE_ID'\n);\n\n-- Generate 7-day forecast and save results\nCALL fuel_consumption_forecast!FORECAST(\n    FORECASTING_PERIODS => 168,  -- 7 days * 24 hours\n    CONFIG_OBJECT => {'prediction_interval': 0.95}\n);\n\n-- FORECAST: Generate a 7-day forecast (168 hours) and save to a table for visualization\nCALL fuel_consumption_forecast!FORECAST(\n    FORECASTING_PERIODS => 168,\n    CONFIG_OBJECT => {'prediction_interval': 0.95}\n);\n\n-- Save forecast results to a table for visualization\nCREATE OR REPLACE TEMPORARY TABLE FUEL_FORECAST_RESULTS AS\nSELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID()));\n\n-- Preview forecast results\nSELECT * FROM FUEL_FORECAST_RESULTS ORDER BY SERIES, TS LIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EVisualize Historical + Forecast Data\u003C/h4\u003E\n","\u003Cp\u003EUse Streamlit in a Snowflake Notebook to plot historical data alongside the forecast:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# 📈 Visualize Historical + Forecast Fuel Consumption\nimport streamlit as st\nimport pandas as pd\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\n\n# Get historical data (last 30 days for one vehicle)\nhistorical_df = session.sql("""\n    SELECT \n        READING_TIMESTAMP AS ts,\n        FUEL_CONSUMPTION_GPH AS value,\n        'Historical' AS data_type\n    FROM RAW.SENSOR_READINGS\n    WHERE VEHICLE_ID = (SELECT SERIES FROM FUEL_FORECAST_RESULTS LIMIT 1)\n      AND READING_TIMESTAMP > DATEADD('day', -30, CURRENT_TIMESTAMP())\n    ORDER BY READING_TIMESTAMP\n""").to_pandas()\n\n# Get forecast data (column names vary - check with DESCRIBE first if needed)\nforecast_df = session.sql("""\n    SELECT \n        TS AS ts,\n        FORECAST AS value,\n        'Forecast' AS data_type\n    FROM FUEL_FORECAST_RESULTS\n    WHERE SERIES = (SELECT SERIES FROM FUEL_FORECAST_RESULTS LIMIT 1)\n    ORDER BY TS\n""").to_pandas()\n\n# Get the vehicle ID for the title\nvehicle_id = session.sql("SELECT SERIES FROM FUEL_FORECAST_RESULTS LIMIT 1").collect()[0][0]\n\nst.subheader(f"📈 Fuel Consumption Forecast: {vehicle_id}")\nst.caption("Historical data (30 days) + 7-day forecast")\n\n# Normalize column names to lowercase\nhistorical_df.columns = historical_df.columns.str.lower()\nforecast_df.columns = forecast_df.columns.str.lower()\n\n# Combine for plotting\ncombined_df = pd.concat([historical_df, forecast_df], ignore_index=True)\ncombined_df['ts'] = pd.to_datetime(combined_df['ts'])\ncombined_df = combined_df.sort_values('ts')\n\n# Create the chart using Streamlit's native line chart\n# Pivot data for multi-line chart\nchart_data = combined_df.pivot_table(index='ts', columns='data_type', values='value', aggfunc='first')\nst.line_chart(chart_data, use_container_width=True)\n\n# Display metrics\ncol1, col2, col3 = st.columns(3)\ncol1.metric("Avg Historical (GPH)", f"{historical_df['value'].mean():.2f}")\ncol2.metric("Avg Forecast (GPH)", f"{forecast_df['value'].mean():.2f}")\nchange = ((forecast_df['value'].mean() - historical_df['value'].mean()) / historical_df['value'].mean()) * 100\ncol3.metric("Projected Change", f"{change:+.1f}%")\n\nst.caption("💡 The forecast helps identify vehicles likely to have higher fuel consumption, enabling proactive maintenance scheduling.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGeospatial Analytics\u003C/h3\u003E\n","\u003Cp\u003EH3 is Uber's hierarchical geospatial indexing system. Snowflake's H3 functions enable:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAggregating vehicles by geographic cell\u003C/li\u003E\u003Cli\u003EEfficient spatial joins and clustering\u003C/li\u003E\u003Cli\u003EMulti-resolution analysis (cells at different zoom levels)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EVEHICLE_LOCATIONS\u003C/code\u003E table contains GEOGRAPHY data for spatial analysis.\u003C/p\u003E\n","\u003Ch4\u003EBasic Geospatial Queries\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Calculate distance between consecutive positions\nSELECT \n    VEHICLE_ID,\n    LOCATION_TIMESTAMP,\n    LAG(LOCATION_POINT) OVER (\n        PARTITION BY VEHICLE_ID ORDER BY LOCATION_TIMESTAMP\n    ) AS prev_location,\n    LOCATION_POINT AS current_location,\n    ST_DISTANCE(\n        LOCATION_POINT,\n        LAG(LOCATION_POINT) OVER (PARTITION BY VEHICLE_ID ORDER BY LOCATION_TIMESTAMP)\n    ) / 1609.34 AS distance_miles  -- Convert meters to miles\nFROM VEHICLE_LOCATIONS\nORDER BY VEHICLE_ID, LOCATION_TIMESTAMP\nLIMIT 50;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EGeofencing Analysis\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Define a geofence (San Francisco downtown)\nSET sf_downtown = ST_MAKEPOLYGON(TO_GEOGRAPHY(\n    'LINESTRING(-122.42 37.78, -122.42 37.79, -122.40 37.79, -122.40 37.78, -122.42 37.78)'\n));\n\n-- Find vehicles inside the geofence\nSELECT \n    VEHICLE_ID,\n    LOCATION_TIMESTAMP,\n    ST_X(LOCATION_POINT) AS longitude,\n    ST_Y(LOCATION_POINT) AS latitude\nFROM VEHICLE_LOCATIONS\nWHERE ST_WITHIN(LOCATION_POINT, $sf_downtown)\nORDER BY LOCATION_TIMESTAMP DESC\nLIMIT 20;\n\n-- Geospatial: Aggregate vehicles by H3 cell (resolution 6)\nSELECT \n    H3_POINT_TO_CELL_STRING(LOCATION_POINT, 6) AS h3_cell,\n    FLEET_REGION,\n    COUNT(DISTINCT VEHICLE_ID) AS vehicle_count,\n    ROUND(AVG(SPEED_MPH), 1) AS avg_speed\nFROM RAW.VEHICLE_LOCATIONS\nWHERE LOCATION_TIMESTAMP > DATEADD('hour', -24, CURRENT_TIMESTAMP())\nGROUP BY 1, 2\nORDER BY vehicle_count DESC\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EInteractive Map Visualization with Streamlit\u003C/h4\u003E\n","\u003Cp\u003EIn the Snowflake Notebook, you can visualize the H3 geospatial data on an interactive map using Streamlit (built into Snowflake Notebooks):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Visualize fleet locations on an interactive map using Streamlit\nimport streamlit as st\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\n\n# Query location data with H3 cell centers (use ST_X/ST_Y for GEOGRAPHY type)\ndf = session.sql("""\n    SELECT \n        H3_POINT_TO_CELL_STRING(LOCATION_POINT, 6) AS h3_cell,\n        ST_X(H3_CELL_TO_POINT(H3_POINT_TO_CELL_STRING(LOCATION_POINT, 6))) AS longitude,\n        ST_Y(H3_CELL_TO_POINT(H3_POINT_TO_CELL_STRING(LOCATION_POINT, 6))) AS latitude,\n        FLEET_REGION,\n        COUNT(DISTINCT VEHICLE_ID) AS vehicle_count,\n        ROUND(AVG(SPEED_MPH), 1) AS avg_speed\n    FROM RAW.VEHICLE_LOCATIONS\n    WHERE LOCATION_TIMESTAMP > DATEADD('hour', -24, CURRENT_TIMESTAMP())\n    GROUP BY 1, 2, 3, 4\n    ORDER BY vehicle_count DESC\n    LIMIT 100\n""").to_pandas()\n\nst.subheader("🗺️ Fleet Vehicle Distribution")\nst.caption("Each point represents an H3 cell with vehicle activity in the last 24 hours")\n\n# Display metrics\ncol1, col2, col3 = st.columns(3)\ncol1.metric("Total H3 Cells", len(df))\ncol2.metric("Total Vehicles", int(df['VEHICLE_COUNT'].sum()))\ncol3.metric("Avg Speed", f"{df['AVG_SPEED'].mean():.1f} mph")\n\n# Interactive map visualization - point size scales with vehicle count\nst.map(df, latitude='LATITUDE', longitude='LONGITUDE', size='VEHICLE_COUNT')\n\n# Show data table\nst.dataframe(df[['FLEET_REGION', 'VEHICLE_COUNT', 'AVG_SPEED']].head(10))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis produces an interactive map showing vehicle distribution across the US, with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EPoints sized by vehicle count per H3 cell\u003C/li\u003E\u003Cli\u003ESummary metrics for cells, vehicles, and average speed\u003C/li\u003E\u003Cli\u003EData table with regional breakdown\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAI Agents with Snowflake CoWork\u003C/h3\u003E\n","\u003Cp\u003EThe setup script creates a Cortex Agent (\u003Ccode\u003EFLEET_ANALYTICS_AGENT\u003C/code\u003E) that enables natural language querying of your fleet data.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Cortex Agents require Enterprise Edition or higher.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003ERegion Availability and Cross-Region Inference\u003C/h4\u003E\n","\u003Cp\u003ECortex Agents and Cortex Analyst rely on LLMs that may not be available in all Snowflake regions. If you see an error like:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ENone of the preferred models are authorized or available in your region...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou have two options:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EEnable Cross-Region Inference\u003C/strong\u003E (recommended): This allows Snowflake to route AI requests to regions where models are available, while keeping your data secure.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Enable cross-region inference for your account\nUSE ROLE ACCOUNTADMIN;\nALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\u003Cstrong\u003EUse a supported region\u003C/strong\u003E: Deploy your Snowflake account in a region with native LLM support.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EFor more details, see:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst#region-availability\"\u003ECortex Analyst Region Availability\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cross-region-inference\"\u003ECross-Region Inference\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrivacy Note\u003C/strong\u003E: With cross-region inference, only the AI inference is routed cross-region. Your underlying data remains in your account's region and is protected by Snowflake's governance controls.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EAgent Configuration\u003C/h4\u003E\n","\u003Cp\u003EThe setup script creates the agent with a complete YAML specification including:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESample questions\u003C/strong\u003E to help users get started\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOrchestration instructions\u003C/strong\u003E explaining the data schema\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst tool\u003C/strong\u003E linked to a semantic model describing all tables\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe semantic model (\u003Ccode\u003Efleet_semantic_model.yaml\u003C/code\u003E) is uploaded to the stage and defines:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETable structures for SENSOR_READINGS, VEHICLE_TELEMETRY_STREAM, VEHICLE_LOCATIONS, etc.\u003C/li\u003E\u003Cli\u003EVARIANT column extraction patterns\u003C/li\u003E\u003Cli\u003EDimension and measure definitions\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: If the agent creation fails due to syntax issues, you can configure it manually through \u003Cstrong\u003ECortex AI\u003C/strong\u003E → \u003Cstrong\u003EAgents\u003C/strong\u003E → \u003Cstrong\u003EFLEET_ANALYTICS_AGENT\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EUsing the Fleet Analytics Agent\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003ECortex AI\u003C/strong\u003E → \u003Cstrong\u003EAgents\u003C/strong\u003E in the left sidebar\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EFLEET_ANALYTICS_AGENT\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EStart asking questions in natural language:\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExample queries:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"Which vehicles had the highest fuel consumption last week?"\u003C/li\u003E\u003Cli\u003E"Show me all critical maintenance events"\u003C/li\u003E\u003Cli\u003E"What's the average speed by fleet region?"\u003C/li\u003E\u003Cli\u003E"Find vehicles with check engine warnings"\u003C/li\u003E\u003Cli\u003E"Which drivers have the most hard braking events?"\u003C/li\u003E\u003Cli\u003E"Show vehicle health scores below 60"\u003C/li\u003E\u003Cli\u003E"How many vehicles are in each H3 cell in California?"\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe agent understands your Iceberg table schema including:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EVARIANT columns\u003C/strong\u003E: Extracts nested JSON fields using colon notation\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGEOGRAPHY columns\u003C/strong\u003E: Uses H3 and ST_* functions for geospatial analysis\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETime-series data\u003C/strong\u003E: Performs ASOF joins and window functions\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EAgent Configuration\u003C/h4\u003E\n","\u003Cp\u003EThe agent was created with access to these tables:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETable\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ERAW.VEHICLE_TELEMETRY_STREAM\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReal-time streaming telemetry (VARIANT)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ERAW.VEHICLE_LOCATIONS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGeospatial positions (GEOGRAPHY)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ERAW.SENSOR_READINGS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHigh-precision time-series sensor data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ERAW.MAINTENANCE_LOGS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMaintenance events (VARIANT)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ERAW.VEHICLE_REGISTRY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVehicle and driver master data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EANALYTICS.DAILY_FLEET_SUMMARY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDaily aggregated metrics\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EANALYTICS.VEHICLE_HEALTH_SCORE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECalculated health scores\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ETo view or modify the agent configuration:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- View agent details\nSHOW AGENTS IN SCHEMA FLEET_ANALYTICS_DB.RAW;\n\n-- Describe the agent\nDESCRIBE AGENT FLEET_ANALYTICS_DB.RAW.FLEET_ANALYTICS_AGENT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor more on building agents, see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence\"\u003ESnowflake CoWork documentation\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBusiness Continuity and Disaster Recovery\u003C/h2\u003E\n","\u003Cp\u003EThis section highlights Snowflake's enterprise-grade, out-of-the-box business continuity and disaster recovery capabilities that extend to Iceberg V3 tables.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Replication features require Business Critical Edition or higher.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EIceberg Table Replication Overview\u003C/h3\u003E\n","\u003Cp\u003ESnowflake supports replicating Iceberg tables across regions and clouds, ensuring your data lakehouse is protected against regional outages. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-replication\"\u003EIceberg Table Replication documentation\u003C/a\u003E for details.\u003C/p\u003E\n","\u003Ch3\u003ESetting Up Replication via UI\u003C/h3\u003E\n","\u003Cp\u003EFollow these steps to set up cross-region replication for your Iceberg tables:\u003C/p\u003E\n","\u003Ch4\u003EStep 1: Create a Replication Group\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAdmin\u003C/strong\u003E → \u003Cstrong\u003EAccounts\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick on the \u003Cstrong\u003EReplication\u003C/strong\u003E tab\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Group\u003C/strong\u003E (or \u003Cstrong\u003E+ Failover Group\u003C/strong\u003E for automatic failover)\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EStep 2: Select Target Account\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EChoose the target Snowflake account in a different cloud or region\u003C/li\u003E\u003Cli\u003EIf no secondary accounts exist, you'll need to create one first\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EStep 3: Configure Objects\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ESelect all objects to replicate:\n\u003Cul\u003E\u003Cli\u003EDatabases (includes all Iceberg tables)\u003C/li\u003E\u003Cli\u003EWarehouses (optional)\u003C/li\u003E\u003Cli\u003ERoles and privileges (recommended for consistent access)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EWith \u003Cstrong\u003ESnowflake-managed\u003C/strong\u003E Iceberg storage (\u003Ccode\u003ESNOWFLAKE_MANAGED\u003C/code\u003E, the lab default), you do not add customer external volumes for table files, and failover/replication groups can use \u003Ccode\u003EOBJECT_TYPES = DATABASES\u003C/code\u003E without listing external volumes. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage#replication\"\u003EReplication\u003C/a\u003E under \u003Cem\u003ESnowflake storage for Apache Iceberg tables\u003C/em\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 4: Enable and Monitor\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate\u003C/strong\u003E to enable replication\u003C/li\u003E\u003Cli\u003EMonitor replication status in the Replication tab\u003C/li\u003E\u003Cli\u003EView replication lag and sync history\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EVerify Replication\u003C/h3\u003E\n","\u003Cp\u003EIn the secondary account:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check that Iceberg tables exist\nUSE DATABASE FLEET_ANALYTICS_DB;\nSHOW ICEBERG TABLES;\n\n-- Query replicated data\nSELECT COUNT(*) FROM VEHICLE_TELEMETRY_STREAM;\nSELECT COUNT(*) FROM MAINTENANCE_LOGS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFailover Testing\u003C/h3\u003E\n","\u003Cp\u003EFor Failover Groups (automatic failover):\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the Replication tab, select your failover group\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EFailover\u003C/strong\u003E to initiate failover to secondary\u003C/li\u003E\u003Cli\u003EVerify applications can connect to secondary account\u003C/li\u003E\u003Cli\u003EUse \u003Cstrong\u003EFailback\u003C/strong\u003E when primary is restored\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EFor detailed steps, see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/account-replication-config#create-a-replication-or-failover-group-using-snowsight\"\u003ECreating Replication/Failover Groups via Snowsight\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInteroperability\u003C/h2\u003E\n","\u003Cp\u003EThis section demonstrates that external engines like Apache Spark can access Iceberg V3 tables managed by Snowflake. Horizon provides temporary, scoped storage credentials and enforces centralized row/column-level access controls for any engine.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites for Spark\u003C/h3\u003E\n","\u003Cp\u003EEnsure you have:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EConda installed\u003C/li\u003E\u003Cli\u003EThe repository cloned (\u003Ccode\u003Esfguide-iceberg-v3-comprehensive-guide\u003C/code\u003E)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EExternal engines still read Iceberg tables that use \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage\"\u003ESnowflake-managed storage\u003C/a\u003E through Horizon Catalog. The Spark notebook includes a \u003Cstrong\u003ECloud Storage Bundles\u003C/strong\u003E section—use the bundle that matches your account’s cloud if you adjust storage or see file I/O errors.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStart the Spark Environment\u003C/h3\u003E\n","\u003Cp\u003EThe setup script created a Conda environment with Spark 4.0+ and all necessary dependencies:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd sfquickstarts/site/sfguides/src/iceberg-v3-comprehensive-guide/assets\n\n# Activate the Spark environment\nconda activate fleet-spark\n\n# Start Jupyter notebook\njupyter notebook spark_iceberg_interop.ipynb\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESpark Notebook Walkthrough\u003C/h3\u003E\n","\u003Cp\u003EThe pre-configured Jupyter notebook contains all the code needed. Here's what you'll do:\u003C/p\u003E\n","\u003Ch4\u003ECell 1: Setup and Imports\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom pyspark.sql import SparkSession\nimport os\n\n# Load configuration (from the same config.env used during setup)\nfrom dotenv import load_dotenv\nload_dotenv('../config.env')\n\nSNOWFLAKE_ACCOUNT = os.getenv('SNOWFLAKE_ACCOUNT')\nSNOWFLAKE_USER = os.getenv('SNOWFLAKE_USER')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ECell 2: Create Spark Session with Horizon Catalog\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom pyspark.sql import SparkSession\n\n# Configuration\nSNOWFLAKE_PASSWORD = os.getenv('SNOWFLAKE_PASSWORD')\nSNOWFLAKE_ROLE = 'ACCOUNTADMIN'\nSNOWFLAKE_SCHEMA = 'RAW'\nSNOWFLAKE_WAREHOUSE = os.getenv('SNOWFLAKE_WAREHOUSE', 'FLEET_ANALYTICS_WH')\nSF_URL = f"{SNOWFLAKE_ACCOUNT_URL}.snowflakecomputing.com"\n\n# Versions - Note: Spark 3.5 required for Snowflake Connector masking support\nSNOWFLAKE_JDBC_VERSION = "3.24.0"\nSNOWFLAKE_SPARK_CONNECTOR_VERSION = "3.1.6"\n\n# Create Spark session with Iceberg and Snowflake catalog configuration\n# Note: \n# - driver.host and bindAddress ensure Spark uses localhost (avoids VPN issues)\n# - Using Spark 4.0 + Iceberg 1.10.1 for native VARIANT support\nspark = SparkSession.builder \\\n    .appName("Fleet Analytics - Iceberg V3 Interop") \\\n    .master("local[*]") \\\n    .config("spark.driver.host", "127.0.0.1") \\\n    .config("spark.driver.bindAddress", "127.0.0.1") \\\n    .config("spark.jars.packages", \n            f"org.apache.iceberg:iceberg-spark-runtime-4.0_{SCALA_VERSION}:{ICEBERG_VERSION},"\n            f"org.apache.iceberg:iceberg-aws-bundle:{ICEBERG_VERSION},"\n            f"net.snowflake:snowflake-jdbc:{SNOWFLAKE_JDBC_VERSION},"\n            f"net.snowflake:spark-snowflake_{SCALA_VERSION}:{SNOWFLAKE_SPARK_CONNECTOR_VERSION}") \\\n    .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \\\n    .config("spark.sql.catalog.horizon", "org.apache.iceberg.spark.SparkCatalog") \\\n    .config("spark.sql.catalog.horizon.type", "rest") \\\n    .config("spark.sql.catalog.horizon.uri", f"https://{SNOWFLAKE_ACCOUNT_URL}.snowflakecomputing.com/polaris/api/catalog") \\\n    .config("spark.sql.catalog.horizon.credential", SNOWFLAKE_ACCOUNTADMIN_TOKEN) \\\n    .config("spark.sql.catalog.horizon.warehouse", SNOWFLAKE_DATABASE) \\\n    .config("spark.sql.catalog.horizon.scope", f"session:role:{SNOWFLAKE_ROLE}") \\\n    .config("spark.sql.catalog.horizon.header.X-Iceberg-Access-Delegation","vended-credentials") \\\n    .config("spark.snowflake.sfURL", SF_URL) \\\n    .config("spark.snowflake.sfUser", os.getenv('SNOWFLAKE_USER')) \\\n    .config("spark.snowflake.sfPassword", SNOWFLAKE_FLEET_ANALYST_TOKEN) \\\n    .config("spark.snowflake.sfDatabase", SNOWFLAKE_DATABASE) \\\n    .config("spark.snowflake.sfSchema", SNOWFLAKE_SCHEMA) \\\n    .config("spark.snowflake.sfRole", SNOWFLAKE_ROLE) \\\n    .config("spark.snowflake.sfWarehouse", SNOWFLAKE_WAREHOUSE) \\\n    .config("spark.sql.iceberg.vectorization.enabled", "false") \\\n    .getOrCreate()\n\nprint("Spark session created successfully!")\nprint(f"Spark version: {spark.version}")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ECell 3: List Available Tables\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Show all tables visible to Spark\nspark.sql("SHOW TABLES IN horizon.RAW").show(truncate=False)\nspark.sql("SHOW TABLES IN horizon.CURATED").show(truncate=False)\nspark.sql("SHOW TABLES IN horizon.ANALYTICS").show(truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOutput:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+---------+------------------------+-----------+\n|namespace|tableName               |isTemporary|\n+---------+------------------------+-----------+\n|RAW      |API_WEATHER_DATA        |false      |\n|RAW      |MAINTENANCE_LOGS        |false      |\n|RAW      |SENSOR_READINGS         |false      |\n|RAW      |VEHICLE_LOCATIONS       |false      |\n|RAW      |VEHICLE_REGISTRY        |false      |\n|RAW      |VEHICLE_TELEMETRY_STREAM|false      |\n+---------+------------------------+-----------+\n\n+---------+--------------------+-----------+\n|namespace|tableName           |isTemporary|\n+---------+--------------------+-----------+\n|CURATED  |MAINTENANCE_ANALYSIS|false      |\n|CURATED  |TELEMETRY_ENRICHED  |false      |\n+---------+--------------------+-----------+\n\n+---------+--------------------+-----------+\n|namespace|tableName           |isTemporary|\n+---------+--------------------+-----------+\n|ANALYTICS|DAILY_FLEET_SUMMARY |false      |\n|ANALYTICS|VEHICLE_HEALTH_SCORE|false      |\n+---------+--------------------+-----------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote that Dynamic Iceberg Tables are also visible!\u003C/p\u003E\n","\u003Ch4\u003ECell 4: Describe Table with Variant Column\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# See variant column in Iceberg table\nspark.sql("DESCRIBE TABLE snowflake.RAW.VEHICLE_TELEMETRY_STREAM").show(truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOutput:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+-------------------+---------------+-------+\n|col_name           |data_type      |comment|\n+-------------------+---------------+-------+\n|VEHICLE_ID         |string         |null   |\n|EVENT_TIMESTAMP    |timestamp      |null   |\n|TELEMETRY_DATA     |variant        |null   |\n+-------------------+---------------+-------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ECell 5: Query Data with Variant\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Use Spark's variant_get() function to extract nested fields from VARIANT\n# This query succeeds, and no Snowflake compute is used, since there's no masking policy on the Iceberg table.\ndf = spark.sql(f"""\n    SELECT \n        VEHICLE_ID,\n        EVENT_TIMESTAMP,\n        variant_get(TELEMETRY_DATA, '$.speed_mph', 'float') AS speed_mph,\n        variant_get(TELEMETRY_DATA, '$.engine.temperature_f', 'int') AS engine_temp,\n        variant_get(TELEMETRY_DATA, '$.location.lat', 'float') AS latitude,\n        variant_get(TELEMETRY_DATA, '$.location.lon', 'float') AS longitude\n    FROM snowflake.RAW.VEHICLE_TELEMETRY_STREAM\n    WHERE variant_get(TELEMETRY_DATA, '$.speed_mph', 'float') > 60\n    LIMIT 10\n""")\ndf.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ECell 6: Demonstrate Access Control (Masking Enforcement)\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# To enforce masking policies from Spark, we need the Snowflake Connector for Spark\n# which routes queries through Snowflake for policy evaluation\n# See: https://docs.snowflake.com/en/user-guide/tables-iceberg-query-using-external-query-engine-snowflake-horizon-enforce-access-policies\n\n# Stop the previous Spark session first\nspark.stop()\nfrom pyspark.sql import SparkSession\n\n# Configuration\nSNOWFLAKE_PASSWORD = os.getenv('SNOWFLAKE_PASSWORD')\nSNOWFLAKE_ROLE = 'FLEET_ANALYST'\nSNOWFLAKE_SCHEMA = 'RAW'\nSNOWFLAKE_WAREHOUSE = os.getenv('SNOWFLAKE_WAREHOUSE', 'FLEET_ANALYTICS_WH')\nSF_URL = f"{SNOWFLAKE_ACCOUNT_URL}.snowflakecomputing.com"\n\n# Versions - Note: Spark 3.5 required for Snowflake Connector masking support\nSNOWFLAKE_JDBC_VERSION = "3.24.0"\nSNOWFLAKE_SPARK_CONNECTOR_VERSION = "3.1.6"\n\nspark_analyst = SparkSession.builder \\\n    .appName("Fleet Analytics - Analyst View") \\\n    .master("local[*]") \\\n    .config("spark.driver.host", "127.0.0.1") \\\n    .config("spark.driver.bindAddress", "127.0.0.1") \\\n    .config("spark.jars.packages", \n            f"org.apache.iceberg:iceberg-spark-runtime-4.0_{SCALA_VERSION}:{ICEBERG_VERSION},"\n            f"org.apache.iceberg:iceberg-aws-bundle:{ICEBERG_VERSION},"\n            f"net.snowflake:snowflake-jdbc:{SNOWFLAKE_JDBC_VERSION},"\n            f"net.snowflake:spark-snowflake_{SCALA_VERSION}:{SNOWFLAKE_SPARK_CONNECTOR_VERSION}") \\\n    .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \\\n    .config("spark.sql.defaultCatalog", "horizon") \\\n    .config("spark.sql.catalog.horizon", "org.apache.spark.sql.snowflake.catalog.SnowflakeFallbackCatalog") \\\n    .config("spark.sql.catalog.horizon.catalog-impl", "org.apache.iceberg.spark.SparkCatalog") \\\n    .config("spark.sql.catalog.horizon.type", "rest") \\\n    .config("spark.sql.catalog.horizon.uri", f"https://{SNOWFLAKE_ACCOUNT_URL}.snowflakecomputing.com/polaris/api/catalog") \\\n    .config("spark.sql.catalog.horizon.warehouse", SNOWFLAKE_DATABASE) \\\n    .config("spark.sql.catalog.horizon.scope", f"session:role:{SNOWFLAKE_ROLE}") \\\n    .config("spark.sql.catalog.horizon.credential", SNOWFLAKE_FLEET_ANALYST_TOKEN) \\\n    .config("spark.sql.catalog.horizon.io-impl", "org.apache.iceberg.aws.s3.S3FileIO") \\\n    .config("spark.sql.catalog.horizon.header.X-Iceberg-Access-Delegation", "vended-credentials") \\\n    .config("spark.snowflake.sfURL", SF_URL) \\\n    .config("spark.snowflake.sfUser", os.getenv('SNOWFLAKE_USER')) \\\n    .config("spark.snowflake.sfPassword", SNOWFLAKE_FLEET_ANALYST_TOKEN) \\\n    .config("spark.snowflake.sfDatabase", SNOWFLAKE_DATABASE) \\\n    .config("spark.snowflake.sfSchema", SNOWFLAKE_SCHEMA) \\\n    .config("spark.snowflake.sfRole", SNOWFLAKE_ROLE) \\\n    .config("spark.snowflake.sfWarehouse", SNOWFLAKE_WAREHOUSE) \\\n    .config("spark.sql.iceberg.vectorization.enabled", "false") \\\n    .getOrCreate()\n\nspark_analyst.sparkContext.setLogLevel("ERROR")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIceberg tables are still accessible via Iceberg REST API\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Espark_analyst.sql("SHOW TABLES in horizon.RAW").show(truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen reading an Iceberg table with a maskign policy, Snowflake returns masked results\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Espark_analyst.sql("""\n    SELECT\n        VEHICLE_ID,\n        MAKE,\n        MODEL,\n        YEAR,\n        LICENSE_PLATE,\n        DRIVER_NAME,\n        DRIVER_EMAIL,\n        DRIVER_PHONE,\n        FLEET_REGION\n    FROM horizon.RAW.VEHICLE_REGISTRY\n""").show(truncate=True)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/spark_masked.png?v=8cbd55b6\" alt=\"Interoperable Masking\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe masking policies defined in Snowflake are enforced even when accessing data through Spark!\u003C/p\u003E\n","\u003Cp\u003EFor more details on Spark integration, see:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-query-using-external-query-engine-snowflake-horizon\"\u003EQuery Iceberg Tables Using External Engines\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-query-using-external-query-engine-snowflake-horizon-enforce-access-policies\"\u003EEnforce Access Policies for External Engines\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003ETo remove all objects created by this guide:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\n-- Drop the database (this removes all tables, schemas, and objects)\nDROP DATABASE IF EXISTS FLEET_ANALYTICS_DB;\n\n-- Drop the external volume only if you used customer-managed storage (USE_SNOWFLAKE_STORAGE=false)\nDROP EXTERNAL VOLUME IF EXISTS FLEET_ICEBERG_VOL;\n\n-- Drop the warehouse\nDROP WAREHOUSE IF EXISTS FLEET_ANALYTICS_WH;\n\n-- Drop roles\nDROP ROLE IF EXISTS FLEET_ANALYST;\nDROP ROLE IF EXISTS FLEET_ENGINEER;\nDROP ROLE IF EXISTS FLEET_ADMIN;\n\n-- If you created replication groups, remove them\nDROP REPLICATION GROUP IF EXISTS FLEET_ANALYTICS_REPLICATION;\n\n-- Remove network policy from user (if applied)\nALTER USER <your_username> UNSET NETWORK_POLICY;\nDROP NETWORK POLICY IF EXISTS FLEET_STREAMING_POLICY;\n\n-- External access integration\nDROP INTEGRATION IF EXISTS OPEN_METEO_ACCESS;\n\nSELECT 'Uncomment the commands above to cleanup' AS NOTE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you used customer-managed cloud storage, you may also want to clean up the Iceberg data files in your bucket or container.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've built a comprehensive analytics platform using Snowflake Iceberg V3 tables, demonstrating the full power of Snowflake's modern lakehouse architecture.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EIceberg V3 Tables\u003C/strong\u003E: Created tables with VARIANT columns for semi-structured data, supporting flexible schemas\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStreaming Ingestion\u003C/strong\u003E: Used Snowpipe Streaming to ingest real-time vehicle telemetry with sub-second latency\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMulti-Source Ingestion\u003C/strong\u003E: Loaded data from web APIs and batch JSON files into Iceberg tables\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDeclarative Pipelines\u003C/strong\u003E: Built incremental transformation pipelines with Dynamic Iceberg Tables\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEnterprise Governance\u003C/strong\u003E: Applied PII detection, masking policies, and data quality monitoring\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdvanced Analytics\u003C/strong\u003E: Performed time-series analysis, forecasting, and geospatial queries\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAI Integration\u003C/strong\u003E: Connected Iceberg data to Snowflake CoWork for natural language analytics\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBusiness Continuity\u003C/strong\u003E: Configured cross-region replication for disaster recovery\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInteroperability\u003C/strong\u003E: Queried Iceberg tables from Spark with enforced access controls\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey Benefits of Iceberg V3 Tables in Snowflake\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EOpen Format\u003C/strong\u003E: Data stored in open Apache Iceberg format, accessible by any compatible engine\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVARIANT Support\u003C/strong\u003E: Full semi-structured data capabilities with V3 table format\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUnified Governance\u003C/strong\u003E: Single place to manage security, quality, and lineage across all engines\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIncremental Processing\u003C/strong\u003E: Dynamic Tables automatically handle incremental transformations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEnterprise Features\u003C/strong\u003E: Time travel, cloning, replication all work with Iceberg tables\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPerformance\u003C/strong\u003E: Snowflake's query optimization applies to Iceberg tables\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDocumentation:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage\"\u003ESnowflake storage for Apache Iceberg tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/iceberg/tables-iceberg-v3-specification-support\"\u003EIceberg V3 Support in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowpipe-streaming/snowpipe-streaming-high-performance-overview\"\u003ESnowpipe Streaming\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-create-iceberg\"\u003EDynamic Iceberg Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence\"\u003ESnowflake CoWork\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-replication\"\u003EIceberg Table Replication\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGuides:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/declarative-pipelines-using-dynamic-iceberg-tables/\"\u003EBuild Declarative Pipelines with Dynamic Iceberg Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-with-data-quality-in-snowflake/\"\u003EGetting Started with Data Quality in Snowflake\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAssets:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/iceberg-v3-tables-comprehensive-guide/assets\"\u003EGuide Assets Folder\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Enterprise Lakehouse Platform for Iceberg V3","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nThis comprehensive guide demonstrates how to build a complete data engineering, analytics, and AI platform using Snowflake's Iceberg V3 tables. You'll work with a realistic Smart Fleet IoT Analytics use case that encompasses streaming semi-structured data, JSON log analysis, time-series analytics with nanosecond precision, and geospatial intelligence.\n\nApache Iceberg V3 tables in Snowflake support the VARIANT data type, enabling efficient storage and querying of semi-structured data while maintaining the open table format's interoperability benefits. Combined with Snowflake's enterprise features for governance, AI/ML, and business continuity, you can build a truly modern lakehouse architecture.\n\n### Use Case: Smart Fleet IoT Analytics\n\nYou'll build an analytics platform for a fleet management company that:\n\n- **Streams real-time vehicle telemetry** - Speed, location, engine diagnostics, fuel consumption as semi-structured VARIANT data\n- **Ingests maintenance logs** - JSON-formatted diagnostic reports and service records from cloud storage\n- **Analyzes time-series sensor data** - High-precision timestamp data for trend analysis and forecasting\n- **Performs geospatial analytics** - Route optimization, geofencing, location-based insights\n- **Powers AI agents** - Natural language querying of fleet analytics via Snowflake CoWork\n\n### What You Will Learn\n\n- Creating and managing Iceberg V3 tables with Snowflake Horizon Catalog\n- Streaming semi-structured data into VARIANT columns in Iceberg tables using Snowpipe Streaming\n- Ingesting data from web APIs and batch loading JSON logs\n- Building declarative transformation pipelines with Dynamic Iceberg Tables, powered by row lineage\n- Applying enterprise governance: PII detection, masking policies, data quality\n- Performing time-series analysis, forecasting, and geospatial analytics with Iceberg v3 data types\n- Enabling AI agents on Iceberg data with Snowflake CoWork\n- Setting up cross-region replication for business continuity on v3 Iceberg tables\n- Querying Iceberg tables from Apache Spark via Iceberg REST API with vended storage credentials and Horizon fine-grained access controls\n\n### What You Will Build\n\n- **6+ Iceberg V3 tables** covering streaming events, logs, time-series, and geospatial data\n- **Dynamic Iceberg Tables** for automated incremental transformations\n- **Streaming pipeline** using Snowpipe Streaming with Python SDK\n- **Governance framework** with masking policies and data quality functions\n- **ML forecasting model** for predictive maintenance\n- **Snowflake CoWork agent** for natural language analytics\n- **Spark notebooks** demonstrating interoperability with enforced access controls\n\n### Prerequisites\n\n**Snowflake Requirements:**\n- Snowflake account with `ACCOUNTADMIN` role access\n- Standard Edition or higher (Enterprise features noted where applicable)\n- Ability to create databases, schemas, warehouses, and Iceberg tables\n\n**Required Tools:**\n- [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation) installed with a [configured connection](https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/specify-credentials)\n- Python 3.9+ with pip\n\n\u003E **Snowflake CLI Connection**: Before running the setup, configure a connection named `default`:\n\u003E ```bash\n\u003E snow connection add --connection-name default --account \u003Cyour-account\u003E --user \u003Cyour-user\u003E\n\u003E ```\n\n**For Snowflake-managed Iceberg storage (default in this lab):**\n- No customer-owned bucket or external volume is required. Snowflake stores Iceberg files using the reserved volume `SNOWFLAKE_MANAGED`; see [Snowflake storage for Apache Iceberg tables](https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage).\n- This storage option is available on accounts hosted on **AWS** or **Azure** (not Google Cloud).\n\n**Optional — customer-managed object storage:**\n- If you set `USE_SNOWFLAKE_STORAGE=false` in `config.env`, you need AWS S3, Google Cloud Storage, or Azure Storage (Blob, ADLS Gen2, or OneLake) and the appropriate IAM roles or service principals.\n\n**For Spark Interoperability Section:**\n- Conda package manager installed (for isolated PySpark environment)\n- Apache Spark 4.0+ (required for VARIANT support in Iceberg V3)\n\n\u003E **Note**: At the time of writing, Spark does not support nanosecond timestamps or geospatial data types in Iceberg tables.\n\n### Objects Created in This Guide\n\n| Object Type | Name | Purpose |\n|------------|------|---------|\n| Database | FLEET_ANALYTICS_DB | Contains all demo objects |\n| Warehouse | FLEET_ANALYTICS_WH | Compute for all operations |\n| Iceberg table storage | Snowflake-managed (`SNOWFLAKE_MANAGED`) | Default: Snowflake stores Iceberg files; no external volume object. Optional: customer volume `FLEET_ICEBERG_VOL` if `USE_SNOWFLAKE_STORAGE=false` |\n| Iceberg Table | VEHICLE_TELEMETRY_STREAM | Real-time streaming events (VARIANT) |\n| Iceberg Table | MAINTENANCE_LOGS | Batch-loaded JSON logs (VARIANT) |\n| Iceberg Table | SENSOR_READINGS | Time-series sensor data |\n| Iceberg Table | VEHICLE_LOCATIONS | Geospatial vehicle positions |\n| Iceberg Table | VEHICLE_REGISTRY | Vehicle master data |\n| Iceberg Table | API_WEATHER_DATA | Weather data from public API (VARIANT) |\n| Dynamic Table | TELEMETRY_ENRICHED | Joined and enriched telemetry |\n| Dynamic Table | DAILY_FLEET_SUMMARY | Aggregated fleet analytics |\n| Masking Policy | PII_MASK | Masks sensitive driver information |\n| Data Metric Function | CHECK_VALID_COORDINATES | Validates geospatial data |\n| Stage | LOGS_STAGE | Internal stage for JSON files |\n| External Access Integration | OPEN_METEO_ACCESS | Allows notebook API calls |\n| Network Policy (optional) | FLEET_STREAMING_POLICY | Allows streaming script connections |\n| Cortex Agent | FLEET_ANALYTICS_AGENT | Natural language querying of fleet data |\n\n\u003C!-- ------------------------ --\u003E\n## Environment Setup\n\nThis section guides you through setting up your environment. The setup is designed to be as simple as possible: clone the repo, configure a few variables, and run the setup script.\n\n### Step 1: Download the Assets\n\nDownload or clone the guide assets from the [assets folder](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/iceberg-v3-tables-comprehensive-guide/assets):\n\n```bash\n# Clone the sfquickstarts repo and navigate to the assets\ngit clone https://github.com/Snowflake-Labs/sfquickstarts.git\ncd sfquickstarts/site/sfguides/src/iceberg-v3-tables-comprehensive-guide/assets\n```\n\n### Step 2: Configure Variables\n\nEdit the `config.env` file in the repository root:\n\n```bash\n# Required: Snowflake Account Information\nSNOWFLAKE_ACCOUNT=\"your_account_identifier\"\nSNOWFLAKE_USER=\"your_username\"\nSNOWFLAKE_ROLE=\"ACCOUNTADMIN\"\nSNOWFLAKE_WAREHOUSE=\"FLEET_ANALYTICS_WH\"\nSNOWFLAKE_DATABASE=\"FLEET_ANALYTICS_DB\"\n\n# Iceberg storage — default is Snowflake-managed (no bucket or external volume).\n# See: https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage\nUSE_SNOWFLAKE_STORAGE=true\n\n# --- Only if USE_SNOWFLAKE_STORAGE=false (customer-managed storage) ---\n# OPTION A: Use an existing external volume\nUSE_EXISTING_VOLUME=false\nEXISTING_VOLUME_NAME=\"\"\n\n# OPTION B: Create a new external volume named FLEET_ICEBERG_VOL\nSTORAGE_PROVIDER=\"S3\"  # Options: S3, GCS, AZURE\n\n# --- AWS S3 ---\nS3_BUCKET_NAME=\"your-bucket-name\"\nS3_PATH=\"iceberg/fleet-analytics/\"\nAWS_ROLE_ARN=\"\"  # Your IAM role ARN (see AWS S3 Setup below)\n\n# --- Google Cloud Storage ---\nGCS_BUCKET_NAME=\"your-bucket-name\"\nGCS_PATH=\"iceberg/fleet-analytics/\"\n\n# --- Azure Storage (Blob, ADLS Gen2, or OneLake) ---\n# See: https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-external-volume-azure\nAZURE_TENANT_ID=\"your-tenant-id\"\nAZURE_STORAGE_ACCOUNT=\"your-storage-account\"\nAZURE_CONTAINER=\"your-container\"\nAZURE_PATH=\"iceberg/fleet-analytics/\"\n# For OneLake, set USE_ONELAKE=true and provide workspace/lakehouse GUIDs\nUSE_ONELAKE=false\nONELAKE_WORKSPACE_GUID=\"\"\nONELAKE_LAKEHOUSE_GUID=\"\"\n```\n\n### Step 3: Run Setup Script\n\nThe setup script will create all necessary Snowflake objects.\n\n```bash\n# Make the script executable\nchmod +x setup.sh\n\n# Run the setup\n./setup.sh\n```\n\n**What the setup script does:**\n\n1. Creates the database, schema, and warehouse\n2. Sets Iceberg storage to Snowflake-managed by default (`SNOWFLAKE_MANAGED`), or creates/uses a customer external volume when `USE_SNOWFLAKE_STORAGE=false`\n3. Creates all Iceberg V3 tables with appropriate schemas\n4. Creates Dynamic Iceberg Tables for transformation pipelines\n5. Creates governance objects (masking policies, data quality functions) - *Enterprise edition only*\n6. Uploads sample JSON log files to the internal stage\n7. Creates the Snowflake CoWork agent configuration\n\n### Step 4: Import the Snowflake Notebook\n\nImport the pre-populated notebook into Snowflake to follow along with the live demo:\n\n1. Sign in to [Snowsight](https://app.snowflake.com)\n2. In the navigation menu, select **Projects » Notebooks**\n3. Click the down arrow next to **+ Notebook** and select **Import .ipynb file**\n4. Navigate to the cloned repository and select `assets/fleet_analytics_notebook.ipynb`\n5. In the import dialog:\n   - **Notebook location**: Select `FLEET_ANALYTICS_DB` and `RAW` schema\n   - **Query warehouse**: Select `FLEET_ANALYTICS_WH`\n6. Click **Create** to import the notebook\n\n\u003E **Note**: The notebook is pre-configured with SQL and Python cells that should import with the correct cell types.\n\n**After importing**, enable external API access for the weather data section:\n1. Click **Notebook settings** (⋮ icon, top right)\n2. Select **External access**\n3. Toggle ON **OPEN_METEO_ACCESS**\n4. Click **Save**\n\nFor more details on creating notebooks, see [Create a notebook](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-create#create-a-new-notebook).\n\n### External volume setup (customer-managed storage only)\n\nWith the default `USE_SNOWFLAKE_STORAGE=true`, Iceberg tables use [Snowflake storage for Apache Iceberg tables](https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage) (`EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'`). You can skip this entire subsection.\n\nIf you set `USE_SNOWFLAKE_STORAGE=false`, Iceberg tables use a **customer-managed external volume** pointing to your object storage. Configure `config.env` (existing volume or new `FLEET_ICEBERG_VOL` via `STORAGE_PROVIDER`) and complete provider-specific setup before running `setup.sh`.\n\nYou can also create external volumes from Snowflake's web UI by navigating to **Catalog \u003E External data \u003E External volumes tab**.\n\n![Create External Volume](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/create_external_volume.png?v=8cbd55b6)\n\n**AWS S3 Setup:**\n\n1. Create an IAM policy granting Snowflake access to your S3 bucket:\n\n```json\n{\n    \"Version\": \"2012-10-17\",\n    \"Statement\": [\n        {\n            \"Effect\": \"Allow\",\n            \"Action\": [\n                \"s3:GetObject\",\n                \"s3:GetObjectVersion\",\n                \"s3:PutObject\",\n                \"s3:DeleteObject\",\n                \"s3:ListBucket\",\n                \"s3:GetBucketLocation\"\n            ],\n            \"Resource\": [\n                \"arn:aws:s3:::your-bucket-name/*\",\n                \"arn:aws:s3:::your-bucket-name\"\n            ]\n        }\n    ]\n}\n```\n\n2. Create an IAM role with this policy and update the trust relationship after running `DESCRIBE EXTERNAL VOLUME` to get the Snowflake AWS IAM user ARN.\n\n**GCS Setup:**\n\n1. Create a Cloud Storage service account\n2. Grant `storage.objectAdmin` role on your bucket\n3. After running `DESCRIBE EXTERNAL VOLUME`, grant the Snowflake service account access\n\n**Azure Setup:**\n\n1. Create an Azure AD application registration\n2. Grant `Storage Blob Data Contributor` role on your container\n3. Configure the application for Snowflake access\n\nThe setup script will provide specific instructions for your chosen provider.\n\n### Verify Setup\n\nAfter running the setup script, verify everything was created:\n\n```sql\n-- Connect to Snowflake and run:\nUSE DATABASE FLEET_ANALYTICS_DB;\nSHOW ICEBERG TABLES;\nSHOW DYNAMIC TABLES;\nSHOW MASKING POLICIES;\nSHOW DATA METRIC FUNCTIONS;\nLIST @LOGS_STAGE;\n```\n\nYou should see all the Iceberg tables, dynamic tables, and governance objects listed.\n\n\u003C!-- ------------------------ --\u003E\n## Streaming and Ingest\n\nThis section demonstrates various methods Snowflake supports for loading semi-structured data into Iceberg V3 tables with the VARIANT data type.\n\n### Snowpipe Streaming: Real-Time Vehicle Telemetry\n\nSnowpipe Streaming enables low-latency ingestion of streaming data directly into Iceberg tables. The high-performance architecture provides sub-second latency for getting data queryable in Snowflake.\n\n#### Review the Streaming Target Table\n\nThe setup script created an Iceberg table designed to receive streaming telemetry:\n\n```sql\nUSE DATABASE FLEET_ANALYTICS_DB;\n\n-- View the table structure\nDESCRIBE TABLE VEHICLE_TELEMETRY_STREAM;\n```\n\n```sql\n-- Confirm the table exists but is empty\nSELECT COUNT(*) FROM VEHICLE_TELEMETRY_STREAM;\n-- Returns: 0\n\n-- View the table properties showing it's an Iceberg V3 table with VARIANT column\nSELECT \n    GET_DDL('TABLE', 'VEHICLE_TELEMETRY_STREAM');\n```\n\nYou'll see a VARIANT column called `TELEMETRY_DATA` designed to hold the flexible schema vehicle telemetry payload.\n\n#### Start the Streaming Simulation\n\nOpen a terminal and navigate to the cloned repository:\n\n```bash\ncd iceberg-v3-tables-comprehensive-guide/assets\n\n# Activate the Python environment (created by setup.sh)\nsource iceberg_v3_demo_venv/bin/activate\n\n# Start the streaming simulation (runs for 5 minutes max)\npython stream_telemetry.py\n```\n\n\u003E **Troubleshooting Connection Issues**: If you're on a VPN or corporate network and get connection errors, you may need a network policy to allow inbound connections. Set `ENABLE_NETWORK_POLICY=true` in `config.env` and re-run setup, or manually run `08_network_policy.sql` in Snowsight, then apply the policy to your user:\n\u003E ```sql\n\u003E ALTER USER \u003Cyour_username\u003E SET NETWORK_POLICY = FLEET_STREAMING_POLICY;\n\u003E ```\n\nThe Python script uses the Snowpipe Streaming SDK to simulate vehicle telemetry events:\n\n```python\n# Example of what the streaming script sends\n{\n    \"vehicle_id\": \"VH-2847\",\n    \"event_timestamp\": \"2026-02-03T14:32:15.847293847\",  # Nanosecond precision\n    \"telemetry_data\": {\n        \"location\": {\"lat\": 37.7749, \"lon\": -122.4194},\n        \"speed_mph\": 65.3,\n        \"heading\": 285,\n        \"engine\": {\n            \"rpm\": 3200,\n            \"temperature_f\": 195,\n            \"oil_pressure_psi\": 42,\n            \"fuel_level_pct\": 67.5\n        },\n        \"diagnostics\": {\n            \"check_engine\": false,\n            \"tire_pressure_warning\": false,\n            \"codes\": []\n        },\n        \"driver_behavior\": {\n            \"hard_acceleration_count\": 2,\n            \"hard_brake_count\": 1,\n            \"sharp_turn_count\": 0\n        }\n    }\n}\n```\n\n#### Observe Data Flowing In\n\nWhile the streaming script runs, watch data appear in Snowflake:\n\n```sql\n-- Check current count of streaming events\nSELECT COUNT(*) AS event_count FROM VEHICLE_TELEMETRY_STREAM;\n\n-- View the latest streaming events with VARIANT extraction\nSELECT \n    VEHICLE_ID,\n    EVENT_TIMESTAMP,\n    TELEMETRY_DATA:location:lat::FLOAT AS latitude,\n    TELEMETRY_DATA:location:lon::FLOAT AS longitude,\n    TELEMETRY_DATA:speed_mph::FLOAT AS speed_mph,\n    TELEMETRY_DATA:engine:temperature_f::INT AS engine_temp,\n    TELEMETRY_DATA:engine:fuel_level_pct::FLOAT AS fuel_level\nFROM VEHICLE_TELEMETRY_STREAM\nORDER BY EVENT_TIMESTAMP DESC\nLIMIT 10;\n```\n\nThe streaming data is immediately queryable with full SQL capabilities on the VARIANT column!\n\n### Pull from a Web API: Weather Data\n\nInstead of multi-step file-based ingestion, Snowflake can connect directly to web APIs and ingest semi-structured responses into Iceberg tables with VARIANT.\n\n#### Enable External Access in Your Notebook\n\nSnowflake Notebooks require an **External Access Integration** to call external APIs. The setup script created an integration for the Open-Meteo weather API.\n\n**Before running the Python cell:**\n\n1. In your Snowflake Notebook, click the **Notebook settings** (⋮ icon, top right)\n2. Select **External access**\n3. Toggle ON **OPEN_METEO_ACCESS**\n4. Click **Save**\n\n\u003E **Note**: If you don't see `OPEN_METEO_ACCESS`, run this SQL first:\n\u003E ```sql\n\u003E CREATE OR REPLACE NETWORK RULE FLEET_ANALYTICS_DB.RAW.OPEN_METEO_NETWORK_RULE\n\u003E     MODE = EGRESS TYPE = HOST_PORT VALUE_LIST = ('api.open-meteo.com:443');\n\u003E CREATE OR REPLACE EXTERNAL ACCESS INTEGRATION OPEN_METEO_ACCESS\n\u003E     ALLOWED_NETWORK_RULES = (FLEET_ANALYTICS_DB.RAW.OPEN_METEO_NETWORK_RULE) ENABLED = TRUE;\n\u003E ```\n\n#### View the Target Table\n\n```sql\n-- The table was created during setup\nDESCRIBE TABLE API_WEATHER_DATA;\n\n-- Confirm it's empty\nSELECT COUNT(*) AS current_count FROM API_WEATHER_DATA;\n```\n\n#### Create and Run the API Ingestion Function\n\nIn your Snowflake Notebook (or worksheet), create a Python function to fetch weather data:\n\n```python\n# This is pre-populated in the Snowflake Notebook\nimport snowflake.snowpark as snowpark\nfrom snowflake.snowpark.functions import col, parse_json, current_timestamp\nimport requests\n\ndef fetch_weather_data(session: snowpark.Session, cities: list) -\u003E str:\n    \"\"\"\n    Fetches weather data from Open-Meteo API and loads into Iceberg table.\n    Open-Meteo is a free, open-source weather API requiring no API key.\n    \"\"\"\n    \n    weather_records = []\n    \n    for city in cities:\n        # Using Open-Meteo free API\n        url = f\"https://api.open-meteo.com/v1/forecast\"\n        params = {\n            \"latitude\": city[\"lat\"],\n            \"longitude\": city[\"lon\"],\n            \"current\": \"temperature_2m,wind_speed_10m,precipitation\",\n            \"hourly\": \"temperature_2m,precipitation_probability\"\n        }\n        \n        response = requests.get(url, params=params)\n        if response.status_code == 200:\n            weather_records.append({\n                \"city_name\": city[\"name\"],\n                \"latitude\": city[\"lat\"],\n                \"longitude\": city[\"lon\"],\n                \"api_response\": response.text,\n                \"fetched_at\": datetime.now().isoformat()\n            })\n    \n    # Create DataFrame and write to Iceberg table\n    df = session.create_dataframe(weather_records)\n    df = df.with_column(\"WEATHER_DATA\", parse_json(col(\"API_RESPONSE\")))\n    df.select(\n        col(\"CITY_NAME\"),\n        col(\"LATITUDE\"),\n        col(\"LONGITUDE\"), \n        col(\"WEATHER_DATA\"),\n        current_timestamp().alias(\"INGESTED_AT\")\n    ).write.mode(\"append\").save_as_table(\"API_WEATHER_DATA\")\n    \n    return f\"Loaded weather data for {len(weather_records)} cities\"\n\n# Define cities for fleet operations\nfleet_cities = [\n    {\"name\": \"San Francisco\", \"lat\": 37.7749, \"lon\": -122.4194},\n    {\"name\": \"Los Angeles\", \"lat\": 34.0522, \"lon\": -118.2437},\n    {\"name\": \"Seattle\", \"lat\": 47.6062, \"lon\": -122.3321},\n    {\"name\": \"Denver\", \"lat\": 39.7392, \"lon\": -104.9903},\n    {\"name\": \"Chicago\", \"lat\": 41.8781, \"lon\": -87.6298}\n]\n\n# Execute the function\nresult = fetch_weather_data(session, fleet_cities)\nprint(result)\n```\n\n#### Query the Weather Data\n\n```sql\n-- View the loaded weather data\nSELECT \n    CITY_NAME,\n    WEATHER_DATA:current:temperature_2m::FLOAT AS current_temp_c,\n    WEATHER_DATA:current:wind_speed_10m::FLOAT AS wind_speed_kmh,\n    WEATHER_DATA:current:precipitation::FLOAT AS precipitation_mm,\n    INGESTED_AT\nFROM API_WEATHER_DATA;\n\n-- Extract hourly forecast for a city\nSELECT \n    CITY_NAME,\n    f.value::FLOAT AS hourly_temp\nFROM API_WEATHER_DATA,\nLATERAL FLATTEN(input =\u003E WEATHER_DATA:hourly:temperature_2m) f\nWHERE CITY_NAME = 'San Francisco'\nLIMIT 24;\n```\n\n\u003E **Tip**: This ingestion pattern can be orchestrated natively in Snowflake using [Tasks](https://docs.snowflake.com/en/user-guide/tasks-intro) or [Serverless Tasks](https://docs.snowflake.com/en/user-guide/tasks-serverless) for scheduled API pulls.\n\n### Batch Ingest JSON Logs\n\nSnowflake handles batch or continuous ingestion of semi-structured data stored in files, such as JSON diagnostic logs.\n\n#### View the Staged Files\n\nThe setup script uploaded 10 sample JSON files mimicking maintenance diagnostic logs:\n\n```sql\n-- List the staged JSON files\nLIST @LOGS_STAGE;\n```\n\nYou should see files like:\n- `maintenance_log_001.json`\n- `maintenance_log_002.json`\n- ... through `maintenance_log_010.json`\n\n#### Load JSON Logs into Iceberg Table\n\n```sql\n-- Load JSON files into Iceberg table\nCOPY INTO MAINTENANCE_LOGS (LOG_ID, VEHICLE_ID, LOG_TIMESTAMP, LOG_DATA, SOURCE_FILE)\nFROM (\n    SELECT \n        $1:log_id::VARCHAR,\n        $1:vehicle_id::VARCHAR,\n        $1:log_timestamp::TIMESTAMP_NTZ,\n        $1,\n        METADATA$FILENAME\n    FROM @LOGS_STAGE\n)\nFILE_FORMAT = (TYPE = 'JSON' STRIP_OUTER_ARRAY = TRUE)\nPATTERN = '.*maintenance_log.*\\.json';\n\n-- View the loaded maintenance logs with VARIANT data\nSELECT \n    LOG_ID,\n    VEHICLE_ID,\n    LOG_TIMESTAMP,\n    LOG_DATA:event_type::STRING AS event_type,\n    LOG_DATA:severity::STRING AS severity,\n    LOG_DATA:description::STRING AS description,\n    LOG_DATA:total_cost::FLOAT AS total_cost\nFROM MAINTENANCE_LOGS\nORDER BY LOG_TIMESTAMP DESC\nLIMIT 10;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Transformation\n\nThis section demonstrates how Snowflake powers transformation pipelines on Iceberg tables using Dynamic Tables. Dynamic Tables provide a declarative approach to data transformation—you define what the output should look like, and Snowflake handles the incremental refresh automatically.\n\nRow lineage is used under the hood of Dynamic Iceberg V3 Tables to power change data capture (CDC) and incremental processing.\n\n### Dynamic Iceberg Tables Overview\n\nThe setup script created several Dynamic Iceberg Tables that form a transformation pipeline:\n\n```sql\n-- View all dynamic tables in the pipeline\nSHOW DYNAMIC TABLES IN DATABASE FLEET_ANALYTICS_DB;\n```\n\nThese tables automatically refresh based on their TARGET_LAG setting, processing only incremental changes from source tables.\n\n#### Explore the Pipeline in the UI\n\n1. Navigate to **Transformation** → **Dynamic Tables** in the left pane\n2. Filter by database: **FLEET_ANALYTICS_DB**\n3. Click on any dynamic table (e.g., `TELEMETRY_ENRICHED`)\n4. Explore the tabs:\n   - **Graph**: Visualize the full pipeline and dependencies\n   - **Refresh History**: See incremental refresh operations\n   - **Definition**: Review the declarative SQL that defines the transformation\n\n#### View the Dynamic Table Graph\n\nThe transformation pipeline looks like this:\n\n![Dynamic Iceberg Table Graph](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/dit_graph.png?v=8cbd55b6)\n\n#### Monitor Incremental Refreshes\n\nAs streaming data flows in, watch the dynamic tables refresh:\n\n![Dynamic Iceberg Table Incremental Refresh](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/dit_incremental_refresh.png?v=8cbd55b6)\n\nThe `REFRESH_MODE = INCREMENTAL` setting ensures only new or changed data is processed, making the pipeline highly efficient.\n\n#### Query Transformed Data\n\n```sql\n-- Query enriched telemetry with vehicle details\nSELECT \n    VEHICLE_ID,\n    MAKE,\n    MODEL,\n    DRIVER_NAME,\n    FLEET_REGION,\n    speed_mph,\n    engine_temp_f,\n    engine_health_status,\n    driving_behavior,\n    EVENT_TIMESTAMP\nFROM CURATED.TELEMETRY_ENRICHED\nWHERE engine_health_status != 'NORMAL' OR driving_behavior = 'AGGRESSIVE'\nORDER BY EVENT_TIMESTAMP DESC\nLIMIT 20;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Security and Governance\n\nThis section highlights Horizon Catalog as the central, unified, interoperable catalog with enterprise-grade security features. All governance capabilities work seamlessly with Iceberg V3 tables.\n\n### PII Detection with Classification\n\nSnowflake can automatically detect and classify sensitive data across your entire database, including Iceberg tables.\n\n#### Classify the Schema\n\n1. Navigate to **Data** → **Databases** → **FLEET_ANALYTICS_DB** → **RAW** (the schema)\n2. Click the **three dots menu** (⋮) in the top right corner\n3. Select **Classify and Tag Sensitive Data**\n4. Select all Iceberg tables in the schema\n5. Toggle **Automatically tag data** ON\n6. Click **Classify and Tag Sensitive Data**\n\n![Classify and Tag Sensitive Data](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/classify_sensitive_data.png?v=8cbd55b6)\n\n#### View Applied Tags\n\nAfter classification completes:\n\n1. Navigate to any table (e.g., `VEHICLE_REGISTRY`)\n2. Click the **Columns** tab\n3. Notice tags applied to sensitive columns like `DRIVER_NAME`, `DRIVER_EMAIL`, `DRIVER_PHONE`\n\n![Tagged Sensitive Data](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/classification_tag.png?v=8cbd55b6)\n\nCommon tags that may be applied:\n- `SEMANTIC_CATEGORY:NAME` - Personal names\n- `SEMANTIC_CATEGORY:EMAIL` - Email addresses\n- `SEMANTIC_CATEGORY:PHONE_NUMBER` - Phone numbers\n- `SEMANTIC_CATEGORY:US_SSN` - Social Security Numbers\n\n\u003E **Tip**: You can also create [custom tags that automatically propagate](https://docs.snowflake.com/en/user-guide/object-tagging/work#define-a-tag-that-will-automatically-propagate) to existing and new downstream objects.\n\n### Fine-Grained Access Control with Masking Policies\n\nThe setup script created masking policies to protect sensitive information. Now apply them to your Iceberg tables.\n\n#### View Created Masking Policies\n\n```sql\n-- View all masking policies in the database\nSHOW MASKING POLICIES IN DATABASE FLEET_ANALYTICS_DB;\n```\n\nThe `PII_MASK` policy was created:\n\n```sql\n-- View the policy definition\nSELECT GET_DDL('MASKING_POLICY', 'PII_MASK');\n```\n\n```sql\nCREATE OR REPLACE MASKING POLICY PII_MASK AS (val STRING) \nRETURNS STRING -\u003E\n    CASE\n        WHEN CURRENT_ROLE() IN ('FLEET_ADMIN', 'ACCOUNTADMIN') THEN val\n        WHEN CURRENT_ROLE() = 'FLEET_ANALYST' THEN \n            CONCAT(LEFT(val, 2), '****', RIGHT(val, 2))\n        ELSE '********'\n    END;\n```\n\n#### Apply Masking Policies via UI\n\n1. Navigate to **Data** → **Databases** → **FLEET_ANALYTICS_DB** → **RAW** → **VEHICLE_REGISTRY**\n2. Click the **Columns** tab\n3. Find the `DRIVER_NAME` column\n4. In the **Policies** column, click the **+** button\n5. Select `PII_NAME_MASK` and click **Done**\n6. Repeat for `DRIVER_EMAIL` and `DRIVER_PHONE` with `PII_EMAIL_MASK` and `PII_PHONE_MASK`\n\n![Mask Tagged Columns](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/tag_mask.png?v=8cbd55b6)\n\n#### Test Masking Behavior\n\n```sql\n-- As ACCOUNTADMIN, see full PII values (unmasked)\nSELECT VEHICLE_ID, DRIVER_NAME, DRIVER_EMAIL, DRIVER_PHONE\nFROM VEHICLE_REGISTRY LIMIT 5;\n\n-- Create a test analyst role and grant it to the current user\nCREATE ROLE IF NOT EXISTS FLEET_ANALYST;\nGRANT USAGE ON DATABASE FLEET_ANALYTICS_DB TO ROLE FLEET_ANALYST;\nGRANT USAGE ON SCHEMA FLEET_ANALYTICS_DB.RAW TO ROLE FLEET_ANALYST;\nGRANT SELECT ON ALL TABLES IN SCHEMA FLEET_ANALYTICS_DB.RAW TO ROLE FLEET_ANALYST;\nGRANT USAGE ON WAREHOUSE FLEET_ANALYTICS_WH TO ROLE FLEET_ANALYST;\n\n-- Grant the role to the current user so we can switch to it\nSET MY_USER = CURRENT_USER();\nGRANT ROLE FLEET_ANALYST TO USER IDENTIFIER($MY_USER);\n\n-- Switch to analyst role and observe MASKED values\nUSE ROLE FLEET_ANALYST;\nSELECT VEHICLE_ID, DRIVER_NAME, DRIVER_EMAIL, DRIVER_PHONE\nFROM VEHICLE_REGISTRY LIMIT 5;\n-- Results show masked values: \"Jo****hn\", \"jo****om\"\n\n-- Switch back to ACCOUNTADMIN for remaining operations\nUSE ROLE ACCOUNTADMIN;\n```\n\n### Data Quality Monitoring\n\nThe setup script created Data Metric Functions (DMFs) to monitor data quality on Iceberg tables.\n\n#### View Data Quality Setup\n\n```sql\n-- Show data metric functions\nSHOW DATA METRIC FUNCTIONS IN DATABASE FLEET_ANALYTICS_DB;\n```\n\nExample DMF for validating geospatial coordinates:\n\n```sql\nCREATE OR REPLACE DATA METRIC FUNCTION CHECK_VALID_COORDINATES(\n    arg_t TABLE(lat FLOAT, lon FLOAT)\n)\nRETURNS NUMBER\nAS\n$$\n    SELECT COUNT(*) \n    FROM arg_t\n    WHERE lat \u003C -90 OR lat \u003E 90 \n       OR lon \u003C -180 OR lon \u003E 180\n$$;\n```\n\n#### Review Data Quality Results\n\n\u003E **Note**: The Data Quality dashboard will initially be empty. DMFs run on a schedule (default: when triggered by Snowflake). To see results sooner, set a shorter schedule and insert new data to trigger the DMF.\n\nTo configure DMF scheduling and verify results:\n\n```sql\n-- Set DMFs to trigger when data changes\nALTER ICEBERG TABLE RAW.VEHICLE_LOCATIONS SET DATA_METRIC_SCHEDULE = 'TRIGGER_ON_CHANGES';\nALTER ICEBERG TABLE RAW.SENSOR_READINGS SET DATA_METRIC_SCHEDULE = 'TRIGGER_ON_CHANGES';\n\n-- Or set a specific schedule (e.g., every 5 minutes)\n-- ALTER ICEBERG TABLE RAW.VEHICLE_LOCATIONS SET DATA_METRIC_SCHEDULE = '5 MINUTE';\n\n-- Check which DMFs are attached to VEHICLE_LOCATIONS\nSELECT * FROM TABLE(INFORMATION_SCHEMA.DATA_METRIC_FUNCTION_REFERENCES(\n    REF_ENTITY_NAME =\u003E 'FLEET_ANALYTICS_DB.RAW.VEHICLE_LOCATIONS',\n    REF_ENTITY_DOMAIN =\u003E 'TABLE'\n));\n\n-- Insert a row to trigger the DMF (if using TRIGGER_ON_CHANGES)\nINSERT INTO RAW.VEHICLE_LOCATIONS (LOCATION_ID, VEHICLE_ID, LOCATION_TIMESTAMP, LATITUDE, LONGITUDE, LOCATION_POINT, SPEED_MPH, HEADING_DEGREES, FLEET_REGION)\nSELECT UUID_STRING(), VEHICLE_ID, CURRENT_TIMESTAMP(), LATITUDE, LONGITUDE, LOCATION_POINT, SPEED_MPH, HEADING_DEGREES, FLEET_REGION\nFROM RAW.VEHICLE_LOCATIONS LIMIT 1;\n\n-- Check DMF results (may take a minute to populate after data insert)\nSELECT \n    TABLE_NAME,\n    METRIC_NAME,\n    VALUE,\n    MEASUREMENT_TIME\nFROM SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_RESULTS\nWHERE TABLE_DATABASE = 'FLEET_ANALYTICS_DB'\nORDER BY MEASUREMENT_TIME DESC\nLIMIT 20;\n```\n\n\u003E **Tip**: Insert or modify data in the table to trigger the DMF if using `TRIGGER_ON_CHANGES`. The Data Quality tab will populate after the next scheduled run.\n\nTo view the Data Quality dashboard:\n\n1. Navigate to **Data** → **Databases** → **FLEET_ANALYTICS_DB** → **RAW** → **VEHICLE_LOCATIONS**\n2. Click on the **Data Quality** tab\n3. After DMFs have run, you'll see:\n   - Quality check results over time\n   - Failed record counts  \n   - Data quality trends by dimension (Freshness, Volume, Accuracy, etc.)\n\nFor more details, see [Getting Started with Data Quality in Snowflake](https://www.snowflake.com/en/developers/guides/getting-started-with-data-quality-in-snowflake/#review).\n\n### Data Lineage\n\nSnowflake automatically tracks lineage for all objects, including Iceberg tables and Dynamic Tables.\n\n#### View Lineage Graph\n\n1. Navigate to **Data** → **Databases** → **FLEET_ANALYTICS_DB** → **RAW** → **MAINTENANCE_LOGS**\n2. Click on the **Lineage** tab\n3. Explore the lineage graph showing both upstream and downstream tables, including upstream lineage from an external telemetry messaging service\n\nThe lineage graph is valuable for:\n- **Impact analysis**: Understanding what's affected when source data changes\n- **Data quality investigation**: Tracing issues back to their source\n- **Security auditing**: Understanding data flow for sensitive information\n\n![Data Lineage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/lineage.png?v=8cbd55b6)\n\n\u003C!-- ------------------------ --\u003E\n## Analytics and AI\n\nThis section demonstrates Snowflake's analytical capabilities on Iceberg V3 tables, including semi-structured data querying, time-series analysis, geospatial functions, and AI agents.\n\n### Semi-Structured Data Analytics\n\nSnowflake efficiently queries VARIANT data in Iceberg tables with automatic pruning and optimization.\n\n#### Query Nested Variant Data\n\n```sql\n-- Extract nested fields from VARIANT with dot notation\nSELECT \n    VEHICLE_ID,\n    TELEMETRY_DATA:speed_mph::FLOAT AS speed,\n    TELEMETRY_DATA:engine:rpm::INT AS engine_rpm,\n    TELEMETRY_DATA:engine:temperature_f::INT AS engine_temp,\n    TELEMETRY_DATA:diagnostics:check_engine::BOOLEAN AS check_engine_light,\n    TELEMETRY_DATA:driver_behavior:hard_brake_count::INT AS hard_brakes\nFROM RAW.VEHICLE_TELEMETRY_STREAM\nWHERE TELEMETRY_DATA:speed_mph::FLOAT \u003E 50\nLIMIT 20;\n\n-- Aggregate on variant fields - find vehicles with check engine warnings\nSELECT \n    TELEMETRY_DATA:diagnostics:check_engine::BOOLEAN AS check_engine,\n    COUNT(*) AS event_count,\n    ROUND(AVG(TELEMETRY_DATA:speed_mph::FLOAT), 1) AS avg_speed,\n    COUNT(DISTINCT VEHICLE_ID) AS vehicle_count\nFROM RAW.VEHICLE_TELEMETRY_STREAM\nGROUP BY 1\nORDER BY event_count DESC;\n```\n\n#### Observe Query Pruning\n\n```sql\n-- Run with profile to see pruning efficiency\nSELECT \n    VEHICLE_ID,\n    EVENT_TIMESTAMP,\n    TELEMETRY_DATA:speed_mph::FLOAT AS speed\nFROM VEHICLE_TELEMETRY_STREAM\nWHERE VEHICLE_ID = 'VH-1234'\n  AND TELEMETRY_DATA:engine:temperature_f::INT \u003E 200;\n\n-- Check query profile in History tab to see partition pruning statistics\n```\n\n### Time-Series Analytics and Forecasting\n\nThe `SENSOR_READINGS` table contains high-precision time-series data for analysis.\n\n#### AS OF Join for Point-in-Time Analysis\n\n```sql\n-- AS OF join: Find closest sensor reading before each maintenance event\nSELECT \n    m.VEHICLE_ID,\n    m.LOG_TIMESTAMP AS maintenance_time,\n    m.LOG_DATA:event_type::STRING AS maintenance_type,\n    s.READING_TIMESTAMP AS closest_reading_time,\n    s.ENGINE_TEMP_F,\n    s.OIL_PRESSURE_PSI\nFROM RAW.MAINTENANCE_LOGS m\nASOF JOIN RAW.SENSOR_READINGS s\n    MATCH_CONDITION (m.LOG_TIMESTAMP \u003E= s.READING_TIMESTAMP)\n    ON m.VEHICLE_ID = s.VEHICLE_ID\nWHERE m.LOG_DATA:severity::STRING IN ('CRITICAL', 'HIGH')\nORDER BY m.LOG_TIMESTAMP DESC\nLIMIT 15;\n```\n\n#### Time-Series Aggregation with \n\nWindow functions enable powerful time-series analysis directly in SQL:\n- **Rolling averages** to smooth out noise\n- **Running totals** for cumulative metrics\n- **Lag/Lead** for period-over-period comparisons\n\n```sql\n-- Calculate rolling averages for engine temperature\nSELECT \n    VEHICLE_ID,\n    READING_TIMESTAMP,\n    ENGINE_TEMP_F,\n    AVG(ENGINE_TEMP_F) OVER (\n        PARTITION BY VEHICLE_ID \n        ORDER BY READING_TIMESTAMP \n        RANGE BETWEEN INTERVAL '1 HOUR' PRECEDING AND CURRENT ROW\n    ) AS rolling_avg_temp,\n    MAX(ENGINE_TEMP_F) OVER (\n        PARTITION BY VEHICLE_ID \n        ORDER BY READING_TIMESTAMP \n        RANGE BETWEEN INTERVAL '1 HOUR' PRECEDING AND CURRENT ROW\n    ) AS rolling_max_temp\nFROM RAW.SENSOR_READINGS\nORDER BY VEHICLE_ID, READING_TIMESTAMP\nLIMIT 50;\n```\n\n#### ML Forecasting for Predictive Maintenance\n\n\u003E ⏱️ **Training Time**: Model training may take **2-5 minutes** depending on data volume and warehouse size. To speed up training:\n\u003E - Use a **larger warehouse** (e.g., MEDIUM or LARGE) - training scales with compute\n\u003E - Reduce the number of time series by filtering to specific vehicles  \n\u003E - Limit historical data range (e.g., 14 days instead of 30)\n\n```sql\n-- Create a forecast model for fuel consumption\n-- For Iceberg tables, we must use SYSTEM$QUERY_REFERENCE instead of TABLE reference\nCREATE OR REPLACE SNOWFLAKE.ML.FORECAST fuel_consumption_forecast(\n    INPUT_DATA =\u003E SYSTEM$QUERY_REFERENCE('\n        SELECT \n            VEHICLE_ID,\n            READING_TIMESTAMP,\n            FUEL_CONSUMPTION_GPH\n        FROM RAW.SENSOR_READINGS\n        WHERE READING_TIMESTAMP \u003E DATEADD(''day'', -30, CURRENT_TIMESTAMP())\n    '),\n    TIMESTAMP_COLNAME =\u003E 'READING_TIMESTAMP',\n    TARGET_COLNAME =\u003E 'FUEL_CONSUMPTION_GPH',\n    SERIES_COLNAME =\u003E 'VEHICLE_ID'\n);\n\n-- Generate 7-day forecast and save results\nCALL fuel_consumption_forecast!FORECAST(\n    FORECASTING_PERIODS =\u003E 168,  -- 7 days * 24 hours\n    CONFIG_OBJECT =\u003E {'prediction_interval': 0.95}\n);\n\n-- FORECAST: Generate a 7-day forecast (168 hours) and save to a table for visualization\nCALL fuel_consumption_forecast!FORECAST(\n    FORECASTING_PERIODS =\u003E 168,\n    CONFIG_OBJECT =\u003E {'prediction_interval': 0.95}\n);\n\n-- Save forecast results to a table for visualization\nCREATE OR REPLACE TEMPORARY TABLE FUEL_FORECAST_RESULTS AS\nSELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID()));\n\n-- Preview forecast results\nSELECT * FROM FUEL_FORECAST_RESULTS ORDER BY SERIES, TS LIMIT 20;\n```\n\n#### Visualize Historical + Forecast Data\n\nUse Streamlit in a Snowflake Notebook to plot historical data alongside the forecast:\n\n```python\n# 📈 Visualize Historical + Forecast Fuel Consumption\nimport streamlit as st\nimport pandas as pd\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\n\n# Get historical data (last 30 days for one vehicle)\nhistorical_df = session.sql(\"\"\"\n    SELECT \n        READING_TIMESTAMP AS ts,\n        FUEL_CONSUMPTION_GPH AS value,\n        'Historical' AS data_type\n    FROM RAW.SENSOR_READINGS\n    WHERE VEHICLE_ID = (SELECT SERIES FROM FUEL_FORECAST_RESULTS LIMIT 1)\n      AND READING_TIMESTAMP \u003E DATEADD('day', -30, CURRENT_TIMESTAMP())\n    ORDER BY READING_TIMESTAMP\n\"\"\").to_pandas()\n\n# Get forecast data (column names vary - check with DESCRIBE first if needed)\nforecast_df = session.sql(\"\"\"\n    SELECT \n        TS AS ts,\n        FORECAST AS value,\n        'Forecast' AS data_type\n    FROM FUEL_FORECAST_RESULTS\n    WHERE SERIES = (SELECT SERIES FROM FUEL_FORECAST_RESULTS LIMIT 1)\n    ORDER BY TS\n\"\"\").to_pandas()\n\n# Get the vehicle ID for the title\nvehicle_id = session.sql(\"SELECT SERIES FROM FUEL_FORECAST_RESULTS LIMIT 1\").collect()[0][0]\n\nst.subheader(f\"📈 Fuel Consumption Forecast: {vehicle_id}\")\nst.caption(\"Historical data (30 days) + 7-day forecast\")\n\n# Normalize column names to lowercase\nhistorical_df.columns = historical_df.columns.str.lower()\nforecast_df.columns = forecast_df.columns.str.lower()\n\n# Combine for plotting\ncombined_df = pd.concat([historical_df, forecast_df], ignore_index=True)\ncombined_df['ts'] = pd.to_datetime(combined_df['ts'])\ncombined_df = combined_df.sort_values('ts')\n\n# Create the chart using Streamlit's native line chart\n# Pivot data for multi-line chart\nchart_data = combined_df.pivot_table(index='ts', columns='data_type', values='value', aggfunc='first')\nst.line_chart(chart_data, use_container_width=True)\n\n# Display metrics\ncol1, col2, col3 = st.columns(3)\ncol1.metric(\"Avg Historical (GPH)\", f\"{historical_df['value'].mean():.2f}\")\ncol2.metric(\"Avg Forecast (GPH)\", f\"{forecast_df['value'].mean():.2f}\")\nchange = ((forecast_df['value'].mean() - historical_df['value'].mean()) / historical_df['value'].mean()) * 100\ncol3.metric(\"Projected Change\", f\"{change:+.1f}%\")\n\nst.caption(\"💡 The forecast helps identify vehicles likely to have higher fuel consumption, enabling proactive maintenance scheduling.\")\n```\n\n### Geospatial Analytics\n\nH3 is Uber's hierarchical geospatial indexing system. Snowflake's H3 functions enable:\n- Aggregating vehicles by geographic cell\n- Efficient spatial joins and clustering\n- Multi-resolution analysis (cells at different zoom levels)\n\nThe `VEHICLE_LOCATIONS` table contains GEOGRAPHY data for spatial analysis.\n\n#### Basic Geospatial Queries\n\n```sql\n-- Calculate distance between consecutive positions\nSELECT \n    VEHICLE_ID,\n    LOCATION_TIMESTAMP,\n    LAG(LOCATION_POINT) OVER (\n        PARTITION BY VEHICLE_ID ORDER BY LOCATION_TIMESTAMP\n    ) AS prev_location,\n    LOCATION_POINT AS current_location,\n    ST_DISTANCE(\n        LOCATION_POINT,\n        LAG(LOCATION_POINT) OVER (PARTITION BY VEHICLE_ID ORDER BY LOCATION_TIMESTAMP)\n    ) / 1609.34 AS distance_miles  -- Convert meters to miles\nFROM VEHICLE_LOCATIONS\nORDER BY VEHICLE_ID, LOCATION_TIMESTAMP\nLIMIT 50;\n```\n\n#### Geofencing Analysis\n\n```sql\n-- Define a geofence (San Francisco downtown)\nSET sf_downtown = ST_MAKEPOLYGON(TO_GEOGRAPHY(\n    'LINESTRING(-122.42 37.78, -122.42 37.79, -122.40 37.79, -122.40 37.78, -122.42 37.78)'\n));\n\n-- Find vehicles inside the geofence\nSELECT \n    VEHICLE_ID,\n    LOCATION_TIMESTAMP,\n    ST_X(LOCATION_POINT) AS longitude,\n    ST_Y(LOCATION_POINT) AS latitude\nFROM VEHICLE_LOCATIONS\nWHERE ST_WITHIN(LOCATION_POINT, $sf_downtown)\nORDER BY LOCATION_TIMESTAMP DESC\nLIMIT 20;\n\n-- Geospatial: Aggregate vehicles by H3 cell (resolution 6)\nSELECT \n    H3_POINT_TO_CELL_STRING(LOCATION_POINT, 6) AS h3_cell,\n    FLEET_REGION,\n    COUNT(DISTINCT VEHICLE_ID) AS vehicle_count,\n    ROUND(AVG(SPEED_MPH), 1) AS avg_speed\nFROM RAW.VEHICLE_LOCATIONS\nWHERE LOCATION_TIMESTAMP \u003E DATEADD('hour', -24, CURRENT_TIMESTAMP())\nGROUP BY 1, 2\nORDER BY vehicle_count DESC\nLIMIT 20;\n```\n\n#### Interactive Map Visualization with Streamlit\n\nIn the Snowflake Notebook, you can visualize the H3 geospatial data on an interactive map using Streamlit (built into Snowflake Notebooks):\n\n```python\n# Visualize fleet locations on an interactive map using Streamlit\nimport streamlit as st\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\n\n# Query location data with H3 cell centers (use ST_X/ST_Y for GEOGRAPHY type)\ndf = session.sql(\"\"\"\n    SELECT \n        H3_POINT_TO_CELL_STRING(LOCATION_POINT, 6) AS h3_cell,\n        ST_X(H3_CELL_TO_POINT(H3_POINT_TO_CELL_STRING(LOCATION_POINT, 6))) AS longitude,\n        ST_Y(H3_CELL_TO_POINT(H3_POINT_TO_CELL_STRING(LOCATION_POINT, 6))) AS latitude,\n        FLEET_REGION,\n        COUNT(DISTINCT VEHICLE_ID) AS vehicle_count,\n        ROUND(AVG(SPEED_MPH), 1) AS avg_speed\n    FROM RAW.VEHICLE_LOCATIONS\n    WHERE LOCATION_TIMESTAMP \u003E DATEADD('hour', -24, CURRENT_TIMESTAMP())\n    GROUP BY 1, 2, 3, 4\n    ORDER BY vehicle_count DESC\n    LIMIT 100\n\"\"\").to_pandas()\n\nst.subheader(\"🗺️ Fleet Vehicle Distribution\")\nst.caption(\"Each point represents an H3 cell with vehicle activity in the last 24 hours\")\n\n# Display metrics\ncol1, col2, col3 = st.columns(3)\ncol1.metric(\"Total H3 Cells\", len(df))\ncol2.metric(\"Total Vehicles\", int(df['VEHICLE_COUNT'].sum()))\ncol3.metric(\"Avg Speed\", f\"{df['AVG_SPEED'].mean():.1f} mph\")\n\n# Interactive map visualization - point size scales with vehicle count\nst.map(df, latitude='LATITUDE', longitude='LONGITUDE', size='VEHICLE_COUNT')\n\n# Show data table\nst.dataframe(df[['FLEET_REGION', 'VEHICLE_COUNT', 'AVG_SPEED']].head(10))\n```\n\nThis produces an interactive map showing vehicle distribution across the US, with:\n- Points sized by vehicle count per H3 cell\n- Summary metrics for cells, vehicles, and average speed\n- Data table with regional breakdown\n\n### AI Agents with Snowflake CoWork\n\nThe setup script creates a Cortex Agent (`FLEET_ANALYTICS_AGENT`) that enables natural language querying of your fleet data.\n\n\u003E **Note**: Cortex Agents require Enterprise Edition or higher.\n\n#### Region Availability and Cross-Region Inference\n\nCortex Agents and Cortex Analyst rely on LLMs that may not be available in all Snowflake regions. If you see an error like:\n\n```\nNone of the preferred models are authorized or available in your region...\n```\n\nYou have two options:\n\n1. **Enable Cross-Region Inference** (recommended): This allows Snowflake to route AI requests to regions where models are available, while keeping your data secure.\n\n```sql\n-- Enable cross-region inference for your account\nUSE ROLE ACCOUNTADMIN;\nALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\n```\n\n2. **Use a supported region**: Deploy your Snowflake account in a region with native LLM support.\n\nFor more details, see:\n- [Cortex Analyst Region Availability](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst#region-availability)\n- [Cross-Region Inference](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cross-region-inference)\n\n\u003E **Privacy Note**: With cross-region inference, only the AI inference is routed cross-region. Your underlying data remains in your account's region and is protected by Snowflake's governance controls.\n\n#### Agent Configuration\n\nThe setup script creates the agent with a complete YAML specification including:\n- **Sample questions** to help users get started\n- **Orchestration instructions** explaining the data schema\n- **Cortex Analyst tool** linked to a semantic model describing all tables\n\nThe semantic model (`fleet_semantic_model.yaml`) is uploaded to the stage and defines:\n- Table structures for SENSOR_READINGS, VEHICLE_TELEMETRY_STREAM, VEHICLE_LOCATIONS, etc.\n- VARIANT column extraction patterns\n- Dimension and measure definitions\n\n\u003E **Note**: If the agent creation fails due to syntax issues, you can configure it manually through **Cortex AI** → **Agents** → **FLEET_ANALYTICS_AGENT**.\n\n#### Using the Fleet Analytics Agent\n\n1. Navigate to **Cortex AI** → **Agents** in the left sidebar\n2. Select **FLEET_ANALYTICS_AGENT**\n3. Start asking questions in natural language:\n\n**Example queries:**\n\n- \"Which vehicles had the highest fuel consumption last week?\"\n- \"Show me all critical maintenance events\"\n- \"What's the average speed by fleet region?\"\n- \"Find vehicles with check engine warnings\"\n- \"Which drivers have the most hard braking events?\"\n- \"Show vehicle health scores below 60\"\n- \"How many vehicles are in each H3 cell in California?\"\n\nThe agent understands your Iceberg table schema including:\n- **VARIANT columns**: Extracts nested JSON fields using colon notation\n- **GEOGRAPHY columns**: Uses H3 and ST_* functions for geospatial analysis\n- **Time-series data**: Performs ASOF joins and window functions\n\n#### Agent Configuration\n\nThe agent was created with access to these tables:\n\n| Table | Description |\n|-------|-------------|\n| `RAW.VEHICLE_TELEMETRY_STREAM` | Real-time streaming telemetry (VARIANT) |\n| `RAW.VEHICLE_LOCATIONS` | Geospatial positions (GEOGRAPHY) |\n| `RAW.SENSOR_READINGS` | High-precision time-series sensor data |\n| `RAW.MAINTENANCE_LOGS` | Maintenance events (VARIANT) |\n| `RAW.VEHICLE_REGISTRY` | Vehicle and driver master data |\n| `ANALYTICS.DAILY_FLEET_SUMMARY` | Daily aggregated metrics |\n| `ANALYTICS.VEHICLE_HEALTH_SCORE` | Calculated health scores |\n\nTo view or modify the agent configuration:\n\n```sql\n-- View agent details\nSHOW AGENTS IN SCHEMA FLEET_ANALYTICS_DB.RAW;\n\n-- Describe the agent\nDESCRIBE AGENT FLEET_ANALYTICS_DB.RAW.FLEET_ANALYTICS_AGENT;\n```\n\nFor more on building agents, see [Snowflake CoWork documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence).\n\n\u003C!-- ------------------------ --\u003E\n## Business Continuity and Disaster Recovery\n\nThis section highlights Snowflake's enterprise-grade, out-of-the-box business continuity and disaster recovery capabilities that extend to Iceberg V3 tables.\n\n\u003E **Note**: Replication features require Business Critical Edition or higher.\n\n### Iceberg Table Replication Overview\n\nSnowflake supports replicating Iceberg tables across regions and clouds, ensuring your data lakehouse is protected against regional outages. See [Iceberg Table Replication documentation](https://docs.snowflake.com/en/user-guide/tables-iceberg-replication) for details.\n\n### Setting Up Replication via UI\n\nFollow these steps to set up cross-region replication for your Iceberg tables:\n\n#### Step 1: Create a Replication Group\n\n1. Navigate to **Admin** → **Accounts**\n2. Click on the **Replication** tab\n3. Click **+ Group** (or **+ Failover Group** for automatic failover)\n\n#### Step 2: Select Target Account\n\n1. Choose the target Snowflake account in a different cloud or region\n2. If no secondary accounts exist, you'll need to create one first\n\n#### Step 3: Configure Objects\n\n1. Select all objects to replicate:\n   - Databases (includes all Iceberg tables)\n   - Warehouses (optional)\n   - Roles and privileges (recommended for consistent access)\n\n\u003E With **Snowflake-managed** Iceberg storage (`SNOWFLAKE_MANAGED`, the lab default), you do not add customer external volumes for table files, and failover/replication groups can use `OBJECT_TYPES = DATABASES` without listing external volumes. See [Replication](https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage#replication) under *Snowflake storage for Apache Iceberg tables*.\n\n#### Step 4: Enable and Monitor\n\n1. Click **Create** to enable replication\n2. Monitor replication status in the Replication tab\n3. View replication lag and sync history\n\n### Verify Replication\n\nIn the secondary account:\n\n```sql\n-- Check that Iceberg tables exist\nUSE DATABASE FLEET_ANALYTICS_DB;\nSHOW ICEBERG TABLES;\n\n-- Query replicated data\nSELECT COUNT(*) FROM VEHICLE_TELEMETRY_STREAM;\nSELECT COUNT(*) FROM MAINTENANCE_LOGS;\n```\n\n### Failover Testing\n\nFor Failover Groups (automatic failover):\n\n1. In the Replication tab, select your failover group\n2. Click **Failover** to initiate failover to secondary\n3. Verify applications can connect to secondary account\n4. Use **Failback** when primary is restored\n\nFor detailed steps, see [Creating Replication/Failover Groups via Snowsight](https://docs.snowflake.com/en/user-guide/account-replication-config#create-a-replication-or-failover-group-using-snowsight).\n\n\u003C!-- ------------------------ --\u003E\n## Interoperability\n\nThis section demonstrates that external engines like Apache Spark can access Iceberg V3 tables managed by Snowflake. Horizon provides temporary, scoped storage credentials and enforces centralized row/column-level access controls for any engine.\n\n### Prerequisites for Spark\n\nEnsure you have:\n- Conda installed\n- The repository cloned (`sfguide-iceberg-v3-comprehensive-guide`)\n\n\u003E External engines still read Iceberg tables that use [Snowflake-managed storage](https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage) through Horizon Catalog. The Spark notebook includes a **Cloud Storage Bundles** section—use the bundle that matches your account’s cloud if you adjust storage or see file I/O errors.\n\n### Start the Spark Environment\n\nThe setup script created a Conda environment with Spark 4.0+ and all necessary dependencies:\n\n```bash\ncd sfquickstarts/site/sfguides/src/iceberg-v3-comprehensive-guide/assets\n\n# Activate the Spark environment\nconda activate fleet-spark\n\n# Start Jupyter notebook\njupyter notebook spark_iceberg_interop.ipynb\n```\n\n### Spark Notebook Walkthrough\n\nThe pre-configured Jupyter notebook contains all the code needed. Here's what you'll do:\n\n#### Cell 1: Setup and Imports\n\n```python\nfrom pyspark.sql import SparkSession\nimport os\n\n# Load configuration (from the same config.env used during setup)\nfrom dotenv import load_dotenv\nload_dotenv('../config.env')\n\nSNOWFLAKE_ACCOUNT = os.getenv('SNOWFLAKE_ACCOUNT')\nSNOWFLAKE_USER = os.getenv('SNOWFLAKE_USER')\n```\n\n#### Cell 2: Create Spark Session with Horizon Catalog\n\n```python\nfrom pyspark.sql import SparkSession\n\n# Configuration\nSNOWFLAKE_PASSWORD = os.getenv('SNOWFLAKE_PASSWORD')\nSNOWFLAKE_ROLE = 'ACCOUNTADMIN'\nSNOWFLAKE_SCHEMA = 'RAW'\nSNOWFLAKE_WAREHOUSE = os.getenv('SNOWFLAKE_WAREHOUSE', 'FLEET_ANALYTICS_WH')\nSF_URL = f\"{SNOWFLAKE_ACCOUNT_URL}.snowflakecomputing.com\"\n\n# Versions - Note: Spark 3.5 required for Snowflake Connector masking support\nSNOWFLAKE_JDBC_VERSION = \"3.24.0\"\nSNOWFLAKE_SPARK_CONNECTOR_VERSION = \"3.1.6\"\n\n# Create Spark session with Iceberg and Snowflake catalog configuration\n# Note: \n# - driver.host and bindAddress ensure Spark uses localhost (avoids VPN issues)\n# - Using Spark 4.0 + Iceberg 1.10.1 for native VARIANT support\nspark = SparkSession.builder \\\n    .appName(\"Fleet Analytics - Iceberg V3 Interop\") \\\n    .master(\"local[*]\") \\\n    .config(\"spark.driver.host\", \"127.0.0.1\") \\\n    .config(\"spark.driver.bindAddress\", \"127.0.0.1\") \\\n    .config(\"spark.jars.packages\", \n            f\"org.apache.iceberg:iceberg-spark-runtime-4.0_{SCALA_VERSION}:{ICEBERG_VERSION},\"\n            f\"org.apache.iceberg:iceberg-aws-bundle:{ICEBERG_VERSION},\"\n            f\"net.snowflake:snowflake-jdbc:{SNOWFLAKE_JDBC_VERSION},\"\n            f\"net.snowflake:spark-snowflake_{SCALA_VERSION}:{SNOWFLAKE_SPARK_CONNECTOR_VERSION}\") \\\n    .config(\"spark.sql.extensions\", \"org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions\") \\\n    .config(\"spark.sql.catalog.horizon\", \"org.apache.iceberg.spark.SparkCatalog\") \\\n    .config(\"spark.sql.catalog.horizon.type\", \"rest\") \\\n    .config(\"spark.sql.catalog.horizon.uri\", f\"https://{SNOWFLAKE_ACCOUNT_URL}.snowflakecomputing.com/polaris/api/catalog\") \\\n    .config(\"spark.sql.catalog.horizon.credential\", SNOWFLAKE_ACCOUNTADMIN_TOKEN) \\\n    .config(\"spark.sql.catalog.horizon.warehouse\", SNOWFLAKE_DATABASE) \\\n    .config(\"spark.sql.catalog.horizon.scope\", f\"session:role:{SNOWFLAKE_ROLE}\") \\\n    .config(\"spark.sql.catalog.horizon.header.X-Iceberg-Access-Delegation\",\"vended-credentials\") \\\n    .config(\"spark.snowflake.sfURL\", SF_URL) \\\n    .config(\"spark.snowflake.sfUser\", os.getenv('SNOWFLAKE_USER')) \\\n    .config(\"spark.snowflake.sfPassword\", SNOWFLAKE_FLEET_ANALYST_TOKEN) \\\n    .config(\"spark.snowflake.sfDatabase\", SNOWFLAKE_DATABASE) \\\n    .config(\"spark.snowflake.sfSchema\", SNOWFLAKE_SCHEMA) \\\n    .config(\"spark.snowflake.sfRole\", SNOWFLAKE_ROLE) \\\n    .config(\"spark.snowflake.sfWarehouse\", SNOWFLAKE_WAREHOUSE) \\\n    .config(\"spark.sql.iceberg.vectorization.enabled\", \"false\") \\\n    .getOrCreate()\n\nprint(\"Spark session created successfully!\")\nprint(f\"Spark version: {spark.version}\")\n```\n\n#### Cell 3: List Available Tables\n\n```python\n# Show all tables visible to Spark\nspark.sql(\"SHOW TABLES IN horizon.RAW\").show(truncate=False)\nspark.sql(\"SHOW TABLES IN horizon.CURATED\").show(truncate=False)\nspark.sql(\"SHOW TABLES IN horizon.ANALYTICS\").show(truncate=False)\n```\n\nOutput:\n```\n+---------+------------------------+-----------+\n|namespace|tableName               |isTemporary|\n+---------+------------------------+-----------+\n|RAW      |API_WEATHER_DATA        |false      |\n|RAW      |MAINTENANCE_LOGS        |false      |\n|RAW      |SENSOR_READINGS         |false      |\n|RAW      |VEHICLE_LOCATIONS       |false      |\n|RAW      |VEHICLE_REGISTRY        |false      |\n|RAW      |VEHICLE_TELEMETRY_STREAM|false      |\n+---------+------------------------+-----------+\n\n+---------+--------------------+-----------+\n|namespace|tableName           |isTemporary|\n+---------+--------------------+-----------+\n|CURATED  |MAINTENANCE_ANALYSIS|false      |\n|CURATED  |TELEMETRY_ENRICHED  |false      |\n+---------+--------------------+-----------+\n\n+---------+--------------------+-----------+\n|namespace|tableName           |isTemporary|\n+---------+--------------------+-----------+\n|ANALYTICS|DAILY_FLEET_SUMMARY |false      |\n|ANALYTICS|VEHICLE_HEALTH_SCORE|false      |\n+---------+--------------------+-----------+\n```\n\nNote that Dynamic Iceberg Tables are also visible!\n\n#### Cell 4: Describe Table with Variant Column\n\n```python\n# See variant column in Iceberg table\nspark.sql(\"DESCRIBE TABLE snowflake.RAW.VEHICLE_TELEMETRY_STREAM\").show(truncate=False)\n```\n\nOutput:\n```\n+-------------------+---------------+-------+\n|col_name           |data_type      |comment|\n+-------------------+---------------+-------+\n|VEHICLE_ID         |string         |null   |\n|EVENT_TIMESTAMP    |timestamp      |null   |\n|TELEMETRY_DATA     |variant        |null   |\n+-------------------+---------------+-------+\n```\n\n#### Cell 5: Query Data with Variant\n\n```python\n# Use Spark's variant_get() function to extract nested fields from VARIANT\n# This query succeeds, and no Snowflake compute is used, since there's no masking policy on the Iceberg table.\ndf = spark.sql(f\"\"\"\n    SELECT \n        VEHICLE_ID,\n        EVENT_TIMESTAMP,\n        variant_get(TELEMETRY_DATA, '$.speed_mph', 'float') AS speed_mph,\n        variant_get(TELEMETRY_DATA, '$.engine.temperature_f', 'int') AS engine_temp,\n        variant_get(TELEMETRY_DATA, '$.location.lat', 'float') AS latitude,\n        variant_get(TELEMETRY_DATA, '$.location.lon', 'float') AS longitude\n    FROM snowflake.RAW.VEHICLE_TELEMETRY_STREAM\n    WHERE variant_get(TELEMETRY_DATA, '$.speed_mph', 'float') \u003E 60\n    LIMIT 10\n\"\"\")\ndf.show()\n```\n\n#### Cell 6: Demonstrate Access Control (Masking Enforcement)\n\n```python\n# To enforce masking policies from Spark, we need the Snowflake Connector for Spark\n# which routes queries through Snowflake for policy evaluation\n# See: https://docs.snowflake.com/en/user-guide/tables-iceberg-query-using-external-query-engine-snowflake-horizon-enforce-access-policies\n\n# Stop the previous Spark session first\nspark.stop()\nfrom pyspark.sql import SparkSession\n\n# Configuration\nSNOWFLAKE_PASSWORD = os.getenv('SNOWFLAKE_PASSWORD')\nSNOWFLAKE_ROLE = 'FLEET_ANALYST'\nSNOWFLAKE_SCHEMA = 'RAW'\nSNOWFLAKE_WAREHOUSE = os.getenv('SNOWFLAKE_WAREHOUSE', 'FLEET_ANALYTICS_WH')\nSF_URL = f\"{SNOWFLAKE_ACCOUNT_URL}.snowflakecomputing.com\"\n\n# Versions - Note: Spark 3.5 required for Snowflake Connector masking support\nSNOWFLAKE_JDBC_VERSION = \"3.24.0\"\nSNOWFLAKE_SPARK_CONNECTOR_VERSION = \"3.1.6\"\n\nspark_analyst = SparkSession.builder \\\n    .appName(\"Fleet Analytics - Analyst View\") \\\n    .master(\"local[*]\") \\\n    .config(\"spark.driver.host\", \"127.0.0.1\") \\\n    .config(\"spark.driver.bindAddress\", \"127.0.0.1\") \\\n    .config(\"spark.jars.packages\", \n            f\"org.apache.iceberg:iceberg-spark-runtime-4.0_{SCALA_VERSION}:{ICEBERG_VERSION},\"\n            f\"org.apache.iceberg:iceberg-aws-bundle:{ICEBERG_VERSION},\"\n            f\"net.snowflake:snowflake-jdbc:{SNOWFLAKE_JDBC_VERSION},\"\n            f\"net.snowflake:spark-snowflake_{SCALA_VERSION}:{SNOWFLAKE_SPARK_CONNECTOR_VERSION}\") \\\n    .config(\"spark.sql.extensions\", \"org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions\") \\\n    .config(\"spark.sql.defaultCatalog\", \"horizon\") \\\n    .config(\"spark.sql.catalog.horizon\", \"org.apache.spark.sql.snowflake.catalog.SnowflakeFallbackCatalog\") \\\n    .config(\"spark.sql.catalog.horizon.catalog-impl\", \"org.apache.iceberg.spark.SparkCatalog\") \\\n    .config(\"spark.sql.catalog.horizon.type\", \"rest\") \\\n    .config(\"spark.sql.catalog.horizon.uri\", f\"https://{SNOWFLAKE_ACCOUNT_URL}.snowflakecomputing.com/polaris/api/catalog\") \\\n    .config(\"spark.sql.catalog.horizon.warehouse\", SNOWFLAKE_DATABASE) \\\n    .config(\"spark.sql.catalog.horizon.scope\", f\"session:role:{SNOWFLAKE_ROLE}\") \\\n    .config(\"spark.sql.catalog.horizon.credential\", SNOWFLAKE_FLEET_ANALYST_TOKEN) \\\n    .config(\"spark.sql.catalog.horizon.io-impl\", \"org.apache.iceberg.aws.s3.S3FileIO\") \\\n    .config(\"spark.sql.catalog.horizon.header.X-Iceberg-Access-Delegation\", \"vended-credentials\") \\\n    .config(\"spark.snowflake.sfURL\", SF_URL) \\\n    .config(\"spark.snowflake.sfUser\", os.getenv('SNOWFLAKE_USER')) \\\n    .config(\"spark.snowflake.sfPassword\", SNOWFLAKE_FLEET_ANALYST_TOKEN) \\\n    .config(\"spark.snowflake.sfDatabase\", SNOWFLAKE_DATABASE) \\\n    .config(\"spark.snowflake.sfSchema\", SNOWFLAKE_SCHEMA) \\\n    .config(\"spark.snowflake.sfRole\", SNOWFLAKE_ROLE) \\\n    .config(\"spark.snowflake.sfWarehouse\", SNOWFLAKE_WAREHOUSE) \\\n    .config(\"spark.sql.iceberg.vectorization.enabled\", \"false\") \\\n    .getOrCreate()\n\nspark_analyst.sparkContext.setLogLevel(\"ERROR\")\n```\n\nIceberg tables are still accessible via Iceberg REST API\n```python\nspark_analyst.sql(\"SHOW TABLES in horizon.RAW\").show(truncate=False)\n```\n\nWhen reading an Iceberg table with a maskign policy, Snowflake returns masked results\n```python\nspark_analyst.sql(\"\"\"\n    SELECT\n        VEHICLE_ID,\n        MAKE,\n        MODEL,\n        YEAR,\n        LICENSE_PLATE,\n        DRIVER_NAME,\n        DRIVER_EMAIL,\n        DRIVER_PHONE,\n        FLEET_REGION\n    FROM horizon.RAW.VEHICLE_REGISTRY\n\"\"\").show(truncate=True)\n```\n\n![Interoperable Masking](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/iceberg-v3-tables-comprehensive-guide/spark_masked.png?v=8cbd55b6)\n\n\nThe masking policies defined in Snowflake are enforced even when accessing data through Spark!\n\nFor more details on Spark integration, see:\n- [Query Iceberg Tables Using External Engines](https://docs.snowflake.com/en/user-guide/tables-iceberg-query-using-external-query-engine-snowflake-horizon)\n- [Enforce Access Policies for External Engines](https://docs.snowflake.com/en/user-guide/tables-iceberg-query-using-external-query-engine-snowflake-horizon-enforce-access-policies)\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\nTo remove all objects created by this guide:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\n-- Drop the database (this removes all tables, schemas, and objects)\nDROP DATABASE IF EXISTS FLEET_ANALYTICS_DB;\n\n-- Drop the external volume only if you used customer-managed storage (USE_SNOWFLAKE_STORAGE=false)\nDROP EXTERNAL VOLUME IF EXISTS FLEET_ICEBERG_VOL;\n\n-- Drop the warehouse\nDROP WAREHOUSE IF EXISTS FLEET_ANALYTICS_WH;\n\n-- Drop roles\nDROP ROLE IF EXISTS FLEET_ANALYST;\nDROP ROLE IF EXISTS FLEET_ENGINEER;\nDROP ROLE IF EXISTS FLEET_ADMIN;\n\n-- If you created replication groups, remove them\nDROP REPLICATION GROUP IF EXISTS FLEET_ANALYTICS_REPLICATION;\n\n-- Remove network policy from user (if applied)\nALTER USER \u003Cyour_username\u003E UNSET NETWORK_POLICY;\nDROP NETWORK POLICY IF EXISTS FLEET_STREAMING_POLICY;\n\n-- External access integration\nDROP INTEGRATION IF EXISTS OPEN_METEO_ACCESS;\n\nSELECT 'Uncomment the commands above to cleanup' AS NOTE;\n```\n\nIf you used customer-managed cloud storage, you may also want to clean up the Iceberg data files in your bucket or container.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nCongratulations! You've built a comprehensive analytics platform using Snowflake Iceberg V3 tables, demonstrating the full power of Snowflake's modern lakehouse architecture.\n\n### What You Learned\n\n- **Iceberg V3 Tables**: Created tables with VARIANT columns for semi-structured data, supporting flexible schemas\n- **Streaming Ingestion**: Used Snowpipe Streaming to ingest real-time vehicle telemetry with sub-second latency\n- **Multi-Source Ingestion**: Loaded data from web APIs and batch JSON files into Iceberg tables\n- **Declarative Pipelines**: Built incremental transformation pipelines with Dynamic Iceberg Tables\n- **Enterprise Governance**: Applied PII detection, masking policies, and data quality monitoring\n- **Advanced Analytics**: Performed time-series analysis, forecasting, and geospatial queries\n- **AI Integration**: Connected Iceberg data to Snowflake CoWork for natural language analytics\n- **Business Continuity**: Configured cross-region replication for disaster recovery\n- **Interoperability**: Queried Iceberg tables from Spark with enforced access controls\n\n### Key Benefits of Iceberg V3 Tables in Snowflake\n\n1. **Open Format**: Data stored in open Apache Iceberg format, accessible by any compatible engine\n2. **VARIANT Support**: Full semi-structured data capabilities with V3 table format\n3. **Unified Governance**: Single place to manage security, quality, and lineage across all engines\n4. **Incremental Processing**: Dynamic Tables automatically handle incremental transformations\n5. **Enterprise Features**: Time travel, cloning, replication all work with Iceberg tables\n6. **Performance**: Snowflake's query optimization applies to Iceberg tables\n\n### Related Resources\n\n**Documentation:**\n- [Snowflake storage for Apache Iceberg tables](https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage)\n- [Iceberg V3 Support in Snowflake](https://docs.snowflake.com/en/LIMITEDACCESS/iceberg/tables-iceberg-v3-specification-support)\n- [Snowpipe Streaming](https://docs.snowflake.com/en/user-guide/snowpipe-streaming/snowpipe-streaming-high-performance-overview)\n- [Dynamic Iceberg Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-create-iceberg)\n- [Snowflake CoWork](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence)\n- [Iceberg Table Replication](https://docs.snowflake.com/en/user-guide/tables-iceberg-replication)\n\n**Guides:**\n- [Build Declarative Pipelines with Dynamic Iceberg Tables](/en/developers/guides/declarative-pipelines-using-dynamic-iceberg-tables/)\n- [Getting Started with Data Quality in Snowflake](/en/developers/guides/getting-started-with-data-quality-in-snowflake/)\n\n**Assets:**\n- [Guide Assets Folder](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/iceberg-v3-tables-comprehensive-guide/assets)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-b310b4e00a","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-f3f3df898c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-2b067271eb","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-04-22",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-eec628f1a8","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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