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engines like Spark, DuckDB, and Flink — while Snowflake handles storage, optimization, and governance for you. With Snowflake-managed Iceberg storage, you don't need to configure a cloud bucket or external volume. Snowflake stores and manages all Iceberg data and metadata files internally, so you can focus on building pipelines and analytics rather than infrastructure.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Snowflake-managed Iceberg tables can be stored in your own cloud storage bucket or in Snowflake-managed storage. For simplicity, this guide uses Snowflake-managed storage so you can get started without any external cloud configuration.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn this Quickstart, you'll build a fleet analytics pipeline using Snowflake-managed Iceberg tables. You'll work with a fictitious fleet management company that collects vehicle telemetry, sensor readings, and maintenance logs. Along the way, you'll stream data, create declarative transformation pipelines, power an AI agent with your data, and prove interoperability by querying the same tables from DuckDB and Apache Spark — without moving a single byte of data.\u003C/p\u003E\n","\u003Cp\u003ELet's get started!\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create Snowflake-managed Iceberg tables with \u003Ccode\u003EEXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to stream semi-structured data into Iceberg tables using a Python script\u003C/li\u003E\u003Cli\u003EHow to batch-load JSON files into Iceberg tables with \u003Ccode\u003ECOPY INTO\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to build declarative transformation pipelines with Dynamic Iceberg Tables\u003C/li\u003E\u003Cli\u003EHow to query Iceberg data with natural language using Snowflake CoWork\u003C/li\u003E\u003Cli\u003EHow to read Snowflake-managed Iceberg tables from DuckDB via Horizon Catalog\u003C/li\u003E\u003Cli\u003EHow to read Snowflake-managed Iceberg tables from Apache Spark via Horizon Catalog\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account (\u003Ca href=\"https://signup.snowflake.com/developers\"\u003Etrial\u003C/a\u003E, or otherwise) on \u003Cstrong\u003EAWS\u003C/strong\u003E or \u003Cstrong\u003EAzure\u003C/strong\u003E (Snowflake-managed Iceberg storage is not available on Google Cloud)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E access\u003C/li\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+\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake-managed Iceberg tables for streaming telemetry, sensor readings, vehicle data, and maintenance logs\u003C/li\u003E\u003Cli\u003EA Dynamic Iceberg Table pipeline for automated incremental transformations\u003C/li\u003E\u003Cli\u003EA Cortex Agent for natural language querying of fleet data via Snowflake CoWork\u003C/li\u003E\u003Cli\u003EDuckDB and Spark notebooks demonstrating cross-engine interoperability on the same Iceberg tables\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up Environment\u003C/h2\u003E\n","\u003Cp\u003EThis section guides you through setting up your environment. We'll download the companion assets, configure a few variables, and run a setup script that creates all the Snowflake objects we need.\u003C/p\u003E\n","\u003Ch3\u003EDownload the Assets\u003C/h3\u003E\n","\u003Cp\u003EDownload the companion assets from the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/get-started-snowflake-managed-iceberg-tables/assets\"\u003Eassets folder\u003C/a\u003E and place them in a local directory. Then navigate into that directory in your terminal.\u003C/p\u003E\n","\u003Ch3\u003EConfigure Variables\u003C/h3\u003E\n","\u003Cp\u003ECopy the template to create your configuration file:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecp config.env.template config.env\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen edit \u003Cstrong\u003Econfig.env\u003C/strong\u003E with your values:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Required: Snowflake CLI connection name (run `snow connection list` to see yours)\nSNOWFLAKE_CONNECTION="default"\n\n# Required: Account identifier and username (used by DuckDB/Spark interop scripts)\n# Find yours: SELECT CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME();\nSNOWFLAKE_ACCOUNT="your_account_identifier"\nSNOWFLAKE_USER="your_username"\n\n# Required: Programmatic Access Token (PAT)\n# Create one at: User Menu → My Profile → Programmatic Access Tokens\nSNOWFLAKE_PAT=""\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe streaming script uses \u003Ccode\u003ESNOWFLAKE_CONNECTION\u003C/code\u003E to connect via your existing Snowflake CLI configuration in \u003Ccode\u003E~/.snowflake/config.toml\u003C/code\u003E — no need to duplicate credentials. The account and user fields are only needed by the DuckDB and Spark interoperability scripts to construct the Horizon Catalog endpoint URL.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Make sure your Snowflake CLI connection is configured. If you haven't set one up yet, run:\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","\u003Ch3\u003ERun Setup Script\u003C/h3\u003E\n","\u003Cp\u003EThe setup script creates all the Snowflake objects we'll use throughout this Quickstart:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Echmod +x setup.sh\n./setup.sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the setup script does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreates a database called \u003Cstrong\u003EFLEET_DB\u003C/strong\u003E with two schemas: \u003Cstrong\u003ERAW\u003C/strong\u003E and \u003Cstrong\u003EANALYTICS\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ECreates a warehouse called \u003Cstrong\u003EFLEET_WH\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESets the database default external volume to \u003Ccode\u003ESNOWFLAKE_MANAGED\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECreates Snowflake-managed Iceberg tables for vehicle telemetry, sensor readings, vehicle registry, and maintenance logs\u003C/li\u003E\u003Cli\u003ECreates Dynamic Iceberg Tables for transformation pipelines\u003C/li\u003E\u003Cli\u003ELoads sample data into the vehicle registry and sensor readings tables\u003C/li\u003E\u003Cli\u003EUploads JSON maintenance log files to an internal stage\u003C/li\u003E\u003Cli\u003ESets up a Python virtual environment for the streaming script\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVerify Setup\u003C/h3\u003E\n","\u003Cp\u003EAfter the script completes, verify that the objects were created:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE FLEET_DB;\nSHOW ICEBERG TABLES IN SCHEMA RAW;\nSHOW DYNAMIC TABLES IN SCHEMA ANALYTICS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see four Iceberg tables in the \u003Cstrong\u003ERAW\u003C/strong\u003E schema and two Dynamic Tables in the \u003Cstrong\u003EANALYTICS\u003C/strong\u003E schema.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Snowflake-Managed Iceberg Tables\u003C/h2\u003E\n","\u003Cp\u003ELet's take a closer look at the Iceberg tables the setup script created. The DDL for these tables lives in \u003Cstrong\u003E02_create_iceberg_tables.sql\u003C/strong\u003E. Understanding it is important because it shows how simple it is to create Iceberg tables with Snowflake-managed storage.\u003C/p\u003E\n","\u003Ch3\u003EWhat Makes These Tables "Iceberg"?\u003C/h3\u003E\n","\u003Cp\u003ETwo parameters in the \u003Ccode\u003ECREATE ICEBERG TABLE\u003C/code\u003E statement control the storage and catalog:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003ECATALOG = 'SNOWFLAKE'\u003C/code\u003E\u003C/strong\u003E — Snowflake manages the Iceberg metadata (catalog entries, snapshots, manifest files)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EEXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\u003C/code\u003E\u003C/strong\u003E — Snowflake stores the Iceberg data files internally, just like standard Snowflake tables\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETogether, these mean you get the open Iceberg format — readable by any engine that speaks the Iceberg REST protocol — without managing cloud storage yourself.\u003C/p\u003E\n","\u003Ch3\u003EVehicle Telemetry (Streaming Target)\u003C/h3\u003E\n","\u003Cp\u003EThis table receives real-time streaming vehicle telemetry. It uses a \u003Ccode\u003EVARIANT\u003C/code\u003E column for the flexible-schema telemetry payload:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ICEBERG TABLE FLEET_DB.RAW.VEHICLE_TELEMETRY_STREAM (\n    VEHICLE_ID STRING NOT NULL,\n    EVENT_TIMESTAMP TIMESTAMP_NTZ(6) NOT NULL,\n    TELEMETRY_DATA VARIANT NOT NULL,\n    INGESTED_AT TIMESTAMP_LTZ(6)\n)\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EVARIANT\u003C/code\u003E column is key here. It lets us stream semi-structured JSON telemetry without defining a rigid schema upfront, while still supporting efficient querying with dot notation (e.g., \u003Ccode\u003ETELEMETRY_DATA:speed_mph\u003C/code\u003E).\u003C/p\u003E\n","\u003Ch3\u003EVehicle Registry (Master Data)\u003C/h3\u003E\n","\u003Cp\u003EThis table holds master data about vehicles and drivers:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ICEBERG TABLE FLEET_DB.RAW.VEHICLE_REGISTRY (\n    VEHICLE_ID STRING NOT NULL,\n    VIN STRING,\n    MAKE STRING,\n    MODEL STRING,\n    YEAR INT,\n    LICENSE_PLATE STRING,\n    DRIVER_NAME STRING,\n    DRIVER_EMAIL STRING,\n    FLEET_REGION STRING,\n    VEHICLE_STATUS STRING DEFAULT 'ACTIVE',\n    REGISTRATION_DATE DATE,\n    LAST_SERVICE_DATE DATE\n)\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESensor Readings (Time-Series)\u003C/h3\u003E\n","\u003Cp\u003EThis table stores high-frequency sensor data for time-series analysis:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ICEBERG TABLE FLEET_DB.RAW.SENSOR_READINGS (\n    READING_ID STRING NOT NULL,\n    VEHICLE_ID STRING NOT NULL,\n    READING_TIMESTAMP TIMESTAMP_NTZ(6) NOT NULL,\n    ENGINE_TEMP_F FLOAT,\n    OIL_PRESSURE_PSI FLOAT,\n    BATTERY_VOLTAGE FLOAT,\n    FUEL_CONSUMPTION_GPH FLOAT,\n    ODOMETER_MILES FLOAT\n)\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMaintenance Logs (Batch JSON)\u003C/h3\u003E\n","\u003Cp\u003EThis table will hold JSON maintenance logs loaded from files:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ICEBERG TABLE FLEET_DB.RAW.MAINTENANCE_LOGS (\n    LOG_ID STRING NOT NULL,\n    VEHICLE_ID STRING NOT NULL,\n    LOG_TIMESTAMP TIMESTAMP_NTZ(6) NOT NULL,\n    LOG_DATA VARIANT NOT NULL,\n    SOURCE_FILE STRING\n)\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEvery one of these tables is a proper Apache Iceberg table stored in the open format — yet creating them requires zero cloud infrastructure setup on your part.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoad Data\u003C/h2\u003E\n","\u003Cp\u003ENow let's populate our tables. The setup script already loaded sample data into the vehicle registry and sensor readings tables. In this step, we'll load JSON maintenance logs from staged files.\u003C/p\u003E\n","\u003Ch3\u003EVerify Pre-Loaded Data\u003C/h3\u003E\n","\u003Cp\u003EThe setup script inserted sample data using \u003Ccode\u003EINSERT ... SELECT\u003C/code\u003E with Snowflake's \u003Ccode\u003EGENERATOR\u003C/code\u003E function. Let's verify:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE FLEET_DB;\nUSE WAREHOUSE FLEET_WH;\n\nSELECT 'VEHICLE_REGISTRY' AS table_name, COUNT(*) AS row_count FROM RAW.VEHICLE_REGISTRY\nUNION ALL\nSELECT 'SENSOR_READINGS', COUNT(*) FROM RAW.SENSOR_READINGS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see 100 vehicles in the registry and 10,000 sensor readings.\u003C/p\u003E\n","\u003Ch3\u003ELoad JSON Maintenance Logs\u003C/h3\u003E\n","\u003Cp\u003EThe setup script uploaded JSON maintenance log files to an internal stage. Let's load them into the \u003Cstrong\u003EMAINTENANCE_LOGS\u003C/strong\u003E Iceberg table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- View the staged files\nLIST @RAW.LOGS_STAGE;\n\n-- Load the JSON files into the Iceberg table\nCOPY INTO RAW.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 @RAW.LOGS_STAGE\n)\nFILE_FORMAT = (TYPE = 'JSON' STRIP_OUTER_ARRAY = TRUE)\nPATTERN = '.*maintenance_log.*\\.json';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the code does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELists the staged JSON files to confirm they're present\u003C/li\u003E\u003Cli\u003EUses \u003Ccode\u003ECOPY INTO\u003C/code\u003E with a subquery to extract typed fields from the raw JSON (\u003Ccode\u003E$1\u003C/code\u003E refers to the entire JSON object in each file)\u003C/li\u003E\u003Cli\u003EStores the full JSON payload in the \u003Ccode\u003ELOG_DATA\u003C/code\u003E VARIANT column for flexible querying\u003C/li\u003E\u003Cli\u003ECaptures the source file name in \u003Ccode\u003ESOURCE_FILE\u003C/code\u003E for lineage tracking\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ELet's verify the load and query the VARIANT data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Verify and query the loaded logs\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 RAW.MAINTENANCE_LOGS\nORDER BY LOG_TIMESTAMP DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see maintenance events like oil changes, tire rotations, and engine diagnostics — all extracted from the VARIANT column with dot notation.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStream Data\u003C/h2\u003E\n","\u003Cp\u003ELet's bring real-time data into our Iceberg table. We'll use a Python script that simulates vehicle telemetry and inserts it directly into the \u003Cstrong\u003EVEHICLE_TELEMETRY_STREAM\u003C/strong\u003E Iceberg table.\u003C/p\u003E\n","\u003Ch3\u003EStart the Streaming Script\u003C/h3\u003E\n","\u003Cp\u003EOpen a terminal and navigate to the assets directory:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd get-started-snowflake-managed-iceberg-tables/assets\n\n# Activate the Python environment created by setup.sh\nsource fleet_venv/bin/activate\n\n# Start the streaming simulation (runs for 5 minutes by default)\npython stream_telemetry.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe script simulates a fleet of 50 vehicles, each emitting telemetry events containing speed, location, engine metrics, and driver behavior data. Each event is a JSON payload inserted into the \u003Ccode\u003ETELEMETRY_DATA\u003C/code\u003E VARIANT column.\u003C/p\u003E\n","\u003Cp\u003EHere's an example of what each streaming event looks like:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\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\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWatch Data Flow In\u003C/h3\u003E\n","\u003Cp\u003EWhile the streaming script runs, open a SQL worksheet and query the table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check current count of streaming events\nSELECT COUNT(*) AS event_count FROM RAW.VEHICLE_TELEMETRY_STREAM;\n\n-- View the latest events, extracting fields from the VARIANT column\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 RAW.VEHICLE_TELEMETRY_STREAM\nORDER BY EVENT_TIMESTAMP DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe data is immediately queryable! Snowflake's dot notation on VARIANT columns makes it easy to extract nested fields from the JSON telemetry payload — no schema flattening required.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip\u003C/strong\u003E: You can stop the streaming script at any time with \u003Cstrong\u003ECtrl+C\u003C/strong\u003E. The data already streamed will remain in the table.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETransform with Dynamic Iceberg Tables\u003C/h2\u003E\n","\u003Cp\u003EWith data flowing into our raw tables, let's build a transformation pipeline using Dynamic Iceberg Tables. Dynamic Tables let you define transformations declaratively — you write a \u003Ccode\u003ESELECT\u003C/code\u003E statement that describes the output, and Snowflake handles incremental refresh, dependency management, and scheduling automatically.\u003C/p\u003E\n","\u003Cp\u003EThe setup script created two Dynamic Iceberg Tables. Let's explore them.\u003C/p\u003E\n","\u003Ch3\u003ETier 1: Enriched Telemetry\u003C/h3\u003E\n","\u003Cp\u003EThis Dynamic Iceberg Table joins streaming telemetry with the vehicle registry to add driver and vehicle details:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DYNAMIC ICEBERG TABLE FLEET_DB.ANALYTICS.TELEMETRY_ENRICHED\n    TARGET_LAG = '10 minutes'\n    WAREHOUSE = FLEET_WH\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE'\nAS\nSELECT\n    t.VEHICLE_ID,\n    t.EVENT_TIMESTAMP,\n    t.TELEMETRY_DATA:speed_mph::FLOAT AS speed_mph,\n    t.TELEMETRY_DATA:engine:temperature_f::INT AS engine_temp_f,\n    t.TELEMETRY_DATA:engine:fuel_level_pct::FLOAT AS fuel_level_pct,\n    t.TELEMETRY_DATA:diagnostics:check_engine::BOOLEAN AS check_engine,\n    CASE\n        WHEN t.TELEMETRY_DATA:engine:temperature_f::INT > 230 THEN 'CRITICAL'\n        WHEN t.TELEMETRY_DATA:engine:temperature_f::INT > 210 THEN 'WARNING'\n        ELSE 'NORMAL'\n    END AS engine_health_status,\n    v.MAKE,\n    v.MODEL,\n    v.DRIVER_NAME,\n    v.FLEET_REGION\nFROM FLEET_DB.RAW.VEHICLE_TELEMETRY_STREAM t\nINNER JOIN FLEET_DB.RAW.VEHICLE_REGISTRY v\n    ON t.VEHICLE_ID = v.VEHICLE_ID;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the code does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EExtracts key fields from the VARIANT telemetry payload using dot notation\u003C/li\u003E\u003Cli\u003EClassifies engine health into NORMAL, WARNING, or CRITICAL based on temperature thresholds\u003C/li\u003E\u003Cli\u003EJoins with the vehicle registry to enrich each event with make, model, driver, and region\u003C/li\u003E\u003Cli\u003ERefreshes automatically within 10 minutes of source data changes thanks to \u003Ccode\u003ETARGET_LAG\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ETier 2: Daily Fleet Summary\u003C/h3\u003E\n","\u003Cp\u003EThis Dynamic Iceberg Table aggregates the enriched telemetry into daily business metrics:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DYNAMIC ICEBERG TABLE FLEET_DB.ANALYTICS.DAILY_FLEET_SUMMARY\n    TARGET_LAG = 'DOWNSTREAM'\n    WAREHOUSE = FLEET_WH\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE'\nAS\nSELECT\n    DATE(EVENT_TIMESTAMP) AS summary_date,\n    FLEET_REGION,\n    COUNT(*) AS total_events,\n    COUNT(DISTINCT VEHICLE_ID) AS active_vehicles,\n    ROUND(AVG(speed_mph), 1) AS avg_speed_mph,\n    ROUND(AVG(engine_temp_f), 1) AS avg_engine_temp_f,\n    ROUND(AVG(fuel_level_pct), 1) AS avg_fuel_level_pct,\n    SUM(CASE WHEN engine_health_status = 'CRITICAL' THEN 1 ELSE 0 END) AS critical_events,\n    SUM(CASE WHEN check_engine THEN 1 ELSE 0 END) AS check_engine_events\nFROM FLEET_DB.ANALYTICS.TELEMETRY_ENRICHED\nGROUP BY DATE(EVENT_TIMESTAMP), FLEET_REGION;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote the \u003Ccode\u003ETARGET_LAG = 'DOWNSTREAM'\u003C/code\u003E. This means the table refreshes only when a downstream consumer needs fresh data, creating an efficient pull-based refresh model.\u003C/p\u003E\n","\u003Ch3\u003ETrigger and Observe a Refresh\u003C/h3\u003E\n","\u003Cp\u003ELet's manually trigger a refresh to see incremental processing in action:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Refresh the pipeline\nALTER DYNAMIC TABLE FLEET_DB.ANALYTICS.TELEMETRY_ENRICHED REFRESH;\nALTER DYNAMIC TABLE FLEET_DB.ANALYTICS.DAILY_FLEET_SUMMARY REFRESH;\n\n-- Check refresh history — look at the refresh_action column\nSELECT name, refresh_action, state, refresh_start_time\nFROM TABLE(FLEET_DB.INFORMATION_SCHEMA.DYNAMIC_TABLE_REFRESH_HISTORY())\nWHERE name IN ('TELEMETRY_ENRICHED', 'DAILY_FLEET_SUMMARY')\nORDER BY refresh_start_time DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELook at the \u003Ccode\u003Erefresh_action\u003C/code\u003E column. After the initial full refresh, subsequent refreshes should show \u003Cstrong\u003EINCREMENTAL\u003C/strong\u003E, meaning Snowflake processed only the new data — not the entire table.\u003C/p\u003E\n","\u003Ch3\u003EQuery the Transformed Data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Enriched telemetry with vehicle details\nSELECT\n    VEHICLE_ID, MAKE, MODEL, DRIVER_NAME, FLEET_REGION,\n    speed_mph, engine_temp_f, engine_health_status, EVENT_TIMESTAMP\nFROM ANALYTICS.TELEMETRY_ENRICHED\nWHERE engine_health_status != 'NORMAL'\nORDER BY EVENT_TIMESTAMP DESC\nLIMIT 10;\n\n-- Daily fleet summary\nSELECT *\nFROM ANALYTICS.DAILY_FLEET_SUMMARY\nORDER BY summary_date DESC, FLEET_REGION\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou've now built a two-tier declarative pipeline on top of Snowflake-managed Iceberg tables. Snowflake handles all the incremental processing and dependency management for you.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuery with Snowflake CoWork\u003C/h2\u003E\n","\u003Cp\u003ELet's make this data accessible to business users who may not know SQL. We'll create a Cortex Agent that lets anyone ask natural language questions about the fleet data through Snowflake CoWork.\u003C/p\u003E\n","\u003Ch3\u003ECreate a Semantic View\u003C/h3\u003E\n","\u003Cp\u003EFirst, we'll create a semantic view that describes our data in business terms. Rather than writing this manually, we'll use Snowflake's AI-powered UI to generate it.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EIn Snowsight, navigate to \u003Cstrong\u003EAI & ML → Analyst\u003C/strong\u003E in the left navigation menu\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ECreate New: Semantic View\u003C/strong\u003E in the top right\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the dialog:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EName\u003C/strong\u003E: Enter \u003Ccode\u003Efleet_semantic_view\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELocation\u003C/strong\u003E: Select database \u003Cstrong\u003EFLEET_DB\u003C/strong\u003E and schema \u003Cstrong\u003EANALYTICS\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E and skip the "Provide context" step\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the following tables:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EANALYTICS.TELEMETRY_ENRICHED\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EANALYTICS.DAILY_FLEET_SUMMARY\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ERAW.VEHICLE_REGISTRY\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ERAW.SENSOR_READINGS\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E, select all columns, and click \u003Cstrong\u003ECreate and Save\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ECortex Analyst will analyze the tables and automatically generate descriptions, logical names, and relationships. This takes a few moments.\u003C/p\u003E\n","\u003Ch3\u003ECreate a Cortex Agent\u003C/h3\u003E\n","\u003Cp\u003ENow let's create an agent that uses the semantic view:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EAI & ML → Agents\u003C/strong\u003E in the left navigation menu\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ECreate agent\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the dialog:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E: Select \u003Cstrong\u003EFLEET_DB\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema\u003C/strong\u003E: Select \u003Cstrong\u003EANALYTICS\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAgent object name\u003C/strong\u003E: Enter \u003Ccode\u003Efleet_agent\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDisplay name\u003C/strong\u003E: Enter \u003Ccode\u003EFleet Analytics Agent\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ECreate\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick the \u003Cstrong\u003ETools\u003C/strong\u003E tab, then click \u003Cstrong\u003EAdd+\u003C/strong\u003E next to \u003Cstrong\u003ECortex Analyst\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the semantic view we just created: \u003Cstrong\u003EFLEET_DB.ANALYTICS.FLEET_SEMANTIC_VIEW\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EEnter a name for the tool and click \u003Cstrong\u003EGenerate with Cortex\u003C/strong\u003E for the description\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EAdd\u003C/strong\u003E, then \u003Cstrong\u003ESave\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EAsk Questions in Snowflake CoWork\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EAI & ML → Agents\u003C/strong\u003E and click the \u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E tab\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EAdd existing agent\u003C/strong\u003E, then select the \u003Cstrong\u003Efleet_agent\u003C/strong\u003E you just created\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOnce added, open \u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E from the left navigation menu and select the \u003Cstrong\u003EFleet Analytics Agent\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ETry asking questions like:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003E"What are the top 5 fleet regions by average speed?"\u003C/li\u003E\u003Cli\u003E"Show me vehicles with critical engine health events"\u003C/li\u003E\u003Cli\u003E"How many check engine events occurred per region today?"\u003C/li\u003E\u003Cli\u003E"Which drivers have the most telemetry events?"\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe agent translates natural language into SQL, executes it against your Iceberg tables, and returns results with charts and explanations.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: If you see a region availability error, enable cross-region inference:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuery from DuckDB\u003C/h2\u003E\n","\u003Cp\u003EHere's where interoperability gets exciting. Because our tables are stored in the open Apache Iceberg format, external engines can read them directly through Snowflake's Horizon Catalog — without copying data, without ETL, without Snowflake compute.\u003C/p\u003E\n","\u003Cp\u003EDuckDB is a lightweight, in-process analytical database. It supports the Iceberg REST Catalog protocol, which means it can connect to Horizon Catalog and read our Snowflake-managed Iceberg tables.\u003C/p\u003E\n","\u003Ch3\u003EInstall DuckDB\u003C/h3\u003E\n","\u003Cp\u003EIf you've deactivated the virtual environment, reactivate it first (\u003Ccode\u003Esource fleet_venv/bin/activate\u003C/code\u003E). Then install DuckDB:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install duckdb requests\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun the DuckDB Interoperability Script\u003C/h3\u003E\n","\u003Cp\u003EThe companion script \u003Cstrong\u003Educkdb_interop.py\u003C/strong\u003E connects DuckDB to your Snowflake-managed Iceberg tables via Horizon Catalog. It reads your connection details and PAT from \u003Cstrong\u003Econfig.env\u003C/strong\u003E, so no extra configuration is needed:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython duckdb_interop.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the script does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EInstalls and loads the DuckDB Iceberg extension\u003C/li\u003E\u003Cli\u003ECreates a bearer token secret using your Snowflake PAT\u003C/li\u003E\u003Cli\u003EAttaches to your Snowflake database through the Horizon Iceberg REST Catalog API\u003C/li\u003E\u003Cli\u003EThe \u003Ccode\u003EENDPOINT\u003C/code\u003E points to Snowflake's built-in Polaris endpoint — no separate catalog server needed\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe script runs several queries to demonstrate interoperability. Here's a look at what it executes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# List tables visible through Horizon Catalog\nconn.execute("SHOW TABLES IN horizon.RAW").show()\n\n# Query the vehicle registry\nconn.execute("""\n    SELECT VEHICLE_ID, MAKE, MODEL, YEAR, FLEET_REGION\n    FROM horizon.RAW.VEHICLE_REGISTRY\n    LIMIT 10\n""").show()\n\n# Aggregate sensor readings by vehicle\nconn.execute("""\n    SELECT\n        VEHICLE_ID,\n        COUNT(*) AS reading_count,\n        ROUND(AVG(ENGINE_TEMP_F), 1) AS avg_engine_temp,\n        ROUND(AVG(FUEL_CONSUMPTION_GPH), 2) AS avg_fuel_gph\n    FROM horizon.RAW.SENSOR_READINGS\n    GROUP BY VEHICLE_ID\n    ORDER BY avg_fuel_gph DESC\n    LIMIT 10\n""").show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDuckDB is reading directly from the Iceberg files managed by Snowflake. No data was copied. No Snowflake warehouse was used. The data is in an open format, and any engine that speaks Iceberg can read it.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip\u003C/strong\u003E: DuckDB reads VARIANT columns as strings. You can use DuckDB's \u003Ccode\u003Ejson_extract\u003C/code\u003E function to parse nested fields from VARIANT data.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThis is the core value proposition of Snowflake-managed Iceberg tables: your data lives in an open format managed by Snowflake, but it's accessible from any compatible engine.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuery from Apache Spark\u003C/h2\u003E\n","\u003Cp\u003EFor teams that rely on Apache Spark, the same Horizon Catalog endpoint works with Spark's Iceberg REST catalog integration. Spark 4.0+ adds native VARIANT support, so you get full access to the semi-structured telemetry data.\u003C/p\u003E\n","\u003Ch3\u003ESet Up the Spark Environment\u003C/h3\u003E\n","\u003Cp\u003EThe setup script created a Conda environment with Spark and all necessary dependencies. Deactivate the virtual environment if it's still active, then activate the Spark environment, install dependencies, and launch the notebook:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edeactivate\nconda activate fleet-spark\npip install pyspark==4.0.0 jupyter python-dotenv requests\njupyter notebook spark_iceberg_interop.ipynb\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: PySpark 4.0 requires \u003Cstrong\u003EJava 17 or 21\u003C/strong\u003E. Java 25 is not compatible. Check with \u003Ccode\u003Ejava -version\u003C/code\u003E before running the notebook.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EConnect Spark to Horizon Catalog\u003C/h3\u003E\n","\u003Cp\u003EThe pre-configured notebook creates a Spark session with the Horizon Catalog:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom pyspark.sql import SparkSession\n\n# Configuration\nSNOWFLAKE_ACCOUNT_URL = "your-org-your-account"\nSNOWFLAKE_PAT = "your-programmatic-access-token"\nSNOWFLAKE_DATABASE = "FLEET_DB"\nSNOWFLAKE_ROLE = "ACCOUNTADMIN"\n\nICEBERG_VERSION = "1.10.1"\nSCALA_VERSION = "2.13"\n\nspark = SparkSession.builder \\\n    .appName("Fleet Analytics - Iceberg 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    .config("spark.sql.extensions",\n            "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",\n            f"https://{SNOWFLAKE_ACCOUNT_URL}.snowflakecomputing.com/polaris/api/catalog") \\\n    .config("spark.sql.catalog.horizon.credential", SNOWFLAKE_PAT) \\\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",\n            "vended-credentials") \\\n    .config("spark.sql.iceberg.vectorization.enabled", "false") \\\n    .getOrCreate()\n\nprint(f"Spark {spark.version} connected to Horizon Catalog!")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the code does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreates a local Spark session with Iceberg extensions\u003C/li\u003E\u003Cli\u003EConfigures the \u003Ccode\u003Ehorizon\u003C/code\u003E catalog to use Snowflake's Iceberg REST API endpoint\u003C/li\u003E\u003Cli\u003EUses vended credentials so Spark can access the underlying Iceberg files without direct cloud storage permissions\u003C/li\u003E\u003Cli\u003EThe \u003Ccode\u003Escope\u003C/code\u003E parameter ensures Spark uses your Snowflake role for access control\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EList and Query Tables\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Show all tables visible through Horizon\nspark.sql("SHOW TABLES IN horizon.RAW").show(truncate=False)\n\n# Query the vehicle registry\nspark.sql("""\n    SELECT VEHICLE_ID, MAKE, MODEL, YEAR, FLEET_REGION\n    FROM horizon.RAW.VEHICLE_REGISTRY\n    LIMIT 10\n""").show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EQuery VARIANT Data with Spark 4.0\u003C/h3\u003E\n","\u003Cp\u003ESpark 4.0 supports the Iceberg V3 VARIANT type natively. Use \u003Ccode\u003Evariant_get()\u003C/code\u003E to extract nested fields:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Extract nested fields from the VARIANT telemetry payload\ndf = spark.sql("""\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, '$.location.lon', 'float') AS longitude\n    FROM horizon.RAW.VEHICLE_TELEMETRY_STREAM\n    WHERE variant_get(TELEMETRY_DATA, '$.speed_mph', 'float') > 60\n    LIMIT 10\n""")\ndf.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESpark is reading the same Iceberg tables that Snowflake manages, including the semi-structured VARIANT data. No data movement, no ETL, no duplication.\u003C/p\u003E\n","\u003Ch3\u003EDynamic Iceberg Tables Are Visible Too\u003C/h3\u003E\n","\u003Cp\u003EDynamic Iceberg Tables appear as regular Iceberg tables to external engines:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Query the Dynamic Iceberg Table\nspark.sql("SHOW TABLES IN horizon.ANALYTICS").show(truncate=False)\n\nspark.sql("""\n    SELECT *\n    FROM horizon.ANALYTICS.DAILY_FLEET_SUMMARY\n    ORDER BY summary_date DESC\n    LIMIT 10\n""").show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is a powerful pattern: Snowflake handles the transformation pipeline (Dynamic Tables), and any engine can read the results.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Cp\u003ELet's clean up your Snowflake account. Run the following in a SQL worksheet:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\n-- Drop the database (removes all tables, dynamic tables, stages, etc.)\nDROP DATABASE IF EXISTS FLEET_DB;\n\n-- Drop the warehouse\nDROP WAREHOUSE IF EXISTS FLEET_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you created a Cortex Agent, you can remove it from the \u003Cstrong\u003EAI & ML → Agents\u003C/strong\u003E page in Snowsight before dropping the database.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You built a fleet analytics pipeline on Snowflake-managed Iceberg tables and proved that the same data is accessible from DuckDB and Apache Spark — without configuring cloud storage, without moving data, and without giving up Snowflake's governance and performance.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECreating Snowflake-managed Iceberg tables with \u003Ccode\u003EEXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\u003C/code\u003E — no cloud bucket required\u003C/li\u003E\u003Cli\u003EStreaming semi-structured JSON data into VARIANT columns in Iceberg tables\u003C/li\u003E\u003Cli\u003EBatch-loading JSON files into Iceberg tables with \u003Ccode\u003ECOPY INTO\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EBuilding incremental transformation pipelines with Dynamic Iceberg Tables\u003C/li\u003E\u003Cli\u003EQuerying fleet data with natural language through a Cortex Agent in Snowflake CoWork\u003C/li\u003E\u003Cli\u003EReading Snowflake-managed Iceberg tables from DuckDB via Horizon Catalog\u003C/li\u003E\u003Cli\u003EReading Snowflake-managed Iceberg tables from Apache Spark via Horizon Catalog, including VARIANT data with \u003Ccode\u003Evariant_get()\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\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/user-guide/tables-iceberg-query-using-external-query-engine-snowflake-horizon\"\u003EQuery Iceberg Tables with an External Engine through Horizon Catalog\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/snowpipe-streaming/snowpipe-streaming-high-performance-overview\"\u003ESnowpipe Streaming\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Get Started with Iceberg Tables on Snowflake-Managed Storage",":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nApache Iceberg tables in Snowflake give you the benefits of an open table format — interoperability with engines like Spark, DuckDB, and Flink — while Snowflake handles storage, optimization, and governance for you. With Snowflake-managed Iceberg storage, you don't need to configure a cloud bucket or external volume. Snowflake stores and manages all Iceberg data and metadata files internally, so you can focus on building pipelines and analytics rather than infrastructure.\n\n\u003E **Note**: Snowflake-managed Iceberg tables can be stored in your own cloud storage bucket or in Snowflake-managed storage. For simplicity, this guide uses Snowflake-managed storage so you can get started without any external cloud configuration.\n\nIn this Quickstart, you'll build a fleet analytics pipeline using Snowflake-managed Iceberg tables. You'll work with a fictitious fleet management company that collects vehicle telemetry, sensor readings, and maintenance logs. Along the way, you'll stream data, create declarative transformation pipelines, power an AI agent with your data, and prove interoperability by querying the same tables from DuckDB and Apache Spark — without moving a single byte of data.\n\nLet's get started!\n\n### What You'll Learn\n\n- How to create Snowflake-managed Iceberg tables with `EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'`\n- How to stream semi-structured data into Iceberg tables using a Python script\n- How to batch-load JSON files into Iceberg tables with `COPY INTO`\n- How to build declarative transformation pipelines with Dynamic Iceberg Tables\n- How to query Iceberg data with natural language using Snowflake CoWork\n- How to read Snowflake-managed Iceberg tables from DuckDB via Horizon Catalog\n- How to read Snowflake-managed Iceberg tables from Apache Spark via Horizon Catalog\n\n### What You'll Need\n\n- A Snowflake account ([trial](https://signup.snowflake.com/developers), or otherwise) on **AWS** or **Azure** (Snowflake-managed Iceberg storage is not available on Google Cloud)\n- **ACCOUNTADMIN** access\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+\n\n### What You'll Build\n\n- Snowflake-managed Iceberg tables for streaming telemetry, sensor readings, vehicle data, and maintenance logs\n- A Dynamic Iceberg Table pipeline for automated incremental transformations\n- A Cortex Agent for natural language querying of fleet data via Snowflake CoWork\n- DuckDB and Spark notebooks demonstrating cross-engine interoperability on the same Iceberg tables\n\n\u003C!-- ------------------------ --\u003E\n## Set Up Environment\n\nThis section guides you through setting up your environment. We'll download the companion assets, configure a few variables, and run a setup script that creates all the Snowflake objects we need.\n\n### Download the Assets\n\nDownload the companion assets from the [assets folder](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/get-started-snowflake-managed-iceberg-tables/assets) and place them in a local directory. Then navigate into that directory in your terminal.\n\n### Configure Variables\n\nCopy the template to create your configuration file:\n\n```bash\ncp config.env.template config.env\n```\n\nThen edit **config.env** with your values:\n\n```bash\n# Required: Snowflake CLI connection name (run `snow connection list` to see yours)\nSNOWFLAKE_CONNECTION=\"default\"\n\n# Required: Account identifier and username (used by DuckDB/Spark interop scripts)\n# Find yours: SELECT CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME();\nSNOWFLAKE_ACCOUNT=\"your_account_identifier\"\nSNOWFLAKE_USER=\"your_username\"\n\n# Required: Programmatic Access Token (PAT)\n# Create one at: User Menu → My Profile → Programmatic Access Tokens\nSNOWFLAKE_PAT=\"\"\n```\n\nThe streaming script uses `SNOWFLAKE_CONNECTION` to connect via your existing Snowflake CLI configuration in `~/.snowflake/config.toml` — no need to duplicate credentials. The account and user fields are only needed by the DuckDB and Spark interoperability scripts to construct the Horizon Catalog endpoint URL.\n\n\u003E **Note**: Make sure your Snowflake CLI connection is configured. If you haven't set one up yet, run:\n\u003E ```bash\n\u003E snow connection add --connection-name default --account \u003Cyour-account\u003E --user \u003Cyour-user\u003E\n\u003E ```\n\n### Run Setup Script\n\nThe setup script creates all the Snowflake objects we'll use throughout this Quickstart:\n\n```bash\nchmod +x setup.sh\n./setup.sh\n```\n\nHere's what the setup script does:\n\n- Creates a database called **FLEET_DB** with two schemas: **RAW** and **ANALYTICS**\n- Creates a warehouse called **FLEET_WH**\n- Sets the database default external volume to `SNOWFLAKE_MANAGED`\n- Creates Snowflake-managed Iceberg tables for vehicle telemetry, sensor readings, vehicle registry, and maintenance logs\n- Creates Dynamic Iceberg Tables for transformation pipelines\n- Loads sample data into the vehicle registry and sensor readings tables\n- Uploads JSON maintenance log files to an internal stage\n- Sets up a Python virtual environment for the streaming script\n\n### Verify Setup\n\nAfter the script completes, verify that the objects were created:\n\n```sql\nUSE DATABASE FLEET_DB;\nSHOW ICEBERG TABLES IN SCHEMA RAW;\nSHOW DYNAMIC TABLES IN SCHEMA ANALYTICS;\n```\n\nYou should see four Iceberg tables in the **RAW** schema and two Dynamic Tables in the **ANALYTICS** schema.\n\n\u003C!-- ------------------------ --\u003E\n## Create Snowflake-Managed Iceberg Tables\n\nLet's take a closer look at the Iceberg tables the setup script created. The DDL for these tables lives in **02_create_iceberg_tables.sql**. Understanding it is important because it shows how simple it is to create Iceberg tables with Snowflake-managed storage.\n\n### What Makes These Tables \"Iceberg\"?\n\nTwo parameters in the `CREATE ICEBERG TABLE` statement control the storage and catalog:\n\n- **`CATALOG = 'SNOWFLAKE'`** — Snowflake manages the Iceberg metadata (catalog entries, snapshots, manifest files)\n- **`EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'`** — Snowflake stores the Iceberg data files internally, just like standard Snowflake tables\n\nTogether, these mean you get the open Iceberg format — readable by any engine that speaks the Iceberg REST protocol — without managing cloud storage yourself.\n\n### Vehicle Telemetry (Streaming Target)\n\nThis table receives real-time streaming vehicle telemetry. It uses a `VARIANT` column for the flexible-schema telemetry payload:\n\n```sql\nCREATE OR REPLACE ICEBERG TABLE FLEET_DB.RAW.VEHICLE_TELEMETRY_STREAM (\n    VEHICLE_ID STRING NOT NULL,\n    EVENT_TIMESTAMP TIMESTAMP_NTZ(6) NOT NULL,\n    TELEMETRY_DATA VARIANT NOT NULL,\n    INGESTED_AT TIMESTAMP_LTZ(6)\n)\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE';\n```\n\nThe `VARIANT` column is key here. It lets us stream semi-structured JSON telemetry without defining a rigid schema upfront, while still supporting efficient querying with dot notation (e.g., `TELEMETRY_DATA:speed_mph`).\n\n### Vehicle Registry (Master Data)\n\nThis table holds master data about vehicles and drivers:\n\n```sql\nCREATE OR REPLACE ICEBERG TABLE FLEET_DB.RAW.VEHICLE_REGISTRY (\n    VEHICLE_ID STRING NOT NULL,\n    VIN STRING,\n    MAKE STRING,\n    MODEL STRING,\n    YEAR INT,\n    LICENSE_PLATE STRING,\n    DRIVER_NAME STRING,\n    DRIVER_EMAIL STRING,\n    FLEET_REGION STRING,\n    VEHICLE_STATUS STRING DEFAULT 'ACTIVE',\n    REGISTRATION_DATE DATE,\n    LAST_SERVICE_DATE DATE\n)\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE';\n```\n\n### Sensor Readings (Time-Series)\n\nThis table stores high-frequency sensor data for time-series analysis:\n\n```sql\nCREATE OR REPLACE ICEBERG TABLE FLEET_DB.RAW.SENSOR_READINGS (\n    READING_ID STRING NOT NULL,\n    VEHICLE_ID STRING NOT NULL,\n    READING_TIMESTAMP TIMESTAMP_NTZ(6) NOT NULL,\n    ENGINE_TEMP_F FLOAT,\n    OIL_PRESSURE_PSI FLOAT,\n    BATTERY_VOLTAGE FLOAT,\n    FUEL_CONSUMPTION_GPH FLOAT,\n    ODOMETER_MILES FLOAT\n)\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE';\n```\n\n### Maintenance Logs (Batch JSON)\n\nThis table will hold JSON maintenance logs loaded from files:\n\n```sql\nCREATE OR REPLACE ICEBERG TABLE FLEET_DB.RAW.MAINTENANCE_LOGS (\n    LOG_ID STRING NOT NULL,\n    VEHICLE_ID STRING NOT NULL,\n    LOG_TIMESTAMP TIMESTAMP_NTZ(6) NOT NULL,\n    LOG_DATA VARIANT NOT NULL,\n    SOURCE_FILE STRING\n)\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE';\n```\n\nEvery one of these tables is a proper Apache Iceberg table stored in the open format — yet creating them requires zero cloud infrastructure setup on your part.\n\n\u003C!-- ------------------------ --\u003E\n## Load Data\n\nNow let's populate our tables. The setup script already loaded sample data into the vehicle registry and sensor readings tables. In this step, we'll load JSON maintenance logs from staged files.\n\n### Verify Pre-Loaded Data\n\nThe setup script inserted sample data using `INSERT ... SELECT` with Snowflake's `GENERATOR` function. Let's verify:\n\n```sql\nUSE DATABASE FLEET_DB;\nUSE WAREHOUSE FLEET_WH;\n\nSELECT 'VEHICLE_REGISTRY' AS table_name, COUNT(*) AS row_count FROM RAW.VEHICLE_REGISTRY\nUNION ALL\nSELECT 'SENSOR_READINGS', COUNT(*) FROM RAW.SENSOR_READINGS;\n```\n\nYou should see 100 vehicles in the registry and 10,000 sensor readings.\n\n### Load JSON Maintenance Logs\n\nThe setup script uploaded JSON maintenance log files to an internal stage. Let's load them into the **MAINTENANCE_LOGS** Iceberg table:\n\n```sql\n-- View the staged files\nLIST @RAW.LOGS_STAGE;\n\n-- Load the JSON files into the Iceberg table\nCOPY INTO RAW.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 @RAW.LOGS_STAGE\n)\nFILE_FORMAT = (TYPE = 'JSON' STRIP_OUTER_ARRAY = TRUE)\nPATTERN = '.*maintenance_log.*\\.json';\n```\n\nHere's what the code does:\n\n- Lists the staged JSON files to confirm they're present\n- Uses `COPY INTO` with a subquery to extract typed fields from the raw JSON (`$1` refers to the entire JSON object in each file)\n- Stores the full JSON payload in the `LOG_DATA` VARIANT column for flexible querying\n- Captures the source file name in `SOURCE_FILE` for lineage tracking\n\nLet's verify the load and query the VARIANT data:\n\n```sql\n-- Verify and query the loaded logs\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 RAW.MAINTENANCE_LOGS\nORDER BY LOG_TIMESTAMP DESC\nLIMIT 10;\n```\n\nYou should see maintenance events like oil changes, tire rotations, and engine diagnostics — all extracted from the VARIANT column with dot notation.\n\n\u003C!-- ------------------------ --\u003E\n## Stream Data\n\nLet's bring real-time data into our Iceberg table. We'll use a Python script that simulates vehicle telemetry and inserts it directly into the **VEHICLE_TELEMETRY_STREAM** Iceberg table.\n\n### Start the Streaming Script\n\nOpen a terminal and navigate to the assets directory:\n\n```bash\ncd get-started-snowflake-managed-iceberg-tables/assets\n\n# Activate the Python environment created by setup.sh\nsource fleet_venv/bin/activate\n\n# Start the streaming simulation (runs for 5 minutes by default)\npython stream_telemetry.py\n```\n\nThe script simulates a fleet of 50 vehicles, each emitting telemetry events containing speed, location, engine metrics, and driver behavior data. Each event is a JSON payload inserted into the `TELEMETRY_DATA` VARIANT column.\n\nHere's an example of what each streaming event looks like:\n\n```json\n{\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### Watch Data Flow In\n\nWhile the streaming script runs, open a SQL worksheet and query the table:\n\n```sql\n-- Check current count of streaming events\nSELECT COUNT(*) AS event_count FROM RAW.VEHICLE_TELEMETRY_STREAM;\n\n-- View the latest events, extracting fields from the VARIANT column\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 RAW.VEHICLE_TELEMETRY_STREAM\nORDER BY EVENT_TIMESTAMP DESC\nLIMIT 10;\n```\n\nThe data is immediately queryable! Snowflake's dot notation on VARIANT columns makes it easy to extract nested fields from the JSON telemetry payload — no schema flattening required.\n\n\u003E **Tip**: You can stop the streaming script at any time with **Ctrl+C**. The data already streamed will remain in the table.\n\n\u003C!-- ------------------------ --\u003E\n## Transform with Dynamic Iceberg Tables\n\nWith data flowing into our raw tables, let's build a transformation pipeline using Dynamic Iceberg Tables. Dynamic Tables let you define transformations declaratively — you write a `SELECT` statement that describes the output, and Snowflake handles incremental refresh, dependency management, and scheduling automatically.\n\nThe setup script created two Dynamic Iceberg Tables. Let's explore them.\n\n### Tier 1: Enriched Telemetry\n\nThis Dynamic Iceberg Table joins streaming telemetry with the vehicle registry to add driver and vehicle details:\n\n```sql\nCREATE OR REPLACE DYNAMIC ICEBERG TABLE FLEET_DB.ANALYTICS.TELEMETRY_ENRICHED\n    TARGET_LAG = '10 minutes'\n    WAREHOUSE = FLEET_WH\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE'\nAS\nSELECT\n    t.VEHICLE_ID,\n    t.EVENT_TIMESTAMP,\n    t.TELEMETRY_DATA:speed_mph::FLOAT AS speed_mph,\n    t.TELEMETRY_DATA:engine:temperature_f::INT AS engine_temp_f,\n    t.TELEMETRY_DATA:engine:fuel_level_pct::FLOAT AS fuel_level_pct,\n    t.TELEMETRY_DATA:diagnostics:check_engine::BOOLEAN AS check_engine,\n    CASE\n        WHEN t.TELEMETRY_DATA:engine:temperature_f::INT \u003E 230 THEN 'CRITICAL'\n        WHEN t.TELEMETRY_DATA:engine:temperature_f::INT \u003E 210 THEN 'WARNING'\n        ELSE 'NORMAL'\n    END AS engine_health_status,\n    v.MAKE,\n    v.MODEL,\n    v.DRIVER_NAME,\n    v.FLEET_REGION\nFROM FLEET_DB.RAW.VEHICLE_TELEMETRY_STREAM t\nINNER JOIN FLEET_DB.RAW.VEHICLE_REGISTRY v\n    ON t.VEHICLE_ID = v.VEHICLE_ID;\n```\n\nHere's what the code does:\n\n- Extracts key fields from the VARIANT telemetry payload using dot notation\n- Classifies engine health into NORMAL, WARNING, or CRITICAL based on temperature thresholds\n- Joins with the vehicle registry to enrich each event with make, model, driver, and region\n- Refreshes automatically within 10 minutes of source data changes thanks to `TARGET_LAG`\n\n### Tier 2: Daily Fleet Summary\n\nThis Dynamic Iceberg Table aggregates the enriched telemetry into daily business metrics:\n\n```sql\nCREATE OR REPLACE DYNAMIC ICEBERG TABLE FLEET_DB.ANALYTICS.DAILY_FLEET_SUMMARY\n    TARGET_LAG = 'DOWNSTREAM'\n    WAREHOUSE = FLEET_WH\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    CATALOG = 'SNOWFLAKE'\nAS\nSELECT\n    DATE(EVENT_TIMESTAMP) AS summary_date,\n    FLEET_REGION,\n    COUNT(*) AS total_events,\n    COUNT(DISTINCT VEHICLE_ID) AS active_vehicles,\n    ROUND(AVG(speed_mph), 1) AS avg_speed_mph,\n    ROUND(AVG(engine_temp_f), 1) AS avg_engine_temp_f,\n    ROUND(AVG(fuel_level_pct), 1) AS avg_fuel_level_pct,\n    SUM(CASE WHEN engine_health_status = 'CRITICAL' THEN 1 ELSE 0 END) AS critical_events,\n    SUM(CASE WHEN check_engine THEN 1 ELSE 0 END) AS check_engine_events\nFROM FLEET_DB.ANALYTICS.TELEMETRY_ENRICHED\nGROUP BY DATE(EVENT_TIMESTAMP), FLEET_REGION;\n```\n\nNote the `TARGET_LAG = 'DOWNSTREAM'`. This means the table refreshes only when a downstream consumer needs fresh data, creating an efficient pull-based refresh model.\n\n### Trigger and Observe a Refresh\n\nLet's manually trigger a refresh to see incremental processing in action:\n\n```sql\n-- Refresh the pipeline\nALTER DYNAMIC TABLE FLEET_DB.ANALYTICS.TELEMETRY_ENRICHED REFRESH;\nALTER DYNAMIC TABLE FLEET_DB.ANALYTICS.DAILY_FLEET_SUMMARY REFRESH;\n\n-- Check refresh history — look at the refresh_action column\nSELECT name, refresh_action, state, refresh_start_time\nFROM TABLE(FLEET_DB.INFORMATION_SCHEMA.DYNAMIC_TABLE_REFRESH_HISTORY())\nWHERE name IN ('TELEMETRY_ENRICHED', 'DAILY_FLEET_SUMMARY')\nORDER BY refresh_start_time DESC\nLIMIT 10;\n```\n\nLook at the `refresh_action` column. After the initial full refresh, subsequent refreshes should show **INCREMENTAL**, meaning Snowflake processed only the new data — not the entire table.\n\n### Query the Transformed Data\n\n```sql\n-- Enriched telemetry with vehicle details\nSELECT\n    VEHICLE_ID, MAKE, MODEL, DRIVER_NAME, FLEET_REGION,\n    speed_mph, engine_temp_f, engine_health_status, EVENT_TIMESTAMP\nFROM ANALYTICS.TELEMETRY_ENRICHED\nWHERE engine_health_status != 'NORMAL'\nORDER BY EVENT_TIMESTAMP DESC\nLIMIT 10;\n\n-- Daily fleet summary\nSELECT *\nFROM ANALYTICS.DAILY_FLEET_SUMMARY\nORDER BY summary_date DESC, FLEET_REGION\nLIMIT 20;\n```\n\nYou've now built a two-tier declarative pipeline on top of Snowflake-managed Iceberg tables. Snowflake handles all the incremental processing and dependency management for you.\n\n\u003C!-- ------------------------ --\u003E\n## Query with Snowflake CoWork\n\nLet's make this data accessible to business users who may not know SQL. We'll create a Cortex Agent that lets anyone ask natural language questions about the fleet data through Snowflake CoWork.\n\n### Create a Semantic View\n\nFirst, we'll create a semantic view that describes our data in business terms. Rather than writing this manually, we'll use Snowflake's AI-powered UI to generate it.\n\n1. In Snowsight, navigate to **AI & ML → Analyst** in the left navigation menu\n\n2. Click **Create New: Semantic View** in the top right\n\n3. In the dialog:\n   - **Name**: Enter `fleet_semantic_view`\n   - **Location**: Select database **FLEET_DB** and schema **ANALYTICS**\n\n4. Click **Next** and skip the \"Provide context\" step\n\n5. Select the following tables:\n   - `ANALYTICS.TELEMETRY_ENRICHED`\n   - `ANALYTICS.DAILY_FLEET_SUMMARY`\n   - `RAW.VEHICLE_REGISTRY`\n   - `RAW.SENSOR_READINGS`\n\n6. Click **Next**, select all columns, and click **Create and Save**\n\nCortex Analyst will analyze the tables and automatically generate descriptions, logical names, and relationships. This takes a few moments.\n\n### Create a Cortex Agent\n\nNow let's create an agent that uses the semantic view:\n\n1. Navigate to **AI & ML → Agents** in the left navigation menu\n\n2. Click **Create agent**\n\n3. In the dialog:\n   - **Database**: Select **FLEET_DB**\n   - **Schema**: Select **ANALYTICS**\n   - **Agent object name**: Enter `fleet_agent`\n   - **Display name**: Enter `Fleet Analytics Agent`\n\n4. Click **Create**\n\n5. Click the **Tools** tab, then click **Add+** next to **Cortex Analyst**\n\n6. Select the semantic view we just created: **FLEET_DB.ANALYTICS.FLEET_SEMANTIC_VIEW**\n\n7. Enter a name for the tool and click **Generate with Cortex** for the description\n\n8. Click **Add**, then **Save**\n\n### Ask Questions in Snowflake CoWork\n\n1. Navigate to **AI & ML → Agents** and click the **Snowflake CoWork** tab\n\n2. Click **Add existing agent**, then select the **fleet_agent** you just created\n\n3. Once added, open **Snowflake CoWork** from the left navigation menu and select the **Fleet Analytics Agent**\n\n4. Try asking questions like:\n\n- \"What are the top 5 fleet regions by average speed?\"\n- \"Show me vehicles with critical engine health events\"\n- \"How many check engine events occurred per region today?\"\n- \"Which drivers have the most telemetry events?\"\n\nThe agent translates natural language into SQL, executes it against your Iceberg tables, and returns results with charts and explanations.\n\n\u003E **Note**: If you see a region availability error, enable cross-region inference:\n\u003E ```sql\n\u003E USE ROLE ACCOUNTADMIN;\n\u003E ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Query from DuckDB\n\nHere's where interoperability gets exciting. Because our tables are stored in the open Apache Iceberg format, external engines can read them directly through Snowflake's Horizon Catalog — without copying data, without ETL, without Snowflake compute.\n\nDuckDB is a lightweight, in-process analytical database. It supports the Iceberg REST Catalog protocol, which means it can connect to Horizon Catalog and read our Snowflake-managed Iceberg tables.\n\n### Install DuckDB\n\nIf you've deactivated the virtual environment, reactivate it first (`source fleet_venv/bin/activate`). Then install DuckDB:\n\n```bash\npip install duckdb requests\n```\n\n### Run the DuckDB Interoperability Script\n\nThe companion script **duckdb_interop.py** connects DuckDB to your Snowflake-managed Iceberg tables via Horizon Catalog. It reads your connection details and PAT from **config.env**, so no extra configuration is needed:\n\n```bash\npython duckdb_interop.py\n```\n\nHere's what the script does:\n\n- Installs and loads the DuckDB Iceberg extension\n- Creates a bearer token secret using your Snowflake PAT\n- Attaches to your Snowflake database through the Horizon Iceberg REST Catalog API\n- The `ENDPOINT` points to Snowflake's built-in Polaris endpoint — no separate catalog server needed\n\nThe script runs several queries to demonstrate interoperability. Here's a look at what it executes:\n\n```python\n# List tables visible through Horizon Catalog\nconn.execute(\"SHOW TABLES IN horizon.RAW\").show()\n\n# Query the vehicle registry\nconn.execute(\"\"\"\n    SELECT VEHICLE_ID, MAKE, MODEL, YEAR, FLEET_REGION\n    FROM horizon.RAW.VEHICLE_REGISTRY\n    LIMIT 10\n\"\"\").show()\n\n# Aggregate sensor readings by vehicle\nconn.execute(\"\"\"\n    SELECT\n        VEHICLE_ID,\n        COUNT(*) AS reading_count,\n        ROUND(AVG(ENGINE_TEMP_F), 1) AS avg_engine_temp,\n        ROUND(AVG(FUEL_CONSUMPTION_GPH), 2) AS avg_fuel_gph\n    FROM horizon.RAW.SENSOR_READINGS\n    GROUP BY VEHICLE_ID\n    ORDER BY avg_fuel_gph DESC\n    LIMIT 10\n\"\"\").show()\n```\n\nDuckDB is reading directly from the Iceberg files managed by Snowflake. No data was copied. No Snowflake warehouse was used. The data is in an open format, and any engine that speaks Iceberg can read it.\n\n\u003E **Tip**: DuckDB reads VARIANT columns as strings. You can use DuckDB's `json_extract` function to parse nested fields from VARIANT data.\n\nThis is the core value proposition of Snowflake-managed Iceberg tables: your data lives in an open format managed by Snowflake, but it's accessible from any compatible engine.\n\n\u003C!-- ------------------------ --\u003E\n## Query from Apache Spark\n\nFor teams that rely on Apache Spark, the same Horizon Catalog endpoint works with Spark's Iceberg REST catalog integration. Spark 4.0+ adds native VARIANT support, so you get full access to the semi-structured telemetry data.\n\n### Set Up the Spark Environment\n\nThe setup script created a Conda environment with Spark and all necessary dependencies. Deactivate the virtual environment if it's still active, then activate the Spark environment, install dependencies, and launch the notebook:\n\n```bash\ndeactivate\nconda activate fleet-spark\npip install pyspark==4.0.0 jupyter python-dotenv requests\njupyter notebook spark_iceberg_interop.ipynb\n```\n\n\u003E **Note**: PySpark 4.0 requires **Java 17 or 21**. Java 25 is not compatible. Check with `java -version` before running the notebook.\n\n### Connect Spark to Horizon Catalog\n\nThe pre-configured notebook creates a Spark session with the Horizon Catalog:\n\n```python\nfrom pyspark.sql import SparkSession\n\n# Configuration\nSNOWFLAKE_ACCOUNT_URL = \"your-org-your-account\"\nSNOWFLAKE_PAT = \"your-programmatic-access-token\"\nSNOWFLAKE_DATABASE = \"FLEET_DB\"\nSNOWFLAKE_ROLE = \"ACCOUNTADMIN\"\n\nICEBERG_VERSION = \"1.10.1\"\nSCALA_VERSION = \"2.13\"\n\nspark = SparkSession.builder \\\n    .appName(\"Fleet Analytics - Iceberg 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    .config(\"spark.sql.extensions\",\n            \"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\",\n            f\"https://{SNOWFLAKE_ACCOUNT_URL}.snowflakecomputing.com/polaris/api/catalog\") \\\n    .config(\"spark.sql.catalog.horizon.credential\", SNOWFLAKE_PAT) \\\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\",\n            \"vended-credentials\") \\\n    .config(\"spark.sql.iceberg.vectorization.enabled\", \"false\") \\\n    .getOrCreate()\n\nprint(f\"Spark {spark.version} connected to Horizon Catalog!\")\n```\n\nHere's what the code does:\n\n- Creates a local Spark session with Iceberg extensions\n- Configures the `horizon` catalog to use Snowflake's Iceberg REST API endpoint\n- Uses vended credentials so Spark can access the underlying Iceberg files without direct cloud storage permissions\n- The `scope` parameter ensures Spark uses your Snowflake role for access control\n\n### List and Query Tables\n\n```python\n# Show all tables visible through Horizon\nspark.sql(\"SHOW TABLES IN horizon.RAW\").show(truncate=False)\n\n# Query the vehicle registry\nspark.sql(\"\"\"\n    SELECT VEHICLE_ID, MAKE, MODEL, YEAR, FLEET_REGION\n    FROM horizon.RAW.VEHICLE_REGISTRY\n    LIMIT 10\n\"\"\").show()\n```\n\n### Query VARIANT Data with Spark 4.0\n\nSpark 4.0 supports the Iceberg V3 VARIANT type natively. Use `variant_get()` to extract nested fields:\n\n```python\n# Extract nested fields from the VARIANT telemetry payload\ndf = spark.sql(\"\"\"\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, '$.location.lon', 'float') AS longitude\n    FROM horizon.RAW.VEHICLE_TELEMETRY_STREAM\n    WHERE variant_get(TELEMETRY_DATA, '$.speed_mph', 'float') \u003E 60\n    LIMIT 10\n\"\"\")\ndf.show()\n```\n\nSpark is reading the same Iceberg tables that Snowflake manages, including the semi-structured VARIANT data. No data movement, no ETL, no duplication.\n\n### Dynamic Iceberg Tables Are Visible Too\n\nDynamic Iceberg Tables appear as regular Iceberg tables to external engines:\n\n```python\n# Query the Dynamic Iceberg Table\nspark.sql(\"SHOW TABLES IN horizon.ANALYTICS\").show(truncate=False)\n\nspark.sql(\"\"\"\n    SELECT *\n    FROM horizon.ANALYTICS.DAILY_FLEET_SUMMARY\n    ORDER BY summary_date DESC\n    LIMIT 10\n\"\"\").show()\n```\n\nThis is a powerful pattern: Snowflake handles the transformation pipeline (Dynamic Tables), and any engine can read the results.\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\nLet's clean up your Snowflake account. Run the following in a SQL worksheet:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\n-- Drop the database (removes all tables, dynamic tables, stages, etc.)\nDROP DATABASE IF EXISTS FLEET_DB;\n\n-- Drop the warehouse\nDROP WAREHOUSE IF EXISTS FLEET_WH;\n```\n\nIf you created a Cortex Agent, you can remove it from the **AI & ML → Agents** page in Snowsight before dropping the database.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nCongratulations! You built a fleet analytics pipeline on Snowflake-managed Iceberg tables and proved that the same data is accessible from DuckDB and Apache Spark — without configuring cloud storage, without moving data, and without giving up Snowflake's governance and performance.\n\n### What You Learned\n\n- Creating Snowflake-managed Iceberg tables with `EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'` — no cloud bucket required\n- Streaming semi-structured JSON data into VARIANT columns in Iceberg tables\n- Batch-loading JSON files into Iceberg tables with `COPY INTO`\n- Building incremental transformation pipelines with Dynamic Iceberg Tables\n- Querying fleet data with natural language through a Cortex Agent in Snowflake CoWork\n- Reading Snowflake-managed Iceberg tables from DuckDB via Horizon Catalog\n- Reading Snowflake-managed Iceberg tables from Apache Spark via Horizon Catalog, including VARIANT data with `variant_get()`\n\n### Related Resources\n\n- [Snowflake Storage for Apache Iceberg Tables](https://docs.snowflake.com/en/user-guide/tables-iceberg-internal-storage)\n- [Query Iceberg Tables with an External Engine through Horizon Catalog](https://docs.snowflake.com/en/user-guide/tables-iceberg-query-using-external-query-engine-snowflake-horizon)\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- [Snowpipe Streaming](https://docs.snowflake.com/en/user-guide/snowpipe-streaming/snowpipe-streaming-high-performance-overview)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-f1fb8dedba","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-709ed44bc5",":itemsOrder":["quickstart_last_modi","text"],":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-410e69c386","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-05",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-d326ccb093","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-81304a8bf0",":itemsOrder":[],":items":{},":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-ccb95f9e03",":itemsOrder":["quickstart_table_of_"],":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-eb55f12214","isDeveloperGuidesPage":false,":itemsOrder":["quickstart_table_of_","quickstart_button"],":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-486930ec94","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/get-started-snowflake-managed-iceberg-tables","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESet Up Environment\u003C/h2\u003E","\u003Ch2\u003ECreate Snowflake-Managed Iceberg Tables\u003C/h2\u003E","\u003Ch2\u003ELoad Data\u003C/h2\u003E","\u003Ch2\u003EStream Data\u003C/h2\u003E","\u003Ch2\u003ETransform with Dynamic Iceberg Tables\u003C/h2\u003E","\u003Ch2\u003EQuery with Snowflake CoWork\u003C/h2\u003E","\u003Ch2\u003EQuery from DuckDB\u003C/h2\u003E","\u003Ch2\u003EQuery from Apache Spark\u003C/h2\u003E","\u003Ch2\u003EClean Up\u003C/h2\u003E","\u003Ch2\u003EConclusion and 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