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taxes that plague multi-engine data architectures:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETax\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETraditional Pain\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat You'll Eliminate\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EStorage Tax\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESeparate copies for each engine\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESingle Iceberg copy, Snowflake-managed, readable by any engine\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EInteroperability Tax\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustom ETL to expose data; governance bypassed outside Snowflake\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHorizon IRC serves Iceberg metadata; FGAC enforced even on Spark\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAI Access Tax\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBespoke APIs/views for every BI/AI consumer\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESemantic View defines meaning once; Cortex Agent serves any user\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/sfguide-iceberg-zero-tax-analytics/architecture.png?v=ef070e11\" alt=\"Architecture\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will build a complete pipeline from raw data ingestion through natural-language analytics — all on open Apache Iceberg.\u003C/p\u003E\n","\u003Ch3\u003EGet the Code\u003C/h3\u003E\n","\u003Cp\u003EClick the \u003Cstrong\u003EFork Repo\u003C/strong\u003E button at the top of this page to get the complete source code. The repository contains all SQL scripts (with detailed inline comments), the Java Snowpipe Streaming V2 ingest application (\u003Ccode\u003ESSV2WeatherIngest.java\u003C/code\u003E + \u003Ccode\u003Epom.xml\u003C/code\u003E), the Databricks notebook, and teardown scripts — all in the \u003Ccode\u003Eassets/\u003C/code\u003E directory. This guide walks you through the key concepts and code — \u003Cstrong\u003Ethe repo is your runnable source of truth.\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/\"\u003ESnowflake account\u003C/a\u003E (Enterprise edition or higher)\u003C/li\u003E\u003Cli\u003EACCOUNTADMIN role or equivalent privileges\u003C/li\u003E\u003Cli\u003EA \u003Ca href=\"https://www.databricks.com/\"\u003EDatabricks workspace\u003C/a\u003E with cluster creation access (DBR 17.3, Spark 4.0)\u003C/li\u003E\u003Cli\u003EJava JDK 11+ (\u003Ccode\u003Ejava -version\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EApache Maven 3.6+ (\u003Ccode\u003Emvn -version\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EOpenSSL for RSA key generation (\u003Ccode\u003Eopenssl version\u003C/code\u003E)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create Snowflake-managed Iceberg tables with zero bucket configuration\u003C/li\u003E\u003Cli\u003EHow to stream real-time data into Iceberg V3 VARIANT columns via Snowpipe Streaming V2\u003C/li\u003E\u003Cli\u003EHow column masking and row-level security enforce governance across external engines\u003C/li\u003E\u003Cli\u003EHow Horizon's Iceberg REST Catalog enables multi-engine reads with vended credentials (Path A) or compute-routed governance (Path B)\u003C/li\u003E\u003Cli\u003EHow to build a Semantic View and Cortex Agent for natural-language analytics over Iceberg tables\u003C/li\u003E\u003Cli\u003EHow Snowflake CoWork turns business questions into SQL — grounded by verified queries\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake-managed Iceberg tables\u003C/strong\u003E — NYC taxi trips and weather data stored as open Apache Iceberg (Parquet + metadata)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EReal-time streaming ingest\u003C/strong\u003E — A Java application streaming weather data into an Iceberg V3 VARIANT table via Snowpipe Streaming V2\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGovernance policies\u003C/strong\u003E — Column masking and row-level security enforced across Snowflake and Databricks\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMulti-engine reads\u003C/strong\u003E — Databricks reading Iceberg tables through Horizon IRC with Path A (direct storage) and Path B (compute-routed) execution\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESemantic View\u003C/strong\u003E — Business-friendly dimensions, metrics, and verified queries over the physical Iceberg tables\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Agent\u003C/strong\u003E — Natural-language interface to the data via Snowflake CoWork\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEnvironment Setup\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Cp\u003EIn this step you will generate authentication credentials and configure the Snowflake environment.\u003C/p\u003E\n","\u003Ch3\u003EAuthentication Overview\u003C/h3\u003E\n","\u003Cp\u003EThis quickstart uses two authentication methods:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAuth Method\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EUsed By\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhy\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EPAT\u003C/strong\u003E (Programmatic Access Token)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHorizon IRC catalog credential\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIceberg REST Catalog spec uses OAuth bearer tokens\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ERSA key-pair\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESpark Connector JDBC fallback + Java SSV2 app\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EJDBC and Snowpipe Streaming SDK require key-pair auth\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIn short:\u003C/strong\u003E PAT handles catalog-level access (discovering tables). Key-pair handles compute-level access (JDBC queries + streaming ingest).\u003C/p\u003E\n","\u003Ch3\u003EGenerate an RSA Key Pair\u003C/h3\u003E\n","\u003Cp\u003EOpen a terminal and run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eopenssl genrsa 2048 | openssl pkcs8 -topk8 -nocrypt -out rsa_key.p8\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExtract the private key as a single base64 line (no PEM headers):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egrep -v "BEGIN\\|END" rsa_key.p8 | tr -d '\\n'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will use this value in two places later:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Eprofile.json\u003C/code\u003E in the \u003Ccode\u003Essv2-streaming/\u003C/code\u003E directory (Java SSV2 ingest app)\u003C/li\u003E\u003Cli\u003EDatabricks notebook cell 1 (Spark Connector config)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EConfigure the SSV2 Streaming App\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Essv2-streaming/\u003C/code\u003E directory in your forked repo contains the complete Java Maven project. Copy it to your local machine and configure credentials:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd ssv2-streaming\ncp profile.json.example profile.json\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEdit \u003Ccode\u003Eprofile.json\u003C/code\u003E with your values:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "user": "<YOUR_SNOWFLAKE_USERNAME>",\n  "account": "<ORG-ACCT>",\n  "url": "https://<ORG-ACCT>.snowflakecomputing.com:443",\n  "private_key": "<BASE64_PRIVATE_KEY_NO_HEADERS>",\n  "role": "DEMO_ADMIN"\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFind your account identifier:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun the Setup Script\u003C/h3\u003E\n","\u003Cp\u003EOpen \u003Ccode\u003Eassets/00_setup.sql\u003C/code\u003E from your forked repo in Snowsight or your preferred SQL client. Before executing, replace \u003Ccode\u003E<YOUR_SNOWFLAKE_USERNAME>\u003C/code\u003E with your actual username.\u003C/p\u003E\n","\u003Cp\u003EThis script creates:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWarehouses (DEMO_WH, DEMO_WH_2)\u003C/li\u003E\u003Cli\u003EDatabase (ICEBERG_DEMO) with Iceberg V3 default\u003C/li\u003E\u003Cli\u003ERoles (DEMO_ADMIN, DEMO_ANALYST, DEMO_DBX_READER)\u003C/li\u003E\u003Cli\u003EExternal stage pointing to pre-loaded NYC taxi Parquet files\u003C/li\u003E\u003Cli\u003ENetwork rule + external access integration for Open-Meteo API\u003C/li\u003E\u003Cli\u003ERSA public key registration on your user\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nSET MY_USER = '<YOUR_SNOWFLAKE_USERNAME>';\n\nCREATE WAREHOUSE IF NOT EXISTS DEMO_WH\n    WAREHOUSE_SIZE = 'MEDIUM'\n    AUTO_SUSPEND   = 120\n    AUTO_RESUME    = TRUE\n    INITIALLY_SUSPENDED = TRUE;\n\nCREATE DATABASE IF NOT EXISTS ICEBERG_DEMO;\nALTER DATABASE ICEBERG_DEMO SET ICEBERG_VERSION_DEFAULT = 3;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun all statements sequentially. At the end, verify:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ELIST @TLC_STAGE/yellow/;\n-- Expected: 26 Parquet files\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Iceberg Tables\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Cp\u003EIn this step you will create Snowflake-managed Iceberg tables from NYC taxi trip records using a single CTAS statement — no bucket configuration, no IAM roles, no compaction scheduling.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERepo shortcut:\u003C/strong\u003E The complete script with inline comments is \u003Ccode\u003Eassets/01_create_tables.sql\u003C/code\u003E in your forked repo. The key statements are shown below.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EKey Concept: Snowflake Storage for Iceberg\u003C/h3\u003E\n","\u003Cp\u003EAll tables use:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECATALOG = SNOWFLAKE\nEXTERNAL_VOLUME = SNOWFLAKE_MANAGED\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis means Snowflake manages the underlying storage (Parquet data files + Iceberg metadata) internally. Zero customer-managed cloud storage required.\u003C/p\u003E\n","\u003Ch3\u003ECreate yellow_trips (Path A — no governance)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE DEMO_ADMIN;\nUSE WAREHOUSE DEMO_WH;\nUSE DATABASE ICEBERG_DEMO;\nUSE SCHEMA PUBLIC;\n\nCREATE OR REPLACE ICEBERG TABLE yellow_trips\n    CATALOG = SNOWFLAKE\n    EXTERNAL_VOLUME = SNOWFLAKE_MANAGED\n    AS SELECT\n        $1:VendorID::INT AS VendorID,\n        TO_TIMESTAMP_NTZ($1:tpep_pickup_datetime::STRING)::TIMESTAMP_NTZ(6) AS tpep_pickup_datetime,\n        TO_TIMESTAMP_NTZ($1:tpep_dropoff_datetime::STRING)::TIMESTAMP_NTZ(6) AS tpep_dropoff_datetime,\n        $1:passenger_count::INT AS passenger_count,\n        $1:trip_distance::FLOAT AS trip_distance,\n        $1:RatecodeID::INT AS RatecodeID,\n        $1:store_and_fwd_flag::STRING AS store_and_fwd_flag,\n        $1:PULocationID::INT AS PULocationID,\n        $1:DOLocationID::INT AS DOLocationID,\n        $1:payment_type::INT AS payment_type,\n        $1:fare_amount::FLOAT AS fare_amount,\n        $1:extra::FLOAT AS extra,\n        $1:mta_tax::FLOAT AS mta_tax,\n        $1:tip_amount::FLOAT AS tip_amount,\n        $1:tolls_amount::FLOAT AS tolls_amount,\n        $1:improvement_surcharge::FLOAT AS improvement_surcharge,\n        $1:total_amount::FLOAT AS total_amount,\n        $1:congestion_surcharge::FLOAT AS congestion_surcharge,\n        $1:Airport_fee::FLOAT AS Airport_fee,\n        $1:cbd_congestion_fee::FLOAT AS cbd_congestion_fee\n    FROM @TLC_STAGE/yellow/ (FILE_FORMAT => 'parquet_ff');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy microseconds?\u003C/strong\u003E This CTAS may take a few minutes. While it runs, note that \u003Ccode\u003ETIMESTAMP_NTZ(6)\u003C/code\u003E (microsecond precision) is an intentional choice — Iceberg Spark runtime 1.10.x cannot read nanosecond timestamps.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate green_trips (Path B — governance will be applied later)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ICEBERG TABLE green_trips\n    CATALOG = SNOWFLAKE\n    EXTERNAL_VOLUME = SNOWFLAKE_MANAGED\n    AS SELECT\n        $1:VendorID::INT AS VendorID,\n        TO_TIMESTAMP_NTZ($1:lpep_pickup_datetime::STRING)::TIMESTAMP_NTZ(6) AS lpep_pickup_datetime,\n        TO_TIMESTAMP_NTZ($1:lpep_dropoff_datetime::STRING)::TIMESTAMP_NTZ(6) AS lpep_dropoff_datetime,\n        $1:passenger_count::INT AS passenger_count,\n        $1:trip_distance::FLOAT AS trip_distance,\n        $1:RatecodeID::INT AS RatecodeID,\n        $1:store_and_fwd_flag::STRING AS store_and_fwd_flag,\n        $1:PULocationID::INT AS PULocationID,\n        $1:DOLocationID::INT AS DOLocationID,\n        $1:payment_type::INT AS payment_type,\n        $1:fare_amount::FLOAT AS fare_amount,\n        $1:extra::FLOAT AS extra,\n        $1:mta_tax::FLOAT AS mta_tax,\n        $1:tip_amount::FLOAT AS tip_amount,\n        $1:tolls_amount::FLOAT AS tolls_amount,\n        $1:improvement_surcharge::FLOAT AS improvement_surcharge,\n        $1:total_amount::FLOAT AS total_amount,\n        $1:congestion_surcharge::FLOAT AS congestion_surcharge,\n        $1:trip_type::INT AS trip_type,\n        $1:ehail_fee::FLOAT AS ehail_fee\n    FROM @TLC_STAGE/green/ (FILE_FORMAT => 'parquet_ff');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate green_trips_nano (timestamp compatibility demo)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ICEBERG TABLE green_trips_nano\n    CATALOG = SNOWFLAKE\n    EXTERNAL_VOLUME = SNOWFLAKE_MANAGED\n    AS SELECT\n        $1:VendorID::INT AS VendorID,\n        TO_TIMESTAMP_NTZ($1:lpep_pickup_datetime::STRING)::TIMESTAMP_NTZ(9) AS lpep_pickup_datetime,\n        TO_TIMESTAMP_NTZ($1:lpep_dropoff_datetime::STRING)::TIMESTAMP_NTZ(9) AS lpep_dropoff_datetime,\n        $1:passenger_count::INT AS passenger_count,\n        $1:trip_distance::FLOAT AS trip_distance,\n        $1:PULocationID::INT AS PULocationID,\n        $1:DOLocationID::INT AS DOLocationID,\n        $1:fare_amount::FLOAT AS fare_amount,\n        $1:tip_amount::FLOAT AS tip_amount,\n        $1:total_amount::FLOAT AS total_amount\n    FROM @TLC_STAGE/green/ (FILE_FORMAT => 'parquet_ff')\n    LIMIT 1000;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIntentional incompatibility:\u003C/strong\u003E \u003Ccode\u003Egreen_trips_nano\u003C/code\u003E uses \u003Ccode\u003ETIMESTAMP_NTZ(9)\u003C/code\u003E (nanosecond). Snowflake reads it perfectly but Spark 1.10.x rejects it. You will observe this failure in the Databricks step.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate zone_lookup (dimension table)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE zone_lookup (\n    LocationID   INT,\n    Borough      VARCHAR,\n    Zone         VARCHAR,\n    service_zone VARCHAR\n);\n\nCOPY INTO zone_lookup\nFROM @TLC_STAGE/lookup/\nFILE_FORMAT = (FORMAT_NAME = 'csv_ff');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Iceberg V3 Metadata\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SYSTEM$GET_ICEBERG_TABLE_INFORMATION('yellow_trips') AS yellow_metadata;\nSELECT SYSTEM$GET_ICEBERG_TABLE_INFORMATION('green_trips') AS green_metadata;\n\nSELECT 'yellow_trips' AS table_name, COUNT(*) AS row_count FROM yellow_trips\nUNION ALL\nSELECT 'green_trips', COUNT(*) FROM green_trips\nUNION ALL\nSELECT 'zone_lookup', COUNT(*) FROM zone_lookup;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EConfirm \u003Ccode\u003EFORMAT_VERSION=3\u003C/code\u003E in metadata and expected row counts.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStream Weather Data with Snowpipe Streaming V2\u003C/h2\u003E\n","\u003Cp\u003EDuration: 15\u003C/p\u003E\n","\u003Cp\u003EIn this step you will create an Iceberg V3 table with a VARIANT column, set up a Snowpipe Streaming V2 pipe, and run a Java application that fetches weather data from the Open-Meteo API and streams it into Snowflake in real time.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGet the code:\u003C/strong\u003E The Java application and Maven project are in your forked repo (\u003Ccode\u003Eassets/SSV2WeatherIngest.java\u003C/code\u003E and \u003Ccode\u003Eassets/pom.xml\u003C/code\u003E). The complete \u003Ccode\u003Essv2-streaming/\u003C/code\u003E directory with the correct Maven layout, source code, and a \u003Ccode\u003Eprofile.json.example\u003C/code\u003E template is also included. Copy the \u003Ccode\u003Essv2-streaming/\u003C/code\u003E folder to your local machine, rename \u003Ccode\u003Eprofile.json.example\u003C/code\u003E to \u003Ccode\u003Eprofile.json\u003C/code\u003E, fill in your credentials, then run the build commands below.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate the Weather Table and Pipe\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE DEMO_ADMIN;\nUSE WAREHOUSE DEMO_WH;\nUSE DATABASE ICEBERG_DEMO;\nUSE SCHEMA PUBLIC;\n\nCREATE OR REPLACE ICEBERG TABLE nyc_weather_ssv2 (\n    location     STRING           NOT NULL,\n    latitude     FLOAT            NOT NULL,\n    longitude    FLOAT            NOT NULL,\n    weather_data VARIANT          NOT NULL,\n    ingested_at  TIMESTAMP_LTZ(6)\n)\nCATALOG = SNOWFLAKE\nEXTERNAL_VOLUME = SNOWFLAKE_MANAGED;\n\nCREATE OR REPLACE PIPE nyc_weather_ssv2_pipe\nAS COPY INTO nyc_weather_ssv2\nFROM (\n    SELECT\n        $1:location::STRING,\n        $1:latitude::FLOAT,\n        $1:longitude::FLOAT,\n        $1:weather_data::VARIANT,\n        $1:ingested_at::TIMESTAMP_LTZ\n    FROM TABLE(DATA_SOURCE(TYPE => 'STREAMING'))\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun the Java SSV2 Ingest Application\u003C/h3\u003E\n","\u003Cp\u003EIn your terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd ssv2-streaming\nmvn package\nmvn exec:java -Dexec.mainClass="com.snowflake.snowpipestreaming.demo.SSV2WeatherIngest"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExpected output:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EStreamed 36 rows.\nAll 36 rows committed to nyc_weather_ssv2.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe application fetches historical weather data from the Open-Meteo API for three NYC airports (JFK, LGA, EWR) and streams each response as a row with the full JSON payload stored as VARIANT.\u003C/p\u003E\n","\u003Ch3\u003EQuery the VARIANT Data\u003C/h3\u003E\n","\u003Cp\u003EReturn to Snowsight and query the streamed data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) AS row_count FROM nyc_weather_ssv2;\n\nSELECT\n    location,\n    ingested_at,\n    weather_data:timezone::STRING         AS timezone,\n    weather_data:elevation::FLOAT         AS elevation_m,\n    weather_data:generationtime_ms::FLOAT AS api_gen_ms\nFROM nyc_weather_ssv2\nORDER BY ingested_at DESC, location\nLIMIT 9;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESub-Column Pruning Proof\u003C/h3\u003E\n","\u003Cp\u003EIceberg V3 VARIANT supports sub-column pruning — only the accessed nested paths are scanned. Compare bytes scanned between a full \u003Ccode\u003ESELECT *\u003C/code\u003E and a targeted sub-column read:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER SESSION SET USE_CACHED_RESULT = FALSE;\nSELECT * FROM nyc_weather_ssv2;\n\n-- Now compare with sub-column access only:\nSELECT\n    location,\n    weather_data:hourly_units:precipitation::STRING AS precip_unit\nFROM nyc_weather_ssv2;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck \u003Ccode\u003EBYTES_SCANNED\u003C/code\u003E in Query History — the sub-column query reads dramatically less data.\u003C/p\u003E\n","\u003Ch3\u003ELATERAL FLATTEN — Hourly Precipitation\u003C/h3\u003E\n","\u003Cp\u003EExtract 700+ hourly data points from the nested VARIANT array into individual rows:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    w.location,\n    t.value::STRING AS weather_hour,\n    p.value::FLOAT  AS precipitation_mm,\n    c.value::INT    AS weather_code\nFROM nyc_weather_ssv2 w,\n    LATERAL FLATTEN(input => w.weather_data:hourly:time)          t,\n    LATERAL FLATTEN(input => w.weather_data:hourly:precipitation) p,\n    LATERAL FLATTEN(input => w.weather_data:hourly:weathercode)   c\nWHERE t.index = p.index\n  AND t.index = c.index\n  AND w.location = 'JFK'\n  AND p.value::FLOAT > 0\nORDER BY t.value::STRING\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWeather + Taxi Trips Join\u003C/h3\u003E\n","\u003Cp\u003ECombine the streamed weather data with taxi trips to analyze tipping behavior by weather condition:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    CASE WHEN w_precip > 0 THEN 'Rainy' ELSE 'Dry' END AS weather_condition,\n    COUNT(*)                                            AS trip_count,\n    ROUND(AVG(y.tip_amount / NULLIF(y.fare_amount, 0)) * 100, 2) AS avg_tip_pct\nFROM yellow_trips y\nJOIN (\n    SELECT\n        TO_TIMESTAMP(t.value::STRING) AS weather_hour,\n        p.value::FLOAT                AS w_precip\n    FROM nyc_weather_ssv2 w,\n        LATERAL FLATTEN(input => w.weather_data:hourly:time)          t,\n        LATERAL FLATTEN(input => w.weather_data:hourly:precipitation) p\n    WHERE t.index = p.index\n      AND w.location = 'JFK'\n) weather\nON DATE_TRUNC('hour', y.tpep_pickup_datetime) = weather.weather_hour\nWHERE y.fare_amount > 0\nGROUP BY 1\nORDER BY 1;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat you'll discover:\u003C/strong\u003E NYC riders tip consistently (~20%) regardless of weather — but rain \u003Cem\u003Edoes\u003C/em\u003E reduce trip volume and average distance. The real signal is demand elasticity, not generosity. This is why joining weather + trip data at query time (zero-copy) beats pre-aggregation.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGovernance — Path A vs Path B\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Cp\u003EIn this step you will apply column masking and row-level security policies to one table while leaving another policy-free. This sets up the two execution paths that Databricks will experience through Horizon. The complete script is \u003Ccode\u003Eassets/03_horizon_governance.sql\u003C/code\u003E in your forked repo.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPath\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETable\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPolicies\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDatabricks Behavior\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EA\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eyellow_trips\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENone\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHorizon vends STS credentials → direct storage read → zero SF compute\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EB\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Egreen_trips\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMasking + RLS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHorizon routes through SF compute → policies enforced server-side\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EVerify Path A is Clean\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE DEMO_ADMIN;\nUSE WAREHOUSE DEMO_WH;\nUSE DATABASE ICEBERG_DEMO;\nUSE SCHEMA PUBLIC;\n\nSELECT *\nFROM TABLE(INFORMATION_SCHEMA.POLICY_REFERENCES(\n    REF_ENTITY_NAME   => 'ICEBERG_DEMO.PUBLIC.yellow_trips',\n    REF_ENTITY_DOMAIN => 'TABLE'\n));\n-- Expected: 0 rows (no policies attached)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETag Sensitive Columns\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE TAG IF NOT EXISTS data_sensitivity ALLOWED_VALUES 'FINANCIAL', 'PII';\n\nALTER ICEBERG TABLE green_trips ALTER COLUMN fare_amount  SET TAG data_sensitivity = 'FINANCIAL';\nALTER ICEBERG TABLE green_trips ALTER COLUMN tip_amount   SET TAG data_sensitivity = 'FINANCIAL';\nALTER ICEBERG TABLE green_trips ALTER COLUMN total_amount SET TAG data_sensitivity = 'FINANCIAL';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Column Masking Policy\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE MASKING POLICY financial_mask\n    AS (val FLOAT)\n    RETURNS FLOAT ->\n    CASE\n        WHEN CURRENT_ROLE() IN ('DEMO_ADMIN') THEN val\n        ELSE -1.0\n    END;\n\nALTER ICEBERG TABLE green_trips ALTER COLUMN fare_amount  SET MASKING POLICY financial_mask;\nALTER ICEBERG TABLE green_trips ALTER COLUMN tip_amount   SET MASKING POLICY financial_mask;\nALTER ICEBERG TABLE green_trips ALTER COLUMN total_amount SET MASKING POLICY financial_mask;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Row Access Policy\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ROW ACCESS POLICY borough_rls\n    AS (pickup_location_id INT)\n    RETURNS BOOLEAN ->\n    CASE\n        WHEN CURRENT_ROLE() = 'DEMO_ADMIN'      THEN TRUE\n        WHEN CURRENT_ROLE() = 'DEMO_ANALYST'    THEN TRUE\n        WHEN CURRENT_ROLE() = 'DEMO_DBX_READER' THEN\n            EXISTS (SELECT 1 FROM zone_lookup z\n                    WHERE z.LocationID = pickup_location_id AND z.Borough = 'Manhattan')\n        ELSE FALSE\n    END;\n\nALTER ICEBERG TABLE green_trips\n    ADD ROW ACCESS POLICY borough_rls ON (PULocationID);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGrant SELECT to Databricks Reader Role\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nGRANT SELECT ON ICEBERG TABLE yellow_trips     TO ROLE DEMO_DBX_READER;\nGRANT SELECT ON ICEBERG TABLE green_trips      TO ROLE DEMO_DBX_READER;\nGRANT SELECT ON ICEBERG TABLE nyc_weather_ssv2 TO ROLE DEMO_DBX_READER;\nGRANT SELECT ON ICEBERG TABLE green_trips_nano TO ROLE DEMO_DBX_READER;\nGRANT SELECT ON TABLE zone_lookup              TO ROLE DEMO_DBX_READER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGenerate a Programmatic Access Token (PAT)\u003C/h3\u003E\n","\u003Cp\u003EThe PAT authenticates Databricks to the Horizon IRC endpoint as DEMO_DBX_READER:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME() AS account_identifier,\n    'https://' || CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME()\n        || '.snowflakecomputing.com/polaris/api/catalog' AS horizon_irc_endpoint;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGenerate the token via Snowsight (\u003Cstrong\u003EUser Menu → My Profile → Programmatic Access Tokens → Generate\u003C/strong\u003E) or SQL:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER USER IDENTIFIER($MY_USER)\n    ADD PROGRAMMATIC ACCESS TOKEN quickstart_dbx_pat\n    DAYS_TO_EXPIRY = 1\n    ROLE_RESTRICTION = 'DEMO_DBX_READER'\n    COMMENT = 'Quickstart Databricks IRC token';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Copy the PAT value immediately — it is shown only once. The token expires in 1 day.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERead from Databricks via Horizon IRC\u003C/h2\u003E\n","\u003Cp\u003EDuration: 15\u003C/p\u003E\n","\u003Cp\u003EIn this step you will configure a Databricks cluster to read Snowflake-managed Iceberg tables through the Horizon Iceberg REST Catalog endpoint, observing both Path A (vended credentials) and Path B (governance-routed) execution.\u003C/p\u003E\n","\u003Ch3\u003ECreate a Databricks Cluster\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESetting\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EValue\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhy\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDatabricks Runtime\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E17.3\u003C/strong\u003E (Spark 4.0)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERequired for Iceberg V3 VARIANT support\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPhoton\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EDisabled\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConflicts with Iceberg Spark runtime\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAccess mode\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ENo isolation shared\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnity Catalog mode blocks REST catalog connections\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EInstall Maven Libraries\u003C/h3\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003ECompute → Your Cluster → Libraries → Install New → Maven\u003C/strong\u003E and install:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMaven Coordinate\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\"\u003E\u003Ccode\u003Eorg.apache.iceberg:iceberg-spark-runtime-4.0_2.13:1.10.1\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIceberg Spark integration\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eorg.apache.iceberg:iceberg-aws-bundle:1.10.1\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ES3FileIO for reading Parquet\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Enet.snowflake:spark-snowflake_2.13:3.1.6\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake Connector (includes SnowflakeFallbackCatalog)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVersion warning:\u003C/strong\u003E Use \u003Ccode\u003E_2.13\u003C/code\u003E artifacts (not \u003Ccode\u003E_2.12\u003C/code\u003E). DBR 17.3 ships Scala 2.13. Do NOT install \u003Ccode\u003Esnowflake-jdbc\u003C/code\u003E separately — it's included transitively.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ERestart the cluster after installing all libraries.\u003C/p\u003E\n","\u003Ch3\u003EUpload and Configure the Notebook\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EImport \u003Ccode\u003Eassets/04_databricks_read.ipynb\u003C/code\u003E from your forked repo into your Databricks workspace\u003C/li\u003E\u003Cli\u003EAttach it to the configured cluster\u003C/li\u003E\u003Cli\u003EFill in cell 1:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003EACCOUNT_IDENTIFIER = "<ORG-ACCT>"\nPAT_TOKEN          = "<YOUR_PAT_TOKEN>"\nSF_USER            = "<YOUR_SNOWFLAKE_USER>"\nRSA_PRIVATE_KEY    = "<BASE64_KEY>"\nREGION             = "us-east-1"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPath A — yellow_trips (Zero Snowflake Compute)\u003C/h3\u003E\n","\u003Cp\u003ERun the Path A cell. Databricks reads yellow_trips directly from Snowflake-managed S3 storage using vended STS credentials:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eyellow_df = spark.table(f"{CAT}.{SCHEMA_NAME}.YELLOW_TRIPS")\nprint(f"Row count: {yellow_df.count():,}")\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EZero compute cost:\u003C/strong\u003E Check your Snowflake credit meter — it will not move. Path A uses zero warehouse compute because no policies are attached.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPath B — green_trips (Governance Enforced)\u003C/h3\u003E\n","\u003Cp\u003ERun the Path B cell. The \u003Ccode\u003ESnowflakeFallbackCatalog\u003C/code\u003E detects that Horizon returns 403 (policies present) and automatically routes the query through Snowflake compute:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Egreen_df = spark.table(f"{CAT}.{SCHEMA_NAME}.GREEN_TRIPS")\nprint(f"Row count (RLS-filtered, Manhattan only): {green_df.count():,}")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EObserve:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Efare_amount\u003C/code\u003E, \u003Ccode\u003Etip_amount\u003C/code\u003E, \u003Ccode\u003Etotal_amount\u003C/code\u003E return \u003Cstrong\u003E-1.0\u003C/strong\u003E (masking policy active)\u003C/li\u003E\u003Cli\u003ERow count is reduced to Manhattan pickups only (RLS active)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EIceberg V3 VARIANT — nyc_weather_ssv2\u003C/h3\u003E\n","\u003Cp\u003ERead the VARIANT column using Spark 4.0's native \u003Ccode\u003Evariant_get\u003C/code\u003E function:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom pyspark.sql.functions import variant_get, col\n\nweather_df = spark.table(f"{CAT}.{SCHEMA_NAME}.NYC_WEATHER_SSV2")\n\nweather_df.select(\n    col("location"),\n    variant_get("weather_data", "$.timezone", "string").alias("timezone"),\n    variant_get("weather_data", "$.elevation", "float").alias("elevation_m"),\n).show(truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETimestamp Compatibility Demo\u003C/h3\u003E\n","\u003Cp\u003ERun the nano timestamp cell to observe Spark rejecting \u003Ccode\u003ETIMESTAMP_NTZ(9)\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etry:\n    nano_df = spark.table(f"{CAT}.{SCHEMA_NAME}.GREEN_TRIPS_NANO")\n    nano_df.show(5)\nexcept Exception as e:\n    print(f"Expected failure: {type(e).__name__}")\n    print("Spark 1.10.x cannot read Iceberg timestamp_ns.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis confirms why the main tables use \u003Ccode\u003ETIMESTAMP_NTZ(6)\u003C/code\u003E — an intentional interoperability choice.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESemantic View & Cortex Agent\u003C/h2\u003E\n","\u003Cp\u003EDuration: 15\u003C/p\u003E\n","\u003Cp\u003EIn this step you will create a Semantic View over the Iceberg tables that defines business-friendly dimensions, metrics, and verified queries — then wrap it with a Cortex Agent for natural-language analytics via Snowflake CoWork.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERepo shortcut:\u003C/strong\u003E The complete script is \u003Ccode\u003Eassets/05_semantic_view.sql\u003C/code\u003E in your forked repo. You can run it directly in Snowsight. The inline code below is shown for learning — it explains each section of the DDL.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhy a Semantic View?\u003C/h3\u003E\n","\u003Cp\u003EA Semantic View is a schema-level object that acts as a "contract" between raw data and AI consumers. Cortex Analyst reads the semantic view to translate natural-language questions into accurate SQL. Verified queries (VQRs) provide gold-standard Q&A pairs that dramatically improve answer accuracy.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Semantic View\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE DEMO_ADMIN;\nUSE WAREHOUSE DEMO_WH;\nUSE DATABASE ICEBERG_DEMO;\nUSE SCHEMA PUBLIC;\n\nCREATE OR REPLACE SEMANTIC VIEW nyc_taxi_analytics\n\n  TABLES (\n    trips AS ICEBERG_DEMO.PUBLIC.YELLOW_TRIPS\n      WITH SYNONYMS ('yellow taxi', 'yellow cab', 'taxi trips', 'rides')\n      COMMENT = 'NYC Yellow Taxi trip records from Jan 2024 to Feb 2026',\n\n    zones AS ICEBERG_DEMO.PUBLIC.ZONE_LOOKUP\n      PRIMARY KEY (LOCATIONID)\n      WITH SYNONYMS ('taxi zones', 'locations', 'neighborhoods')\n      COMMENT = 'NYC taxi zone lookup — maps LocationID to Borough and Zone name'\n  )\n\n  RELATIONSHIPS (\n    pickup_zone AS\n      trips (PULOCATIONID) REFERENCES zones (LOCATIONID)\n  )\n\n  FACTS (\n    trips.fare AS fare_amount\n      COMMENT = 'Base fare amount in USD',\n    trips.tip AS tip_amount\n      COMMENT = 'Tip amount in USD',\n    trips.total AS total_amount\n      COMMENT = 'Total charge including fare, tips, tolls, surcharges',\n    trips.distance AS trip_distance\n      COMMENT = 'Trip distance in miles',\n    trips.passengers AS passenger_count\n      COMMENT = 'Number of passengers in the vehicle',\n    zones.borough_name AS Borough\n      COMMENT = 'NYC borough name from zone lookup'\n  )\n\n  DIMENSIONS (\n    trips.pickup_datetime AS tpep_pickup_datetime\n      WITH SYNONYMS = ('pickup time', 'start time', 'trip start')\n      COMMENT = 'Timestamp when the taxi meter was engaged',\n\n    trips.dropoff_datetime AS tpep_dropoff_datetime\n      WITH SYNONYMS = ('dropoff time', 'end time', 'trip end')\n      COMMENT = 'Timestamp when the taxi meter was disengaged',\n\n    trips.pickup_hour AS HOUR(tpep_pickup_datetime)\n      WITH SYNONYMS = ('hour of day', 'time of day')\n      COMMENT = 'Hour of pickup (0-23)',\n\n    trips.pickup_date AS DATE(tpep_pickup_datetime)\n      WITH SYNONYMS = ('trip date', 'date')\n      COMMENT = 'Date of pickup',\n\n    trips.pickup_month AS DATE_TRUNC('month', tpep_pickup_datetime)\n      WITH SYNONYMS = ('month', 'trip month')\n      COMMENT = 'Month of pickup',\n\n    trips.pickup_day_of_week AS DAYNAME(tpep_pickup_datetime)\n      WITH SYNONYMS = ('day of week', 'weekday')\n      COMMENT = 'Day of week (Mon, Tue, etc.)',\n\n    trips.payment_method AS\n      CASE payment_type\n        WHEN 1 THEN 'Credit Card'\n        WHEN 2 THEN 'Cash'\n        WHEN 3 THEN 'No Charge'\n        WHEN 4 THEN 'Dispute'\n        ELSE 'Unknown'\n      END\n      WITH SYNONYMS = ('payment type', 'how they paid')\n      COMMENT = 'Human-readable payment method',\n\n    zones.pickup_borough AS BOROUGH\n      WITH SYNONYMS = ('borough', 'pickup area', 'neighborhood')\n      COMMENT = 'NYC borough of pickup location',\n\n    zones.pickup_zone_name AS "ZONE"\n      WITH SYNONYMS = ('zone', 'pickup zone', 'pickup neighborhood')\n      COMMENT = 'NYC taxi zone name of pickup'\n  )\n\n  METRICS (\n    trips.total_trips AS COUNT(*)\n      WITH SYNONYMS = ('trip count', 'number of rides', 'ride count', 'how many trips')\n      COMMENT = 'Total number of taxi trips',\n\n    trips.total_revenue AS SUM(trips.total)\n      WITH SYNONYMS = ('revenue', 'total sales', 'earnings', 'income')\n      COMMENT = 'Total revenue in USD (fare + tips + tolls + surcharges)',\n\n    trips.avg_fare AS AVG(trips.fare)\n      WITH SYNONYMS = ('average fare', 'mean fare', 'typical fare')\n      COMMENT = 'Average base fare amount in USD',\n\n    trips.avg_tip_pct AS AVG(trips.tip / NULLIF(trips.fare, 0)) * 100\n      WITH SYNONYMS = ('tip percentage', 'tip rate', 'tipping rate', 'average tip percent')\n      COMMENT = 'Average tip as a percentage of fare',\n\n    trips.avg_distance AS AVG(trips.distance)\n      WITH SYNONYMS = ('average distance', 'mean distance', 'typical trip length')\n      COMMENT = 'Average trip distance in miles',\n\n    trips.avg_passengers AS AVG(trips.passengers)\n      WITH SYNONYMS = ('average passengers', 'riders per trip')\n      COMMENT = 'Average number of passengers per trip'\n  )\n\n  COMMENT = 'NYC Yellow Taxi analytics — trip volumes, revenue, tipping, and geography. Covers Jan 2024 through Feb 2026.'\n\n  AI_SQL_GENERATION 'Always round currency values to 2 decimal places. Always round percentages to 1 decimal place. When computing tip percentage, exclude rows where fare_amount is 0 or NULL. Default sort order is descending by the primary metric unless the user specifies otherwise.'\n\n  AI_QUESTION_CATEGORIZATION 'This semantic view covers NYC yellow taxi trip data only. It does NOT contain weather data, green taxi data, or airport information. If asked about weather, precipitation, temperature, or green taxis, respond that you do not have that information available.'\n\n  AI_VERIFIED_QUERIES (\n    revenue_by_borough AS (\n      QUESTION 'What is total revenue by borough?'\n      ONBOARDING_QUESTION TRUE\n      VERIFIED_BY '(STEWARD = demo_admin)'\n      SQL 'SELECT\n               __zones.pickup_borough,\n               ROUND(SUM(__trips.total), 2) AS total_revenue\n             FROM __trips\n             LEFT JOIN __zones ON __trips.pulocationid = __zones.locationid\n             GROUP BY __zones.pickup_borough\n             ORDER BY total_revenue DESC'\n    ),\n\n    avg_fare_by_payment AS (\n      QUESTION 'What is the average fare by payment method?'\n      ONBOARDING_QUESTION TRUE\n      VERIFIED_BY '(STEWARD = demo_admin)'\n      SQL 'SELECT\n               __trips.payment_method,\n               ROUND(AVG(__trips.fare), 2) AS avg_fare\n             FROM __trips\n             GROUP BY __trips.payment_method\n             ORDER BY avg_fare DESC'\n    ),\n\n    busiest_day_of_week AS (\n      QUESTION 'Which day of the week has the most trips?'\n      ONBOARDING_QUESTION TRUE\n      VERIFIED_BY '(STEWARD = demo_admin)'\n      SQL 'SELECT\n               __trips.pickup_day_of_week,\n               COUNT(*) AS total_trips\n             FROM __trips\n             GROUP BY __trips.pickup_day_of_week\n             ORDER BY total_trips DESC'\n    ),\n\n    tip_pct_by_borough AS (\n      QUESTION 'What is the average tip percentage by borough?'\n      VERIFIED_BY '(STEWARD = demo_admin)'\n      SQL 'SELECT\n               __zones.pickup_borough,\n               ROUND(AVG(__trips.tip / NULLIF(__trips.fare, 0)) * 100, 1) AS avg_tip_pct\n             FROM __trips\n             LEFT JOIN __zones ON __trips.pulocationid = __zones.locationid\n             WHERE __trips.fare > 0\n             GROUP BY __zones.pickup_borough\n             ORDER BY avg_tip_pct DESC'\n    ),\n\n    monthly_trip_volume AS (\n      QUESTION 'Show me monthly trip volume over time'\n      VERIFIED_BY '(STEWARD = demo_admin)'\n      SQL 'SELECT\n               __trips.pickup_month,\n               COUNT(*) AS total_trips\n             FROM __trips\n             GROUP BY __trips.pickup_month\n             ORDER BY __trips.pickup_month'\n    )\n  );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify the Semantic View\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDESCRIBE SEMANTIC VIEW nyc_taxi_analytics;\nSHOW SEMANTIC DIMENSIONS IN nyc_taxi_analytics;\nSHOW SEMANTIC METRICS IN nyc_taxi_analytics;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETest with a Direct Semantic Query\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM SEMANTIC_VIEW(\n  nyc_taxi_analytics\n  METRICS trips.total_trips, trips.total_revenue\n  DIMENSIONS zones.pickup_borough\n) ORDER BY total_revenue DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Cortex Agent\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE AGENT nyc_taxi_agent\n  COMMENT = 'NYC Yellow Taxi trip analyst — answers questions about trip volumes, revenue, tips, and geography'\n  FROM SPECIFICATION $$\ntools:\n  - tool_spec:\n      type: cortex_analyst_text_to_sql\n      name: taxi_analytics\n      description: "Answers questions about NYC yellow taxi trips including revenue, trip counts, tip percentages, payment methods, boroughs, and time-based trends. Covers January 2024 through February 2026."\ntool_resources:\n  taxi_analytics:\n    semantic_view: "ICEBERG_DEMO.PUBLIC.NYC_TAXI_ANALYTICS"\n    execution_environment:\n      type: warehouse\n      warehouse: "DEMO_WH"\n$$;\n\nGRANT USAGE ON AGENT ICEBERG_DEMO.PUBLIC.nyc_taxi_agent TO ROLE DEMO_ADMIN;\nGRANT USAGE ON AGENT ICEBERG_DEMO.PUBLIC.nyc_taxi_agent TO ROLE DEMO_ANALYST;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETest the Agent\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE DEMO_ADMIN;\nUSE WAREHOUSE DEMO_WH;\n\nSELECT TRY_PARSE_JSON(\n  SNOWFLAKE.CORTEX.DATA_AGENT_RUN(\n    'ICEBERG_DEMO.PUBLIC.NYC_TAXI_AGENT',\n    $${ \n      "messages": [\n        {\n          "role": "user",\n          "content": [{"type": "text", "text": "What is total revenue by borough?"}]\n        }\n      ]\n    }$$\n  )\n) AS resp;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETry it in Snowflake CoWork\u003C/h3\u003E\n","\u003Cp\u003EOpen Snowflake CoWork in Snowsight: \u003Cstrong\u003EAI & ML → Snowflake CoWork → select nyc_taxi_agent → start chatting.\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EQuestions to try:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E"What is total revenue by borough?" (hits VQR — high confidence)\u003C/li\u003E\u003Cli\u003E"Which day of the week is busiest?" (hits VQR)\u003C/li\u003E\u003Cli\u003E"Show me monthly trip trends" (hits VQR)\u003C/li\u003E\u003Cli\u003E"What's the average tip percentage for credit card vs cash?" (cross-dimension)\u003C/li\u003E\u003Cli\u003E"How many trips happened in Manhattan in January 2025?" (filter + dimension)\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EGuardrail tests:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"What was the weather like on rainy days?" → Agent declines (no weather data in SV)\u003C/li\u003E\u003Cli\u003E"Show me green taxi trips" → Agent declines (no green_trips in SV)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow guardrails work:\u003C/strong\u003E The agent's guardrails are not magic — they come from \u003Ccode\u003EAI_QUESTION_CATEGORIZATION\u003C/code\u003E in the semantic view DDL. You define what the AI should and should not answer.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERBAC Enforcement Demo\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EThis step demonstrates two critical RBAC enforcement points:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EGovernance on Iceberg tables\u003C/strong\u003E — The masking and RLS policies you applied in Module 3 are enforced via role-based access. A role without SELECT on the governed tables sees nothing.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGovernance on AI interfaces\u003C/strong\u003E — The Semantic View and Cortex Agent you built in the previous step respect the same privilege model. A role without grants on the semantic view or underlying tables cannot query through Snowflake CoWork either. There is no bypass — not through SQL, not through AI.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ECreate a Denied Role\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE ROLE IF NOT EXISTS DEMO_SV_DENIED\n  COMMENT = 'Role with no semantic view or table access — for RBAC demo';\nGRANT ROLE DEMO_SV_DENIED TO ROLE ACCOUNTADMIN;\nGRANT USAGE ON WAREHOUSE DEMO_WH TO ROLE DEMO_SV_DENIED;\nGRANT USAGE ON DATABASE ICEBERG_DEMO TO ROLE DEMO_SV_DENIED;\nGRANT USAGE ON SCHEMA ICEBERG_DEMO.PUBLIC TO ROLE DEMO_SV_DENIED;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Access is Denied\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE DEMO_SV_DENIED;\nUSE SECONDARY ROLES NONE;\nUSE WAREHOUSE DEMO_WH;\n\nSELECT * FROM SEMANTIC_VIEW(\n  nyc_taxi_analytics\n  METRICS trips.total_trips\n  DIMENSIONS zones.pickup_borough\n);\n-- Expected: Insufficient privileges error\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERestore Admin Context\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE DEMO_ADMIN;\nUSE SECONDARY ROLES ALL;\nUSE WAREHOUSE DEMO_WH;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETry it yourself:\u003C/strong\u003E Switch to the \u003Ccode\u003EDEMO_SV_DENIED\u003C/code\u003E role in Snowflake CoWork and ask the agent a question. It will fail. RBAC applies to every access path — SQL, API, and AI.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExplore with Snowflake CoCo (Optional)\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Cp\u003EThis optional step provides curated prompts you can paste into Snowflake CoCo (CoCo) — Snowflake's AI coding assistant — to explore the entire quickstart interactively.\u003C/p\u003E\n","\u003Ch3\u003EHow to Use\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EOpen Snowflake CoCo in Snowsight (lower-right panel) or use the CLI\u003C/li\u003E\u003Cli\u003ECopy any prompt below and paste it into CoCo\u003C/li\u003E\u003Cli\u003ECoCo will generate SQL, explain concepts, or build artifacts for you\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EData Engineering Prompts\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E"What tables exist in ICEBERG_DEMO.PUBLIC? Describe each one including\n row counts, column types, and whether they are Iceberg tables."\n\n"Explain the difference between CATALOG = SNOWFLAKE and using an external\n volume with a customer-managed bucket. What are the tradeoffs?"\n\n"Create a new Iceberg table called trip_summary that aggregates yellow_trips\n by pickup date and borough with total trips, revenue, and average fare."\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStreaming & VARIANT Prompts\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E"Explore the nyc_weather_ssv2 table. Show me how to extract hourly\n temperature and precipitation from the weather_data VARIANT column for JFK."\n\n"Write a query that finds the rainiest days at each airport station using\n the VARIANT data in nyc_weather_ssv2. Flatten the hourly array."\n\n"Write a query that joins nyc_weather_ssv2 weather data with yellow_trips\n to see if rainy days have lower tip percentages."\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGovernance Prompts\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E"Explain what masking and row-level security policies are applied in\n ICEBERG_DEMO.PUBLIC. Which tables and columns are protected?"\n\n"Show me how to verify that the borough_rls policy is working. Write\n queries that demonstrate filtered vs unfiltered results by role."\n\n"What is the difference between Path A and Path B when Databricks reads\n through Horizon? Explain the governance enforcement mechanism."\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESemantic View & Agent Prompts\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E"Show me the semantic view nyc_taxi_analytics. What dimensions, metrics,\n and verified queries does it define?"\n\n"Add a new metric to the nyc_taxi_analytics semantic view called\n avg_trip_duration that calculates average minutes between pickup and dropoff."\n\n"Write a new verified query (VQR) for the semantic view that answers:\n What is the average trip distance by hour of day?"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EOpen-Ended Exploration\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E"Build a Streamlit dashboard that shows a bar chart of total trips by\n borough and a line chart of daily revenue trends from yellow_trips."\n\n"Compare yellow_trips and green_trips — what are the differences in schema,\n row counts, and trip patterns? Which boroughs does each serve?"\n\n"Write a data quality check that validates yellow_trips has no future\n pickup dates, no negative fares, and no null location IDs."\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Cp\u003ECongratulations! You have built a complete zero-tax analytics pipeline — from raw Iceberg ingestion through natural-language AI queries — with governance enforced across engine boundaries.\u003C/p\u003E\n","\u003Ch3\u003ECleanup\u003C/h3\u003E\n","\u003Cp\u003EWhen you are done, use the teardown scripts in your forked repo:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Eassets/teardown_full.sql\u003C/code\u003E\u003C/strong\u003E — Drops all tables, pipes, policies, semantic view, agent, and tags. Preserves database, warehouse, roles, and stage.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Eassets/teardown_governance.sql\u003C/code\u003E\u003C/strong\u003E — Removes only governance objects (policies, tags, grants). Tables and data are preserved so you can re-apply policies without rebuilding Iceberg tables.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOpen the appropriate script in Snowsight and run all statements sequentially.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EZero Storage Tax\u003C/strong\u003E — Create Iceberg tables with \u003Ccode\u003ECATALOG = SNOWFLAKE\u003C/code\u003E — single copy, no bucket management, readable by any engine\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EZero Interoperability Tax\u003C/strong\u003E — Horizon IRC serves Iceberg metadata to Spark/Databricks with governance enforced server-side\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EZero AI Access Tax\u003C/strong\u003E — Semantic View + Cortex Agent turn business questions into SQL without bespoke pipelines\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowpipe Streaming V2\u003C/strong\u003E — Real-time ingest into Iceberg V3 VARIANT columns\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPath A vs Path B\u003C/strong\u003E — Vended credentials (zero compute) vs compute-routed governance — same endpoint, different outcome\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERBAC Everywhere\u003C/strong\u003E — Privilege enforcement across SQL, AI agents, and external engines\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\"\u003ESnowflake Storage for Iceberg Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-rest-catalog\"\u003EHorizon Iceberg REST Catalog\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-overview\"\u003ESnowpipe Streaming V2\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/semantic-views\"\u003ESemantic Views\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-column-ddm-intro\"\u003EColumn Masking Policies\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-row-intro\"\u003ERow Access Policies\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-iceberg-zero-tax-analytics\"\u003EGitHub Repository\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Open Lakehouse Done Right: Iceberg, Governance & Cortex in One Lab",":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nDuration: 5\n\n**Total estimated time: ~90 minutes**\n\nThis quickstart eliminates the three taxes that plague multi-engine data architectures:\n\n| Tax | Traditional Pain | What You'll Eliminate |\n|---|---|---|\n| **Storage Tax** | Separate copies for each engine | Single Iceberg copy, Snowflake-managed, readable by any engine |\n| **Interoperability Tax** | Custom ETL to expose data; governance bypassed outside Snowflake | Horizon IRC serves Iceberg metadata; FGAC enforced even on Spark |\n| **AI Access Tax** | Bespoke APIs/views for every BI/AI consumer | Semantic View defines meaning once; Cortex Agent serves any user |\n\n![Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/sfguide-iceberg-zero-tax-analytics/architecture.png?v=ef070e11)\n\nYou will build a complete pipeline from raw data ingestion through natural-language analytics — all on open Apache Iceberg.\n\n### Get the Code\n\nClick the **Fork Repo** button at the top of this page to get the complete source code. The repository contains all SQL scripts (with detailed inline comments), the Java Snowpipe Streaming V2 ingest application (`SSV2WeatherIngest.java` + `pom.xml`), the Databricks notebook, and teardown scripts — all in the `assets/` directory. This guide walks you through the key concepts and code — **the repo is your runnable source of truth.**\n\n### Prerequisites\n\n- A [Snowflake account](https://signup.snowflake.com/) (Enterprise edition or higher)\n- ACCOUNTADMIN role or equivalent privileges\n- A [Databricks workspace](https://www.databricks.com/) with cluster creation access (DBR 17.3, Spark 4.0)\n- Java JDK 11+ (`java -version`)\n- Apache Maven 3.6+ (`mvn -version`)\n- OpenSSL for RSA key generation (`openssl version`)\n\n### What You'll Learn\n\n- How to create Snowflake-managed Iceberg tables with zero bucket configuration\n- How to stream real-time data into Iceberg V3 VARIANT columns via Snowpipe Streaming V2\n- How column masking and row-level security enforce governance across external engines\n- How Horizon's Iceberg REST Catalog enables multi-engine reads with vended credentials (Path A) or compute-routed governance (Path B)\n- How to build a Semantic View and Cortex Agent for natural-language analytics over Iceberg tables\n- How Snowflake CoWork turns business questions into SQL — grounded by verified queries\n\n### What You'll Build\n\n- **Snowflake-managed Iceberg tables** — NYC taxi trips and weather data stored as open Apache Iceberg (Parquet + metadata)\n- **Real-time streaming ingest** — A Java application streaming weather data into an Iceberg V3 VARIANT table via Snowpipe Streaming V2\n- **Governance policies** — Column masking and row-level security enforced across Snowflake and Databricks\n- **Multi-engine reads** — Databricks reading Iceberg tables through Horizon IRC with Path A (direct storage) and Path B (compute-routed) execution\n- **Semantic View** — Business-friendly dimensions, metrics, and verified queries over the physical Iceberg tables\n- **Cortex Agent** — Natural-language interface to the data via Snowflake CoWork\n\n\u003C!-- ------------------------ --\u003E\n## Environment Setup\n\nDuration: 10\n\nIn this step you will generate authentication credentials and configure the Snowflake environment.\n\n### Authentication Overview\n\nThis quickstart uses two authentication methods:\n\n| Auth Method | Used By | Why |\n|---|---|---|\n| **PAT** (Programmatic Access Token) | Horizon IRC catalog credential | Iceberg REST Catalog spec uses OAuth bearer tokens |\n| **RSA key-pair** | Spark Connector JDBC fallback + Java SSV2 app | JDBC and Snowpipe Streaming SDK require key-pair auth |\n\n**In short:** PAT handles catalog-level access (discovering tables). Key-pair handles compute-level access (JDBC queries + streaming ingest).\n\n### Generate an RSA Key Pair\n\nOpen a terminal and run:\n\n```bash\nopenssl genrsa 2048 | openssl pkcs8 -topk8 -nocrypt -out rsa_key.p8\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n```\n\nExtract the private key as a single base64 line (no PEM headers):\n\n```bash\ngrep -v \"BEGIN\\|END\" rsa_key.p8 | tr -d '\\n'\n```\n\nYou will use this value in two places later:\n- `profile.json` in the `ssv2-streaming/` directory (Java SSV2 ingest app)\n- Databricks notebook cell 1 (Spark Connector config)\n\n### Configure the SSV2 Streaming App\n\nThe `ssv2-streaming/` directory in your forked repo contains the complete Java Maven project. Copy it to your local machine and configure credentials:\n\n```bash\ncd ssv2-streaming\ncp profile.json.example profile.json\n```\n\nEdit `profile.json` with your values:\n\n```json\n{\n  \"user\": \"\u003CYOUR_SNOWFLAKE_USERNAME\u003E\",\n  \"account\": \"\u003CORG-ACCT\u003E\",\n  \"url\": \"https://\u003CORG-ACCT\u003E.snowflakecomputing.com:443\",\n  \"private_key\": \"\u003CBASE64_PRIVATE_KEY_NO_HEADERS\u003E\",\n  \"role\": \"DEMO_ADMIN\"\n}\n```\n\nFind your account identifier:\n\n```sql\nSELECT CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME();\n```\n\n### Run the Setup Script\n\nOpen `assets/00_setup.sql` from your forked repo in Snowsight or your preferred SQL client. Before executing, replace `\u003CYOUR_SNOWFLAKE_USERNAME\u003E` with your actual username.\n\nThis script creates:\n- Warehouses (DEMO_WH, DEMO_WH_2)\n- Database (ICEBERG_DEMO) with Iceberg V3 default\n- Roles (DEMO_ADMIN, DEMO_ANALYST, DEMO_DBX_READER)\n- External stage pointing to pre-loaded NYC taxi Parquet files\n- Network rule + external access integration for Open-Meteo API\n- RSA public key registration on your user\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nSET MY_USER = '\u003CYOUR_SNOWFLAKE_USERNAME\u003E';\n\nCREATE WAREHOUSE IF NOT EXISTS DEMO_WH\n    WAREHOUSE_SIZE = 'MEDIUM'\n    AUTO_SUSPEND   = 120\n    AUTO_RESUME    = TRUE\n    INITIALLY_SUSPENDED = TRUE;\n\nCREATE DATABASE IF NOT EXISTS ICEBERG_DEMO;\nALTER DATABASE ICEBERG_DEMO SET ICEBERG_VERSION_DEFAULT = 3;\n```\n\nRun all statements sequentially. At the end, verify:\n\n```sql\nLIST @TLC_STAGE/yellow/;\n-- Expected: 26 Parquet files\n```\n\n\u003C!-- ------------------------ --\u003E\n## Create Iceberg Tables\n\nDuration: 10\n\nIn this step you will create Snowflake-managed Iceberg tables from NYC taxi trip records using a single CTAS statement — no bucket configuration, no IAM roles, no compaction scheduling.\n\n\u003E **Repo shortcut:** The complete script with inline comments is `assets/01_create_tables.sql` in your forked repo. The key statements are shown below.\n\n### Key Concept: Snowflake Storage for Iceberg\n\nAll tables use:\n```sql\nCATALOG = SNOWFLAKE\nEXTERNAL_VOLUME = SNOWFLAKE_MANAGED\n```\n\nThis means Snowflake manages the underlying storage (Parquet data files + Iceberg metadata) internally. Zero customer-managed cloud storage required.\n\n### Create yellow_trips (Path A — no governance)\n\n```sql\nUSE ROLE DEMO_ADMIN;\nUSE WAREHOUSE DEMO_WH;\nUSE DATABASE ICEBERG_DEMO;\nUSE SCHEMA PUBLIC;\n\nCREATE OR REPLACE ICEBERG TABLE yellow_trips\n    CATALOG = SNOWFLAKE\n    EXTERNAL_VOLUME = SNOWFLAKE_MANAGED\n    AS SELECT\n        $1:VendorID::INT AS VendorID,\n        TO_TIMESTAMP_NTZ($1:tpep_pickup_datetime::STRING)::TIMESTAMP_NTZ(6) AS tpep_pickup_datetime,\n        TO_TIMESTAMP_NTZ($1:tpep_dropoff_datetime::STRING)::TIMESTAMP_NTZ(6) AS tpep_dropoff_datetime,\n        $1:passenger_count::INT AS passenger_count,\n        $1:trip_distance::FLOAT AS trip_distance,\n        $1:RatecodeID::INT AS RatecodeID,\n        $1:store_and_fwd_flag::STRING AS store_and_fwd_flag,\n        $1:PULocationID::INT AS PULocationID,\n        $1:DOLocationID::INT AS DOLocationID,\n        $1:payment_type::INT AS payment_type,\n        $1:fare_amount::FLOAT AS fare_amount,\n        $1:extra::FLOAT AS extra,\n        $1:mta_tax::FLOAT AS mta_tax,\n        $1:tip_amount::FLOAT AS tip_amount,\n        $1:tolls_amount::FLOAT AS tolls_amount,\n        $1:improvement_surcharge::FLOAT AS improvement_surcharge,\n        $1:total_amount::FLOAT AS total_amount,\n        $1:congestion_surcharge::FLOAT AS congestion_surcharge,\n        $1:Airport_fee::FLOAT AS Airport_fee,\n        $1:cbd_congestion_fee::FLOAT AS cbd_congestion_fee\n    FROM @TLC_STAGE/yellow/ (FILE_FORMAT =\u003E 'parquet_ff');\n```\n\n\u003E **Why microseconds?** This CTAS may take a few minutes. While it runs, note that `TIMESTAMP_NTZ(6)` (microsecond precision) is an intentional choice — Iceberg Spark runtime 1.10.x cannot read nanosecond timestamps.\n\n### Create green_trips (Path B — governance will be applied later)\n\n```sql\nCREATE OR REPLACE ICEBERG TABLE green_trips\n    CATALOG = SNOWFLAKE\n    EXTERNAL_VOLUME = SNOWFLAKE_MANAGED\n    AS SELECT\n        $1:VendorID::INT AS VendorID,\n        TO_TIMESTAMP_NTZ($1:lpep_pickup_datetime::STRING)::TIMESTAMP_NTZ(6) AS lpep_pickup_datetime,\n        TO_TIMESTAMP_NTZ($1:lpep_dropoff_datetime::STRING)::TIMESTAMP_NTZ(6) AS lpep_dropoff_datetime,\n        $1:passenger_count::INT AS passenger_count,\n        $1:trip_distance::FLOAT AS trip_distance,\n        $1:RatecodeID::INT AS RatecodeID,\n        $1:store_and_fwd_flag::STRING AS store_and_fwd_flag,\n        $1:PULocationID::INT AS PULocationID,\n        $1:DOLocationID::INT AS DOLocationID,\n        $1:payment_type::INT AS payment_type,\n        $1:fare_amount::FLOAT AS fare_amount,\n        $1:extra::FLOAT AS extra,\n        $1:mta_tax::FLOAT AS mta_tax,\n        $1:tip_amount::FLOAT AS tip_amount,\n        $1:tolls_amount::FLOAT AS tolls_amount,\n        $1:improvement_surcharge::FLOAT AS improvement_surcharge,\n        $1:total_amount::FLOAT AS total_amount,\n        $1:congestion_surcharge::FLOAT AS congestion_surcharge,\n        $1:trip_type::INT AS trip_type,\n        $1:ehail_fee::FLOAT AS ehail_fee\n    FROM @TLC_STAGE/green/ (FILE_FORMAT =\u003E 'parquet_ff');\n```\n\n### Create green_trips_nano (timestamp compatibility demo)\n\n```sql\nCREATE OR REPLACE ICEBERG TABLE green_trips_nano\n    CATALOG = SNOWFLAKE\n    EXTERNAL_VOLUME = SNOWFLAKE_MANAGED\n    AS SELECT\n        $1:VendorID::INT AS VendorID,\n        TO_TIMESTAMP_NTZ($1:lpep_pickup_datetime::STRING)::TIMESTAMP_NTZ(9) AS lpep_pickup_datetime,\n        TO_TIMESTAMP_NTZ($1:lpep_dropoff_datetime::STRING)::TIMESTAMP_NTZ(9) AS lpep_dropoff_datetime,\n        $1:passenger_count::INT AS passenger_count,\n        $1:trip_distance::FLOAT AS trip_distance,\n        $1:PULocationID::INT AS PULocationID,\n        $1:DOLocationID::INT AS DOLocationID,\n        $1:fare_amount::FLOAT AS fare_amount,\n        $1:tip_amount::FLOAT AS tip_amount,\n        $1:total_amount::FLOAT AS total_amount\n    FROM @TLC_STAGE/green/ (FILE_FORMAT =\u003E 'parquet_ff')\n    LIMIT 1000;\n```\n\n\u003E **Intentional incompatibility:** `green_trips_nano` uses `TIMESTAMP_NTZ(9)` (nanosecond). Snowflake reads it perfectly but Spark 1.10.x rejects it. You will observe this failure in the Databricks step.\n\n### Create zone_lookup (dimension table)\n\n```sql\nCREATE OR REPLACE TABLE zone_lookup (\n    LocationID   INT,\n    Borough      VARCHAR,\n    Zone         VARCHAR,\n    service_zone VARCHAR\n);\n\nCOPY INTO zone_lookup\nFROM @TLC_STAGE/lookup/\nFILE_FORMAT = (FORMAT_NAME = 'csv_ff');\n```\n\n### Verify Iceberg V3 Metadata\n\n```sql\nSELECT SYSTEM$GET_ICEBERG_TABLE_INFORMATION('yellow_trips') AS yellow_metadata;\nSELECT SYSTEM$GET_ICEBERG_TABLE_INFORMATION('green_trips') AS green_metadata;\n\nSELECT 'yellow_trips' AS table_name, COUNT(*) AS row_count FROM yellow_trips\nUNION ALL\nSELECT 'green_trips', COUNT(*) FROM green_trips\nUNION ALL\nSELECT 'zone_lookup', COUNT(*) FROM zone_lookup;\n```\n\nConfirm `FORMAT_VERSION=3` in metadata and expected row counts.\n\n\u003C!-- ------------------------ --\u003E\n## Stream Weather Data with Snowpipe Streaming V2\n\nDuration: 15\n\nIn this step you will create an Iceberg V3 table with a VARIANT column, set up a Snowpipe Streaming V2 pipe, and run a Java application that fetches weather data from the Open-Meteo API and streams it into Snowflake in real time.\n\n\u003E **Get the code:** The Java application and Maven project are in your forked repo (`assets/SSV2WeatherIngest.java` and `assets/pom.xml`). The complete `ssv2-streaming/` directory with the correct Maven layout, source code, and a `profile.json.example` template is also included. Copy the `ssv2-streaming/` folder to your local machine, rename `profile.json.example` to `profile.json`, fill in your credentials, then run the build commands below.\n\n### Create the Weather Table and Pipe\n\n```sql\nUSE ROLE DEMO_ADMIN;\nUSE WAREHOUSE DEMO_WH;\nUSE DATABASE ICEBERG_DEMO;\nUSE SCHEMA PUBLIC;\n\nCREATE OR REPLACE ICEBERG TABLE nyc_weather_ssv2 (\n    location     STRING           NOT NULL,\n    latitude     FLOAT            NOT NULL,\n    longitude    FLOAT            NOT NULL,\n    weather_data VARIANT          NOT NULL,\n    ingested_at  TIMESTAMP_LTZ(6)\n)\nCATALOG = SNOWFLAKE\nEXTERNAL_VOLUME = SNOWFLAKE_MANAGED;\n\nCREATE OR REPLACE PIPE nyc_weather_ssv2_pipe\nAS COPY INTO nyc_weather_ssv2\nFROM (\n    SELECT\n        $1:location::STRING,\n        $1:latitude::FLOAT,\n        $1:longitude::FLOAT,\n        $1:weather_data::VARIANT,\n        $1:ingested_at::TIMESTAMP_LTZ\n    FROM TABLE(DATA_SOURCE(TYPE =\u003E 'STREAMING'))\n);\n```\n\n### Run the Java SSV2 Ingest Application\n\nIn your terminal:\n\n```bash\ncd ssv2-streaming\nmvn package\nmvn exec:java -Dexec.mainClass=\"com.snowflake.snowpipestreaming.demo.SSV2WeatherIngest\"\n```\n\nExpected output:\n\n```\nStreamed 36 rows.\nAll 36 rows committed to nyc_weather_ssv2.\n```\n\nThe application fetches historical weather data from the Open-Meteo API for three NYC airports (JFK, LGA, EWR) and streams each response as a row with the full JSON payload stored as VARIANT.\n\n### Query the VARIANT Data\n\nReturn to Snowsight and query the streamed data:\n\n```sql\nSELECT COUNT(*) AS row_count FROM nyc_weather_ssv2;\n\nSELECT\n    location,\n    ingested_at,\n    weather_data:timezone::STRING         AS timezone,\n    weather_data:elevation::FLOAT         AS elevation_m,\n    weather_data:generationtime_ms::FLOAT AS api_gen_ms\nFROM nyc_weather_ssv2\nORDER BY ingested_at DESC, location\nLIMIT 9;\n```\n\n### Sub-Column Pruning Proof\n\nIceberg V3 VARIANT supports sub-column pruning — only the accessed nested paths are scanned. Compare bytes scanned between a full `SELECT *` and a targeted sub-column read:\n\n```sql\nALTER SESSION SET USE_CACHED_RESULT = FALSE;\nSELECT * FROM nyc_weather_ssv2;\n\n-- Now compare with sub-column access only:\nSELECT\n    location,\n    weather_data:hourly_units:precipitation::STRING AS precip_unit\nFROM nyc_weather_ssv2;\n```\n\nCheck `BYTES_SCANNED` in Query History — the sub-column query reads dramatically less data.\n\n### LATERAL FLATTEN — Hourly Precipitation\n\nExtract 700+ hourly data points from the nested VARIANT array into individual rows:\n\n```sql\nSELECT\n    w.location,\n    t.value::STRING AS weather_hour,\n    p.value::FLOAT  AS precipitation_mm,\n    c.value::INT    AS weather_code\nFROM nyc_weather_ssv2 w,\n    LATERAL FLATTEN(input =\u003E w.weather_data:hourly:time)          t,\n    LATERAL FLATTEN(input =\u003E w.weather_data:hourly:precipitation) p,\n    LATERAL FLATTEN(input =\u003E w.weather_data:hourly:weathercode)   c\nWHERE t.index = p.index\n  AND t.index = c.index\n  AND w.location = 'JFK'\n  AND p.value::FLOAT \u003E 0\nORDER BY t.value::STRING\nLIMIT 20;\n```\n\n### Weather + Taxi Trips Join\n\nCombine the streamed weather data with taxi trips to analyze tipping behavior by weather condition:\n\n```sql\nSELECT\n    CASE WHEN w_precip \u003E 0 THEN 'Rainy' ELSE 'Dry' END AS weather_condition,\n    COUNT(*)                                            AS trip_count,\n    ROUND(AVG(y.tip_amount / NULLIF(y.fare_amount, 0)) * 100, 2) AS avg_tip_pct\nFROM yellow_trips y\nJOIN (\n    SELECT\n        TO_TIMESTAMP(t.value::STRING) AS weather_hour,\n        p.value::FLOAT                AS w_precip\n    FROM nyc_weather_ssv2 w,\n        LATERAL FLATTEN(input =\u003E w.weather_data:hourly:time)          t,\n        LATERAL FLATTEN(input =\u003E w.weather_data:hourly:precipitation) p\n    WHERE t.index = p.index\n      AND w.location = 'JFK'\n) weather\nON DATE_TRUNC('hour', y.tpep_pickup_datetime) = weather.weather_hour\nWHERE y.fare_amount \u003E 0\nGROUP BY 1\nORDER BY 1;\n```\n\n\u003E **What you'll discover:** NYC riders tip consistently (~20%) regardless of weather — but rain *does* reduce trip volume and average distance. The real signal is demand elasticity, not generosity. This is why joining weather + trip data at query time (zero-copy) beats pre-aggregation.\n\n\u003C!-- ------------------------ --\u003E\n## Governance — Path A vs Path B\n\nDuration: 10\n\nIn this step you will apply column masking and row-level security policies to one table while leaving another policy-free. This sets up the two execution paths that Databricks will experience through Horizon. The complete script is `assets/03_horizon_governance.sql` in your forked repo.\n\n| Path | Table | Policies | Databricks Behavior |\n|---|---|---|---|\n| **A** | yellow_trips | None | Horizon vends STS credentials → direct storage read → zero SF compute |\n| **B** | green_trips | Masking + RLS | Horizon routes through SF compute → policies enforced server-side |\n\n### Verify Path A is Clean\n\n```sql\nUSE ROLE DEMO_ADMIN;\nUSE WAREHOUSE DEMO_WH;\nUSE DATABASE ICEBERG_DEMO;\nUSE SCHEMA PUBLIC;\n\nSELECT *\nFROM TABLE(INFORMATION_SCHEMA.POLICY_REFERENCES(\n    REF_ENTITY_NAME   =\u003E 'ICEBERG_DEMO.PUBLIC.yellow_trips',\n    REF_ENTITY_DOMAIN =\u003E 'TABLE'\n));\n-- Expected: 0 rows (no policies attached)\n```\n\n### Tag Sensitive Columns\n\n```sql\nCREATE TAG IF NOT EXISTS data_sensitivity ALLOWED_VALUES 'FINANCIAL', 'PII';\n\nALTER ICEBERG TABLE green_trips ALTER COLUMN fare_amount  SET TAG data_sensitivity = 'FINANCIAL';\nALTER ICEBERG TABLE green_trips ALTER COLUMN tip_amount   SET TAG data_sensitivity = 'FINANCIAL';\nALTER ICEBERG TABLE green_trips ALTER COLUMN total_amount SET TAG data_sensitivity = 'FINANCIAL';\n```\n\n### Create Column Masking Policy\n\n```sql\nCREATE OR REPLACE MASKING POLICY financial_mask\n    AS (val FLOAT)\n    RETURNS FLOAT -\u003E\n    CASE\n        WHEN CURRENT_ROLE() IN ('DEMO_ADMIN') THEN val\n        ELSE -1.0\n    END;\n\nALTER ICEBERG TABLE green_trips ALTER COLUMN fare_amount  SET MASKING POLICY financial_mask;\nALTER ICEBERG TABLE green_trips ALTER COLUMN tip_amount   SET MASKING POLICY financial_mask;\nALTER ICEBERG TABLE green_trips ALTER COLUMN total_amount SET MASKING POLICY financial_mask;\n```\n\n### Create Row Access Policy\n\n```sql\nCREATE OR REPLACE ROW ACCESS POLICY borough_rls\n    AS (pickup_location_id INT)\n    RETURNS BOOLEAN -\u003E\n    CASE\n        WHEN CURRENT_ROLE() = 'DEMO_ADMIN'      THEN TRUE\n        WHEN CURRENT_ROLE() = 'DEMO_ANALYST'    THEN TRUE\n        WHEN CURRENT_ROLE() = 'DEMO_DBX_READER' THEN\n            EXISTS (SELECT 1 FROM zone_lookup z\n                    WHERE z.LocationID = pickup_location_id AND z.Borough = 'Manhattan')\n        ELSE FALSE\n    END;\n\nALTER ICEBERG TABLE green_trips\n    ADD ROW ACCESS POLICY borough_rls ON (PULocationID);\n```\n\n### Grant SELECT to Databricks Reader Role\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nGRANT SELECT ON ICEBERG TABLE yellow_trips     TO ROLE DEMO_DBX_READER;\nGRANT SELECT ON ICEBERG TABLE green_trips      TO ROLE DEMO_DBX_READER;\nGRANT SELECT ON ICEBERG TABLE nyc_weather_ssv2 TO ROLE DEMO_DBX_READER;\nGRANT SELECT ON ICEBERG TABLE green_trips_nano TO ROLE DEMO_DBX_READER;\nGRANT SELECT ON TABLE zone_lookup              TO ROLE DEMO_DBX_READER;\n```\n\n### Generate a Programmatic Access Token (PAT)\n\nThe PAT authenticates Databricks to the Horizon IRC endpoint as DEMO_DBX_READER:\n\n```sql\nSELECT\n    CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME() AS account_identifier,\n    'https://' || CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME()\n        || '.snowflakecomputing.com/polaris/api/catalog' AS horizon_irc_endpoint;\n```\n\nGenerate the token via Snowsight (**User Menu → My Profile → Programmatic Access Tokens → Generate**) or SQL:\n\n```sql\nALTER USER IDENTIFIER($MY_USER)\n    ADD PROGRAMMATIC ACCESS TOKEN quickstart_dbx_pat\n    DAYS_TO_EXPIRY = 1\n    ROLE_RESTRICTION = 'DEMO_DBX_READER'\n    COMMENT = 'Quickstart Databricks IRC token';\n```\n\n\u003E **Important:** Copy the PAT value immediately — it is shown only once. The token expires in 1 day.\n\n\u003C!-- ------------------------ --\u003E\n## Read from Databricks via Horizon IRC\n\nDuration: 15\n\nIn this step you will configure a Databricks cluster to read Snowflake-managed Iceberg tables through the Horizon Iceberg REST Catalog endpoint, observing both Path A (vended credentials) and Path B (governance-routed) execution.\n\n### Create a Databricks Cluster\n\n| Setting | Value | Why |\n|---|---|---|\n| Databricks Runtime | **17.3** (Spark 4.0) | Required for Iceberg V3 VARIANT support |\n| Photon | **Disabled** | Conflicts with Iceberg Spark runtime |\n| Access mode | **No isolation shared** | Unity Catalog mode blocks REST catalog connections |\n\n### Install Maven Libraries\n\nNavigate to **Compute → Your Cluster → Libraries → Install New → Maven** and install:\n\n| Maven Coordinate | Purpose |\n|---|---|\n| `org.apache.iceberg:iceberg-spark-runtime-4.0_2.13:1.10.1` | Iceberg Spark integration |\n| `org.apache.iceberg:iceberg-aws-bundle:1.10.1` | S3FileIO for reading Parquet |\n| `net.snowflake:spark-snowflake_2.13:3.1.6` | Snowflake Connector (includes SnowflakeFallbackCatalog) |\n\n\u003E **Version warning:** Use `_2.13` artifacts (not `_2.12`). DBR 17.3 ships Scala 2.13. Do NOT install `snowflake-jdbc` separately — it's included transitively.\n\nRestart the cluster after installing all libraries.\n\n### Upload and Configure the Notebook\n\n1. Import `assets/04_databricks_read.ipynb` from your forked repo into your Databricks workspace\n2. Attach it to the configured cluster\n3. Fill in cell 1:\n\n```python\nACCOUNT_IDENTIFIER = \"\u003CORG-ACCT\u003E\"\nPAT_TOKEN          = \"\u003CYOUR_PAT_TOKEN\u003E\"\nSF_USER            = \"\u003CYOUR_SNOWFLAKE_USER\u003E\"\nRSA_PRIVATE_KEY    = \"\u003CBASE64_KEY\u003E\"\nREGION             = \"us-east-1\"\n```\n\n### Path A — yellow_trips (Zero Snowflake Compute)\n\nRun the Path A cell. Databricks reads yellow_trips directly from Snowflake-managed S3 storage using vended STS credentials:\n\n```python\nyellow_df = spark.table(f\"{CAT}.{SCHEMA_NAME}.YELLOW_TRIPS\")\nprint(f\"Row count: {yellow_df.count():,}\")\n```\n\n\u003E **Zero compute cost:** Check your Snowflake credit meter — it will not move. Path A uses zero warehouse compute because no policies are attached.\n\n### Path B — green_trips (Governance Enforced)\n\nRun the Path B cell. The `SnowflakeFallbackCatalog` detects that Horizon returns 403 (policies present) and automatically routes the query through Snowflake compute:\n\n```python\ngreen_df = spark.table(f\"{CAT}.{SCHEMA_NAME}.GREEN_TRIPS\")\nprint(f\"Row count (RLS-filtered, Manhattan only): {green_df.count():,}\")\n```\n\nObserve:\n- `fare_amount`, `tip_amount`, `total_amount` return **-1.0** (masking policy active)\n- Row count is reduced to Manhattan pickups only (RLS active)\n\n### Iceberg V3 VARIANT — nyc_weather_ssv2\n\nRead the VARIANT column using Spark 4.0's native `variant_get` function:\n\n```python\nfrom pyspark.sql.functions import variant_get, col\n\nweather_df = spark.table(f\"{CAT}.{SCHEMA_NAME}.NYC_WEATHER_SSV2\")\n\nweather_df.select(\n    col(\"location\"),\n    variant_get(\"weather_data\", \"$.timezone\", \"string\").alias(\"timezone\"),\n    variant_get(\"weather_data\", \"$.elevation\", \"float\").alias(\"elevation_m\"),\n).show(truncate=False)\n```\n\n### Timestamp Compatibility Demo\n\nRun the nano timestamp cell to observe Spark rejecting `TIMESTAMP_NTZ(9)`:\n\n```python\ntry:\n    nano_df = spark.table(f\"{CAT}.{SCHEMA_NAME}.GREEN_TRIPS_NANO\")\n    nano_df.show(5)\nexcept Exception as e:\n    print(f\"Expected failure: {type(e).__name__}\")\n    print(\"Spark 1.10.x cannot read Iceberg timestamp_ns.\")\n```\n\nThis confirms why the main tables use `TIMESTAMP_NTZ(6)` — an intentional interoperability choice.\n\n\u003C!-- ------------------------ --\u003E\n## Semantic View & Cortex Agent\n\nDuration: 15\n\nIn this step you will create a Semantic View over the Iceberg tables that defines business-friendly dimensions, metrics, and verified queries — then wrap it with a Cortex Agent for natural-language analytics via Snowflake CoWork.\n\n\u003E **Repo shortcut:** The complete script is `assets/05_semantic_view.sql` in your forked repo. You can run it directly in Snowsight. The inline code below is shown for learning — it explains each section of the DDL.\n\n### Why a Semantic View?\n\nA Semantic View is a schema-level object that acts as a \"contract\" between raw data and AI consumers. Cortex Analyst reads the semantic view to translate natural-language questions into accurate SQL. Verified queries (VQRs) provide gold-standard Q&A pairs that dramatically improve answer accuracy.\n\n### Create the Semantic View\n\n```sql\nUSE ROLE DEMO_ADMIN;\nUSE WAREHOUSE DEMO_WH;\nUSE DATABASE ICEBERG_DEMO;\nUSE SCHEMA PUBLIC;\n\nCREATE OR REPLACE SEMANTIC VIEW nyc_taxi_analytics\n\n  TABLES (\n    trips AS ICEBERG_DEMO.PUBLIC.YELLOW_TRIPS\n      WITH SYNONYMS ('yellow taxi', 'yellow cab', 'taxi trips', 'rides')\n      COMMENT = 'NYC Yellow Taxi trip records from Jan 2024 to Feb 2026',\n\n    zones AS ICEBERG_DEMO.PUBLIC.ZONE_LOOKUP\n      PRIMARY KEY (LOCATIONID)\n      WITH SYNONYMS ('taxi zones', 'locations', 'neighborhoods')\n      COMMENT = 'NYC taxi zone lookup — maps LocationID to Borough and Zone name'\n  )\n\n  RELATIONSHIPS (\n    pickup_zone AS\n      trips (PULOCATIONID) REFERENCES zones (LOCATIONID)\n  )\n\n  FACTS (\n    trips.fare AS fare_amount\n      COMMENT = 'Base fare amount in USD',\n    trips.tip AS tip_amount\n      COMMENT = 'Tip amount in USD',\n    trips.total AS total_amount\n      COMMENT = 'Total charge including fare, tips, tolls, surcharges',\n    trips.distance AS trip_distance\n      COMMENT = 'Trip distance in miles',\n    trips.passengers AS passenger_count\n      COMMENT = 'Number of passengers in the vehicle',\n    zones.borough_name AS Borough\n      COMMENT = 'NYC borough name from zone lookup'\n  )\n\n  DIMENSIONS (\n    trips.pickup_datetime AS tpep_pickup_datetime\n      WITH SYNONYMS = ('pickup time', 'start time', 'trip start')\n      COMMENT = 'Timestamp when the taxi meter was engaged',\n\n    trips.dropoff_datetime AS tpep_dropoff_datetime\n      WITH SYNONYMS = ('dropoff time', 'end time', 'trip end')\n      COMMENT = 'Timestamp when the taxi meter was disengaged',\n\n    trips.pickup_hour AS HOUR(tpep_pickup_datetime)\n      WITH SYNONYMS = ('hour of day', 'time of day')\n      COMMENT = 'Hour of pickup (0-23)',\n\n    trips.pickup_date AS DATE(tpep_pickup_datetime)\n      WITH SYNONYMS = ('trip date', 'date')\n      COMMENT = 'Date of pickup',\n\n    trips.pickup_month AS DATE_TRUNC('month', tpep_pickup_datetime)\n      WITH SYNONYMS = ('month', 'trip month')\n      COMMENT = 'Month of pickup',\n\n    trips.pickup_day_of_week AS DAYNAME(tpep_pickup_datetime)\n      WITH SYNONYMS = ('day of week', 'weekday')\n      COMMENT = 'Day of week (Mon, Tue, etc.)',\n\n    trips.payment_method AS\n      CASE payment_type\n        WHEN 1 THEN 'Credit Card'\n        WHEN 2 THEN 'Cash'\n        WHEN 3 THEN 'No Charge'\n        WHEN 4 THEN 'Dispute'\n        ELSE 'Unknown'\n      END\n      WITH SYNONYMS = ('payment type', 'how they paid')\n      COMMENT = 'Human-readable payment method',\n\n    zones.pickup_borough AS BOROUGH\n      WITH SYNONYMS = ('borough', 'pickup area', 'neighborhood')\n      COMMENT = 'NYC borough of pickup location',\n\n    zones.pickup_zone_name AS \"ZONE\"\n      WITH SYNONYMS = ('zone', 'pickup zone', 'pickup neighborhood')\n      COMMENT = 'NYC taxi zone name of pickup'\n  )\n\n  METRICS (\n    trips.total_trips AS COUNT(*)\n      WITH SYNONYMS = ('trip count', 'number of rides', 'ride count', 'how many trips')\n      COMMENT = 'Total number of taxi trips',\n\n    trips.total_revenue AS SUM(trips.total)\n      WITH SYNONYMS = ('revenue', 'total sales', 'earnings', 'income')\n      COMMENT = 'Total revenue in USD (fare + tips + tolls + surcharges)',\n\n    trips.avg_fare AS AVG(trips.fare)\n      WITH SYNONYMS = ('average fare', 'mean fare', 'typical fare')\n      COMMENT = 'Average base fare amount in USD',\n\n    trips.avg_tip_pct AS AVG(trips.tip / NULLIF(trips.fare, 0)) * 100\n      WITH SYNONYMS = ('tip percentage', 'tip rate', 'tipping rate', 'average tip percent')\n      COMMENT = 'Average tip as a percentage of fare',\n\n    trips.avg_distance AS AVG(trips.distance)\n      WITH SYNONYMS = ('average distance', 'mean distance', 'typical trip length')\n      COMMENT = 'Average trip distance in miles',\n\n    trips.avg_passengers AS AVG(trips.passengers)\n      WITH SYNONYMS = ('average passengers', 'riders per trip')\n      COMMENT = 'Average number of passengers per trip'\n  )\n\n  COMMENT = 'NYC Yellow Taxi analytics — trip volumes, revenue, tipping, and geography. Covers Jan 2024 through Feb 2026.'\n\n  AI_SQL_GENERATION 'Always round currency values to 2 decimal places. Always round percentages to 1 decimal place. When computing tip percentage, exclude rows where fare_amount is 0 or NULL. Default sort order is descending by the primary metric unless the user specifies otherwise.'\n\n  AI_QUESTION_CATEGORIZATION 'This semantic view covers NYC yellow taxi trip data only. It does NOT contain weather data, green taxi data, or airport information. If asked about weather, precipitation, temperature, or green taxis, respond that you do not have that information available.'\n\n  AI_VERIFIED_QUERIES (\n    revenue_by_borough AS (\n      QUESTION 'What is total revenue by borough?'\n      ONBOARDING_QUESTION TRUE\n      VERIFIED_BY '(STEWARD = demo_admin)'\n      SQL 'SELECT\n               __zones.pickup_borough,\n               ROUND(SUM(__trips.total), 2) AS total_revenue\n             FROM __trips\n             LEFT JOIN __zones ON __trips.pulocationid = __zones.locationid\n             GROUP BY __zones.pickup_borough\n             ORDER BY total_revenue DESC'\n    ),\n\n    avg_fare_by_payment AS (\n      QUESTION 'What is the average fare by payment method?'\n      ONBOARDING_QUESTION TRUE\n      VERIFIED_BY '(STEWARD = demo_admin)'\n      SQL 'SELECT\n               __trips.payment_method,\n               ROUND(AVG(__trips.fare), 2) AS avg_fare\n             FROM __trips\n             GROUP BY __trips.payment_method\n             ORDER BY avg_fare DESC'\n    ),\n\n    busiest_day_of_week AS (\n      QUESTION 'Which day of the week has the most trips?'\n      ONBOARDING_QUESTION TRUE\n      VERIFIED_BY '(STEWARD = demo_admin)'\n      SQL 'SELECT\n               __trips.pickup_day_of_week,\n               COUNT(*) AS total_trips\n             FROM __trips\n             GROUP BY __trips.pickup_day_of_week\n             ORDER BY total_trips DESC'\n    ),\n\n    tip_pct_by_borough AS (\n      QUESTION 'What is the average tip percentage by borough?'\n      VERIFIED_BY '(STEWARD = demo_admin)'\n      SQL 'SELECT\n               __zones.pickup_borough,\n               ROUND(AVG(__trips.tip / NULLIF(__trips.fare, 0)) * 100, 1) AS avg_tip_pct\n             FROM __trips\n             LEFT JOIN __zones ON __trips.pulocationid = __zones.locationid\n             WHERE __trips.fare \u003E 0\n             GROUP BY __zones.pickup_borough\n             ORDER BY avg_tip_pct DESC'\n    ),\n\n    monthly_trip_volume AS (\n      QUESTION 'Show me monthly trip volume over time'\n      VERIFIED_BY '(STEWARD = demo_admin)'\n      SQL 'SELECT\n               __trips.pickup_month,\n               COUNT(*) AS total_trips\n             FROM __trips\n             GROUP BY __trips.pickup_month\n             ORDER BY __trips.pickup_month'\n    )\n  );\n```\n\n### Verify the Semantic View\n\n```sql\nDESCRIBE SEMANTIC VIEW nyc_taxi_analytics;\nSHOW SEMANTIC DIMENSIONS IN nyc_taxi_analytics;\nSHOW SEMANTIC METRICS IN nyc_taxi_analytics;\n```\n\n### Test with a Direct Semantic Query\n\n```sql\nSELECT * FROM SEMANTIC_VIEW(\n  nyc_taxi_analytics\n  METRICS trips.total_trips, trips.total_revenue\n  DIMENSIONS zones.pickup_borough\n) ORDER BY total_revenue DESC;\n```\n\n### Create the Cortex Agent\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE AGENT nyc_taxi_agent\n  COMMENT = 'NYC Yellow Taxi trip analyst — answers questions about trip volumes, revenue, tips, and geography'\n  FROM SPECIFICATION $$\ntools:\n  - tool_spec:\n      type: cortex_analyst_text_to_sql\n      name: taxi_analytics\n      description: \"Answers questions about NYC yellow taxi trips including revenue, trip counts, tip percentages, payment methods, boroughs, and time-based trends. Covers January 2024 through February 2026.\"\ntool_resources:\n  taxi_analytics:\n    semantic_view: \"ICEBERG_DEMO.PUBLIC.NYC_TAXI_ANALYTICS\"\n    execution_environment:\n      type: warehouse\n      warehouse: \"DEMO_WH\"\n$$;\n\nGRANT USAGE ON AGENT ICEBERG_DEMO.PUBLIC.nyc_taxi_agent TO ROLE DEMO_ADMIN;\nGRANT USAGE ON AGENT ICEBERG_DEMO.PUBLIC.nyc_taxi_agent TO ROLE DEMO_ANALYST;\n```\n\n### Test the Agent\n\n```sql\nUSE ROLE DEMO_ADMIN;\nUSE WAREHOUSE DEMO_WH;\n\nSELECT TRY_PARSE_JSON(\n  SNOWFLAKE.CORTEX.DATA_AGENT_RUN(\n    'ICEBERG_DEMO.PUBLIC.NYC_TAXI_AGENT',\n    $${ \n      \"messages\": [\n        {\n          \"role\": \"user\",\n          \"content\": [{\"type\": \"text\", \"text\": \"What is total revenue by borough?\"}]\n        }\n      ]\n    }$$\n  )\n) AS resp;\n```\n\n### Try it in Snowflake CoWork\n\nOpen Snowflake CoWork in Snowsight: **AI & ML → Snowflake CoWork → select nyc_taxi_agent → start chatting.**\n\nQuestions to try:\n1. \"What is total revenue by borough?\" (hits VQR — high confidence)\n2. \"Which day of the week is busiest?\" (hits VQR)\n3. \"Show me monthly trip trends\" (hits VQR)\n4. \"What's the average tip percentage for credit card vs cash?\" (cross-dimension)\n5. \"How many trips happened in Manhattan in January 2025?\" (filter + dimension)\n\nGuardrail tests:\n- \"What was the weather like on rainy days?\" → Agent declines (no weather data in SV)\n- \"Show me green taxi trips\" → Agent declines (no green_trips in SV)\n\n\u003E **How guardrails work:** The agent's guardrails are not magic — they come from `AI_QUESTION_CATEGORIZATION` in the semantic view DDL. You define what the AI should and should not answer.\n\n\u003C!-- ------------------------ --\u003E\n## RBAC Enforcement Demo\n\nDuration: 5\n\nThis step demonstrates two critical RBAC enforcement points:\n\n1. **Governance on Iceberg tables** — The masking and RLS policies you applied in Module 3 are enforced via role-based access. A role without SELECT on the governed tables sees nothing.\n2. **Governance on AI interfaces** — The Semantic View and Cortex Agent you built in the previous step respect the same privilege model. A role without grants on the semantic view or underlying tables cannot query through Snowflake CoWork either. There is no bypass — not through SQL, not through AI.\n\n### Create a Denied Role\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE ROLE IF NOT EXISTS DEMO_SV_DENIED\n  COMMENT = 'Role with no semantic view or table access — for RBAC demo';\nGRANT ROLE DEMO_SV_DENIED TO ROLE ACCOUNTADMIN;\nGRANT USAGE ON WAREHOUSE DEMO_WH TO ROLE DEMO_SV_DENIED;\nGRANT USAGE ON DATABASE ICEBERG_DEMO TO ROLE DEMO_SV_DENIED;\nGRANT USAGE ON SCHEMA ICEBERG_DEMO.PUBLIC TO ROLE DEMO_SV_DENIED;\n```\n\n### Verify Access is Denied\n\n```sql\nUSE ROLE DEMO_SV_DENIED;\nUSE SECONDARY ROLES NONE;\nUSE WAREHOUSE DEMO_WH;\n\nSELECT * FROM SEMANTIC_VIEW(\n  nyc_taxi_analytics\n  METRICS trips.total_trips\n  DIMENSIONS zones.pickup_borough\n);\n-- Expected: Insufficient privileges error\n```\n\n### Restore Admin Context\n\n```sql\nUSE ROLE DEMO_ADMIN;\nUSE SECONDARY ROLES ALL;\nUSE WAREHOUSE DEMO_WH;\n```\n\n\u003E **Try it yourself:** Switch to the `DEMO_SV_DENIED` role in Snowflake CoWork and ask the agent a question. It will fail. RBAC applies to every access path — SQL, API, and AI.\n\n\u003C!-- ------------------------ --\u003E\n## Explore with Snowflake CoCo (Optional)\n\nDuration: 10\n\nThis optional step provides curated prompts you can paste into Snowflake CoCo (CoCo) — Snowflake's AI coding assistant — to explore the entire quickstart interactively.\n\n### How to Use\n\n1. Open Snowflake CoCo in Snowsight (lower-right panel) or use the CLI\n2. Copy any prompt below and paste it into CoCo\n3. CoCo will generate SQL, explain concepts, or build artifacts for you\n\n### Data Engineering Prompts\n\n```\n\"What tables exist in ICEBERG_DEMO.PUBLIC? Describe each one including\n row counts, column types, and whether they are Iceberg tables.\"\n\n\"Explain the difference between CATALOG = SNOWFLAKE and using an external\n volume with a customer-managed bucket. What are the tradeoffs?\"\n\n\"Create a new Iceberg table called trip_summary that aggregates yellow_trips\n by pickup date and borough with total trips, revenue, and average fare.\"\n```\n\n### Streaming & VARIANT Prompts\n\n```\n\"Explore the nyc_weather_ssv2 table. Show me how to extract hourly\n temperature and precipitation from the weather_data VARIANT column for JFK.\"\n\n\"Write a query that finds the rainiest days at each airport station using\n the VARIANT data in nyc_weather_ssv2. Flatten the hourly array.\"\n\n\"Write a query that joins nyc_weather_ssv2 weather data with yellow_trips\n to see if rainy days have lower tip percentages.\"\n```\n\n### Governance Prompts\n\n```\n\"Explain what masking and row-level security policies are applied in\n ICEBERG_DEMO.PUBLIC. Which tables and columns are protected?\"\n\n\"Show me how to verify that the borough_rls policy is working. Write\n queries that demonstrate filtered vs unfiltered results by role.\"\n\n\"What is the difference between Path A and Path B when Databricks reads\n through Horizon? Explain the governance enforcement mechanism.\"\n```\n\n### Semantic View & Agent Prompts\n\n```\n\"Show me the semantic view nyc_taxi_analytics. What dimensions, metrics,\n and verified queries does it define?\"\n\n\"Add a new metric to the nyc_taxi_analytics semantic view called\n avg_trip_duration that calculates average minutes between pickup and dropoff.\"\n\n\"Write a new verified query (VQR) for the semantic view that answers:\n What is the average trip distance by hour of day?\"\n```\n\n### Open-Ended Exploration\n\n```\n\"Build a Streamlit dashboard that shows a bar chart of total trips by\n borough and a line chart of daily revenue trends from yellow_trips.\"\n\n\"Compare yellow_trips and green_trips — what are the differences in schema,\n row counts, and trip patterns? Which boroughs does each serve?\"\n\n\"Write a data quality check that validates yellow_trips has no future\n pickup dates, no negative fares, and no null location IDs.\"\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nDuration: 3\n\nCongratulations! You have built a complete zero-tax analytics pipeline — from raw Iceberg ingestion through natural-language AI queries — with governance enforced across engine boundaries.\n\n### Cleanup\n\nWhen you are done, use the teardown scripts in your forked repo:\n\n- **`assets/teardown_full.sql`** — Drops all tables, pipes, policies, semantic view, agent, and tags. Preserves database, warehouse, roles, and stage.\n- **`assets/teardown_governance.sql`** — Removes only governance objects (policies, tags, grants). Tables and data are preserved so you can re-apply policies without rebuilding Iceberg tables.\n\nOpen the appropriate script in Snowsight and run all statements sequentially.\n\n### What You Learned\n\n- **Zero Storage Tax** — Create Iceberg tables with `CATALOG = SNOWFLAKE` — single copy, no bucket management, readable by any engine\n- **Zero Interoperability Tax** — Horizon IRC serves Iceberg metadata to Spark/Databricks with governance enforced server-side\n- **Zero AI Access Tax** — Semantic View + Cortex Agent turn business questions into SQL without bespoke pipelines\n- **Snowpipe Streaming V2** — Real-time ingest into Iceberg V3 VARIANT columns\n- **Path A vs Path B** — Vended credentials (zero compute) vs compute-routed governance — same endpoint, different outcome\n- **RBAC Everywhere** — Privilege enforcement across SQL, AI agents, and external engines\n\n### Related Resources\n\n- [Snowflake Storage for Iceberg Documentation](https://docs.snowflake.com/en/user-guide/tables-iceberg)\n- [Horizon Iceberg REST Catalog](https://docs.snowflake.com/en/user-guide/tables-iceberg-rest-catalog)\n- [Snowpipe Streaming V2](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-overview)\n- [Semantic Views](https://docs.snowflake.com/en/user-guide/semantic-views)\n- [Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents)\n- [Column Masking Policies](https://docs.snowflake.com/en/user-guide/security-column-ddm-intro)\n- [Row Access Policies](https://docs.snowflake.com/en/user-guide/security-row-intro)\n- [GitHub Repository](https://github.com/Snowflake-Labs/sfguide-iceberg-zero-tax-analytics)\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-38aa45041f","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-60f0d3b2a8",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-6020e522e4","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-19",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-30e30e1df5","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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