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attention?"\u003C/em\u003E, and gets a governed, AI-enriched answer — drawn in real time from Iceberg tables that live across two completely separate catalogs.\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EBehind that single query:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOrder data originates in \u003Cstrong\u003EDatabricks Unity Catalog\u003C/strong\u003E (Delta + Iceberg UniForm tables on Databricks-managed S3)\u003C/li\u003E\u003Cli\u003ESnowflake federates those tables into a \u003Cstrong\u003ECatalog-Linked Database\u003C/strong\u003E — no data movement, no copy jobs\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowpark Connect\u003C/strong\u003E reads the federated tables and calls \u003Cstrong\u003ESnowflake Cortex\u003C/strong\u003E to classify risk and generate operational notes inline in SQL\u003C/li\u003E\u003Cli\u003EResults are written to a new \u003Cstrong\u003ESnowflake-managed Iceberg table\u003C/strong\u003E on Snowflake's own storage\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EHorizon governance\u003C/strong\u003E — column masking and row access policies — applies to every table, including the AI-generated \u003Ccode\u003Erisk_level\u003C/code\u003E column\u003C/li\u003E\u003Cli\u003EWhen a non-admin user asks the same question, Cortex Analyst returns \u003Ccode\u003E*** RESTRICTED ***\u003C/code\u003E for HIGH risk orders — governance fires through conversational AI just as it does through SQL\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis is federated Iceberg analytics with AI enrichment and end-to-end governance, all without rewriting pipelines or duplicating data.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EThe Problem\u003C/h3\u003E\n","\u003Cp\u003EData teams increasingly run Iceberg tables in multiple catalogs — Snowflake, Databricks, AWS Glue. Getting a unified, governed, AI-powered analytics layer across all of them requires stitching together bespoke ETL, cross-catalog security policies, and custom ML pipelines.\u003C/p\u003E\n","\u003Cp\u003EThis quickstart shows how to replace that with a single, open-standards approach:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIceberg everywhere + Snowpark Connect as the unified compute layer + Horizon for governance + Cortex for AI\u003C/strong\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EWhat You Build\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1 — Snowflake-managed Iceberg + Horizon governance\u003C/strong\u003E (\u003Ccode\u003E01\u003C/code\u003E, \u003Ccode\u003E02\u003C/code\u003E)\nCreate two Snowflake-managed Iceberg tables. Apply column masking (hide \u003Ccode\u003Esensitive_data\u003C/code\u003E from non-admin roles) and a row access policy (filter historical rows). Use Snowpark Connect to run PySpark DataFrames against them — governance fires on every query because Snowpark Connect routes through Snowflake's SQL engine.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2 — Federate external Iceberg tables + Snowpark Connect governance\u003C/strong\u003E (\u003Ccode\u003E04\u003C/code\u003E, \u003Ccode\u003E05\u003C/code\u003E)\nCreate Iceberg tables in Databricks Unity Catalog (Delta + UniForm). Federate them into Snowflake as a Catalog-Linked Database. Apply Snowflake's own Horizon masking policy to \u003Ccode\u003Ecredit_card\u003C/code\u003E — independent of any Databricks policies. Snowpark Connect queries the federated tables with live role-based masking.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3 — Cortex AI enrichment pipeline → Cortex Analyst\u003C/strong\u003E (\u003Ccode\u003E06\u003C/code\u003E, \u003Ccode\u003E07\u003C/code\u003E)\nSnowpark Connect reads from both catalogs. Cortex classifies each order as HIGH / MEDIUM / LOW risk and generates a one-sentence operational note. Results are written to a new Snowflake-managed Iceberg table. Horizon masking applies to \u003Ccode\u003Erisk_level\u003C/code\u003E exactly as it does to raw columns. A Cortex Analyst semantic view spans all three Iceberg tables — ask questions in natural language from Snowsight, with governance enforced per active role.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDownload the code:\u003C/strong\u003E\n\u003Ca href=\"https://download-directory.github.io/?url=https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets\"\u003EDownload all files (ZIP)\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EOr download individual files:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/01_sf_iceberg_catalog_setup.sql\"\u003E01_sf_iceberg_catalog_setup.sql\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/02_sf_iceberg_demo.ipynb\"\u003E02_sf_iceberg_demo.ipynb\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/03_databricks_rw_sf_iceberg.py\"\u003E03_databricks_rw_sf_iceberg.py\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/04_databricks_create_uc_tables.py\"\u003E04_databricks_create_uc_tables.py\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/05_databricks_federation_demo.ipynb\"\u003E05_databricks_federation_demo.ipynb\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/06_cortex_ai_pipeline.ipynb\"\u003E06_cortex_ai_pipeline.ipynb\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/07_ai_pipeline.ipynb\"\u003E07_ai_pipeline.ipynb\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow \u003Cstrong\u003ESnowpark Connect\u003C/strong\u003E runs PySpark DataFrames through Snowflake's SQL engine — enforcing Horizon governance on every query regardless of the caller\u003C/li\u003E\u003Cli\u003EHow \u003Cstrong\u003ECatalog-Linked Databases\u003C/strong\u003E federate externally-managed Iceberg tables into Snowflake with independent Horizon governance applied at query time\u003C/li\u003E\u003Cli\u003EHow \u003Cstrong\u003ECortex LLM functions\u003C/strong\u003E enrich federated Iceberg data inline in SQL, writing AI results to Snowflake-managed Iceberg\u003C/li\u003E\u003Cli\u003EWhy \u003Cstrong\u003EHorizon masking applies to AI-generated columns\u003C/strong\u003E exactly as it does to raw data — no extra configuration\u003C/li\u003E\u003Cli\u003EHow \u003Cstrong\u003ECortex Analyst\u003C/strong\u003E queries a semantic view spanning multiple Iceberg catalogs with governance enforced per role\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey Capabilities\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EFederated Iceberg\u003C/strong\u003E: Query Iceberg tables across Snowflake and external catalogs from a single Snowflake Workspace notebook\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGovernance at the SQL layer\u003C/strong\u003E: Snowpark Connect routes every query through Snowflake's SQL engine — Horizon policies apply regardless of the caller's role\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIndependent governance on federated tables\u003C/strong\u003E: Snowflake applies its own masking policies on top of externally-managed tables, independently of the source catalog's policies\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAI enrichment on Iceberg\u003C/strong\u003E: Cortex LLM functions run inline in SQL on federated data, writing results to Snowflake-managed Iceberg\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGoverned AI output\u003C/strong\u003E: Horizon masking applies to Cortex-generated columns exactly as it does to raw data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConversational analytics\u003C/strong\u003E: Cortex Analyst semantic view spans SF-managed and federated Iceberg tables — natural language queries with governance enforced per active role\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/\"\u003ESnowflake account\u003C/a\u003E with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E access and Cortex LLM functions enabled (us-east-1 or us-west-2)\u003C/li\u003E\u003Cli\u003EA Databricks workspace with Unity Catalog enabled (for Step 2)\u003C/li\u003E\u003Cli\u003EAn S3 bucket accessible from Snowflake via an external volume (\u003Ccode\u003ECREATE EXTERNAL VOLUME\u003C/code\u003E)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Snowflake SQL and the Snowsight UI\u003C/li\u003E\u003Cli\u003EBasic familiarity with PySpark DataFrames\u003C/li\u003E\u003Cli\u003EAn external volume already configured in Snowflake (\u003Ccode\u003ECREATE EXTERNAL VOLUME\u003C/code\u003E)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EArchitecture\u003C/h2\u003E\n","\u003Ch3\u003EScenario 1 — Snowflake-Managed Iceberg + Snowpark Connect\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003ESnowflake Account\n  │  CREATE ICEBERG TABLE ... CATALOG = 'SNOWFLAKE'\n  │  Horizon governance: column masking, row access policy\n  │\n  └─ Snowpark Connect\n       spark.table("DEMO_SCHEMA.OPEN_TABLE")\n       spark.table("DEMO_SCHEMA.PROTECTED_TABLE")\n       → queries route through Snowflake SQL engine\n       → Horizon policies ARE enforced\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EScenario 2 — Federate External Iceberg + Cortex AI Enrichment\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003EExternal Catalog (Unity Catalog)\n  │  Delta + Iceberg UniForm tables\n  │  Iceberg REST endpoint → Snowflake Catalog Integration\n  │\nSnowflake Catalog-Linked Database (DATABRICKS_DEMO_DB)\n  │  Auto-discovers schemas + tables every 30 s\n  │  Applies independent Horizon masking policies\n  │\n  └─ Snowpark Connect\n       spark.sql("SELECT * FROM DATABRICKS_DEMO_DB.horizon_demo.customer_orders")\n       sf_session.sql(CTAS + CORTEX.COMPLETE → AI_ORDER_INSIGHTS)\n       → Snowflake SQL engine enforces masking on both raw and AI columns\n       → Cortex Analyst: natural language queries on AI-enriched Iceberg table\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGovernance contrast (external engine vs Snowpark Connect):\u003C/strong\u003E An external Spark engine reading via Horizon IRC receives raw Parquet via vended S3 credentials — Snowflake SQL-layer policies do not apply to that path. Snowpark Connect routes through the Snowflake SQL engine, so Horizon masking and row access policies are always enforced. See \u003Ccode\u003E03_databricks_rw_sf_iceberg.py\u003C/code\u003E for the full Databricks IRC demo.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDatabricks Cluster Setup (Scenario 2 prerequisites)\u003C/h2\u003E\n","\u003Cp\u003EScenarios 1 and 2 Snowflake notebooks run entirely within Snowflake — no external cluster needed.\u003C/p\u003E\n","\u003Cp\u003EScenario 2 requires two Databricks notebooks run once as setup:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ENotebook\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECluster needed\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E04_databricks_create_uc_tables.py\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESingle User + Unity Catalog, DBR 13.3 LTS+\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E03_databricks_rw_sf_iceberg.py\u003C/code\u003E \u003Cem\u003E(optional governance contrast)\u003C/em\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESingle User, no UC, DBR 14.3 LTS+, Maven: \u003Ccode\u003Eorg.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.9.1\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EScenario 1 — Snowflake Setup\u003C/h2\u003E\n","\u003Cp\u003ERun \u003Ccode\u003E01_sf_iceberg_catalog_setup.sql\u003C/code\u003E in a Snowflake worksheet as \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EThis creates two Snowflake-managed Iceberg tables and applies Horizon governance policies.\u003C/p\u003E\n","\u003Ch3\u003ECreate Database and Tables\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nUSE WAREHOUSE LOAD_WH;\n\nCREATE DATABASE IF NOT EXISTS HORIZON_DEMO_SFDB\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    COMMENT = 'Snowflake-managed Iceberg catalog (Horizon IRC)';\n\nCREATE SCHEMA IF NOT EXISTS HORIZON_DEMO_SFDB.DEMO_SCHEMA;\n\n-- OPEN_TABLE: no governance restrictions\nCREATE OR REPLACE ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.OPEN_TABLE (\n    id          INT,\n    product     STRING,\n    quantity    INT,\n    price       DECIMAL(10, 2),\n    created_at  TIMESTAMP\n)\nCATALOG = 'SNOWFLAKE';\n\n-- PROTECTED_TABLE: column masking + row access policy applied below\nCREATE OR REPLACE ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.PROTECTED_TABLE (\n    id              INT,\n    customer_name   STRING,\n    sensitive_data  STRING,\n    amount          DECIMAL(10, 2),\n    created_at      TIMESTAMP\n)\nCATALOG = 'SNOWFLAKE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EApply Horizon Governance Policies\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Column masking: hide sensitive_data from non-admin roles\nCREATE OR REPLACE MASKING POLICY HORIZON_DEMO_SFDB.DEMO_SCHEMA.MASK_SENSITIVE\n    AS (val STRING) RETURNS STRING ->\n    CASE\n        WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN', 'SYSADMIN') THEN val\n        ELSE '*** MASKED ***'\n    END;\n\nALTER ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.PROTECTED_TABLE\n    MODIFY COLUMN sensitive_data\n    SET MASKING POLICY HORIZON_DEMO_SFDB.DEMO_SCHEMA.MASK_SENSITIVE;\n\n-- Row access policy: non-admin roles see current-year rows only\nCREATE OR REPLACE ROW ACCESS POLICY HORIZON_DEMO_SFDB.DEMO_SCHEMA.CURRENT_YEAR_ONLY\n    AS (created_at TIMESTAMP) RETURNS BOOLEAN ->\n        CURRENT_ROLE() IN ('ACCOUNTADMIN', 'SYSADMIN')\n        OR YEAR(created_at) = YEAR(CURRENT_DATE());\n\nALTER ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.PROTECTED_TABLE\n    ADD ROW ACCESS POLICY HORIZON_DEMO_SFDB.DEMO_SCHEMA.CURRENT_YEAR_ONLY\n    ON (created_at);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfigure Credential Vending (for Scenario 2)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- OPEN_TABLE_RW: SELECT + writes → Snowflake vends write-capable S3 credentials\nCREATE DATABASE ROLE IF NOT EXISTS HORIZON_DEMO_SFDB.OPEN_TABLE_RW;\nGRANT SELECT, INSERT, UPDATE, DELETE ON TABLE\n    HORIZON_DEMO_SFDB.DEMO_SCHEMA.OPEN_TABLE\n    TO DATABASE ROLE HORIZON_DEMO_SFDB.OPEN_TABLE_RW;\n\n-- PROTECTED_TABLE_RO: SELECT only → Snowflake vends read-only S3 credentials\nCREATE DATABASE ROLE IF NOT EXISTS HORIZON_DEMO_SFDB.PROTECTED_TABLE_RO;\nGRANT SELECT ON TABLE\n    HORIZON_DEMO_SFDB.DEMO_SCHEMA.PROTECTED_TABLE\n    TO DATABASE ROLE HORIZON_DEMO_SFDB.PROTECTED_TABLE_RO;\n\nGRANT DATABASE ROLE HORIZON_DEMO_SFDB.OPEN_TABLE_RW      TO ROLE $SF_EXT_COMPUTE_ROLE;\nGRANT DATABASE ROLE HORIZON_DEMO_SFDB.PROTECTED_TABLE_RO TO ROLE $SF_EXT_COMPUTE_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGenerate PAT (for Scenario 2)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- ⚠ Copy the token immediately — shown only once\nALTER USER <YOUR_USERNAME>\n    ADD PROGRAMMATIC ACCESS TOKEN MY_DEMO_PAT\n    COMMENT = 'Iceberg Federation Demo — Horizon IRC integration';\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EScenario 1 — Snowpark Connect Governance Demo\u003C/h2\u003E\n","\u003Cp\u003EUpload \u003Ccode\u003E02_sf_iceberg_demo.ipynb\u003C/code\u003E to your Snowflake workspace:\n\u003Cstrong\u003ESnowflake Workspaces → drag and drop \u003Ccode\u003E02_sf_iceberg_demo.ipynb\u003C/code\u003E into the file tree\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Ccode\u003Esnowpark-connect\u003C/code\u003E is pre-installed in Snowflake Workspaces — no package picker step needed.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EInitialize Session\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.snowpark.context import get_active_session\nfrom snowflake import snowpark_connect\nfrom pyspark.sql.functions import col\nimport pandas as pd\n\nsf_session = get_active_session()\nsf_session.sql(f"USE DATABASE {SF_MANAGED_ICEBERG_DB}").collect()\n\nspark = snowpark_connect.init_spark_session()\n\ndef switch_role(role):\n    sf_session.sql(f"USE ROLE {role}").collect()\n    print(f"Active role → {role}")\n\nswitch_role("ACCOUNTADMIN")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDemo 1 — Read OPEN_TABLE\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Espark.table(TBL_OPEN).orderBy("id").show(truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected:\u003C/strong\u003E 3 rows (Laptop, Mouse, Keyboard). All columns visible. No policies applied.\u003C/p\u003E\n","\u003Ch3\u003EDemo 2 — ACCOUNTADMIN Reads PROTECTED_TABLE\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eswitch_role("ACCOUNTADMIN")\nspark.table(TBL_PROTECTED).orderBy("id").show(truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected:\u003C/strong\u003E 3 rows including the 2023 row. \u003Ccode\u003Esensitive_data\u003C/code\u003E shows raw values (e.g. \u003Ccode\u003ESSN-123-45-6789\u003C/code\u003E).\u003C/p\u003E\n","\u003Ch3\u003EDemo 3 — Reader Role Reads PROTECTED_TABLE\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eswitch_role(SF_READER_ROLE)\nspark.table(TBL_PROTECTED).orderBy("id").show(truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected:\u003C/strong\u003E 2 rows (2023 row filtered by row access policy). \u003Ccode\u003Esensitive_data\u003C/code\u003E shows \u003Ccode\u003E*** MASKED ***\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EDemo 4 — Side-by-Side Comparison\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003ECOLS = ["id", "customer_name", "sensitive_data", "amount"]\n\nswitch_role("ACCOUNTADMIN")\nadmin_rows = spark.table(TBL_PROTECTED).select(*COLS).collect()\n\nswitch_role(SF_READER_ROLE)\nreader_rows = spark.table(TBL_PROTECTED).select(*COLS).collect()\n\nadmin_pd  = pd.DataFrame([r.asDict() for r in admin_rows])\nadmin_pd.insert(0, "role", "ACCOUNTADMIN")\n\nreader_pd = pd.DataFrame([r.asDict() for r in reader_rows])\nreader_pd.insert(0, "role", SF_READER_ROLE)\n\ncombined = pd.concat([admin_pd, reader_pd]).sort_values(["id", "role"]).reset_index(drop=True)\nprint(combined.to_string(index=False))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected:\u003C/strong\u003E Same Iceberg table, same Parquet files — ACCOUNTADMIN sees 3 unmasked rows, reader role sees 2 masked rows. This is Snowflake Horizon governance enforced through Snowpark Connect.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey insight:\u003C/strong\u003E Snowpark Connect routes queries through the Snowflake SQL engine. Horizon policies apply to every query regardless of the PySpark API used. Scenario 2 demonstrates the contrast — an external engine reading the same table bypasses these policies.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGovernance Contrast — Snowpark Connect vs External Engine\u003C/h2\u003E\n","\u003Cp\u003EThe same Snowflake-managed Iceberg table returns different results depending on the read path.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAccess path\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EEngine\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERoute\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EHorizon policies\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpark Connect (Scenarios 1 & 2)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake SQL engine\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELogical query plan\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E✅ Enforced\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHorizon IRC (external Spark)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDatabricks / any Spark\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERaw Parquet via vended S3 creds\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E❌ Bypassed\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowpark Connect — PROTECTED_TABLE (masking + row filter enforced):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eswitch_role("EXT_COMPUTE_ENG_DEMO_ROLE")\nspark.table("HORIZON_DEMO_SFDB.DEMO_SCHEMA.PROTECTED_TABLE").show()\n# 2 rows (2023 row filtered) │ sensitive_data: *** MASKED ***\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExternal Spark via Horizon IRC — same table (raw Parquet):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Databricks reads via vended S3 credentials\nspark.table("sf_horizon.DEMO_SCHEMA.PROTECTED_TABLE").show()\n# 3 rows (all rows) │ sensitive_data: raw value visible\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESnowflake's masking and row access policies execute inside the SQL engine. An engine that bypasses the SQL engine and reads Parquet files directly will never see those policies fire.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECredential vending also enforces write access:\u003C/strong\u003E \u003Ccode\u003EPROTECTED_TABLE_RO\u003C/code\u003E (SELECT only) → Snowflake vends read-only S3 credentials → any \u003Ccode\u003Es3:PutObject\u003C/code\u003E attempt returns 403. \u003Ccode\u003EOPEN_TABLE_RW\u003C/code\u003E (INSERT/UPDATE/DELETE) → write-capable credentials vended. No application code required — the catalog decides.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFull Databricks IRC notebook:\u003C/strong\u003E \u003Ccode\u003E03_databricks_rw_sf_iceberg.py\u003C/code\u003E (reads, writes, credential vending demo).\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EScenario 2 — External Catalog Setup\u003C/h2\u003E\n","\u003Cp\u003EAttach \u003Ccode\u003E04_databricks_create_uc_tables.py\u003C/code\u003E to \u003Cstrong\u003ECluster B\u003C/strong\u003E (Unity Catalog enabled).\u003C/p\u003E\n","\u003Ch3\u003ECreate Catalog, Schema, and Tables\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003ECATALOG_NAME = "<DBX_UC_CATALOG>"    # e.g. my_demo\nSCHEMA_NAME  = "horizon_demo"         # pre-set\n\nspark.sql(f"CREATE CATALOG IF NOT EXISTS {CATALOG_NAME}")\nspark.sql(f"CREATE SCHEMA  IF NOT EXISTS {CATALOG_NAME}.{SCHEMA_NAME}")\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Espark.sql(f"""\nCREATE OR REPLACE TABLE {CATALOG_NAME}.{SCHEMA_NAME}.customer_orders (\n    order_id BIGINT, customer_id BIGINT, product STRING,\n    amount DECIMAL(10,2), order_date DATE, status STRING\n)\nUSING DELTA\nTBLPROPERTIES (\n    'delta.universalFormat.enabledFormats' = 'iceberg',\n    'delta.enableIcebergCompatV2'          = 'true',\n    'delta.columnMapping.mode'             = 'name'\n)\n""")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDelta UniForm writes standard Delta files \u003Cstrong\u003Eand\u003C/strong\u003E Iceberg metadata in parallel — no data duplication, no ETL.\u003C/p\u003E\n","\u003Ch3\u003EApply External Catalog Governance\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Column mask on credit_card (applies within the external catalog only)\nspark.sql(f"""\nCREATE OR REPLACE FUNCTION {CATALOG_NAME}.{SCHEMA_NAME}.mask_credit_card(cc STRING)\nRETURN CASE WHEN is_account_group_member('account_unity_admin') THEN cc\n            ELSE CONCAT('****-****-****-', RIGHT(cc, 4)) END\n""")\nspark.sql(f"""ALTER TABLE {CATALOG_NAME}.{SCHEMA_NAME}.sensitive_orders\nALTER COLUMN credit_card SET MASK {CATALOG_NAME}.{SCHEMA_NAME}.mask_credit_card""")\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThese external catalog policies apply within that catalog only. Snowflake applies its own independent governance policies in the next step.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EGet the Iceberg REST Endpoint\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003EWORKSPACE_HOST = "<DBX_WORKSPACE_HOST>"\nprint(f"CATALOG_URI  : https://{WORKSPACE_HOST}/api/2.1/unity-catalog/iceberg-rest")\nprint(f"CATALOG_NAME : {CATALOG_NAME}")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECopy these values into \u003Ccode\u003E05_databricks_federation_demo.ipynb\u003C/code\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EScenario 2 — Snowflake Setup\u003C/h2\u003E\n","\u003Cp\u003EOpen \u003Ccode\u003E05_databricks_federation_demo.ipynb\u003C/code\u003E in a Snowflake Workspace notebook (drag and drop into the file tree).\u003C/p\u003E\n","\u003Ch3\u003ECreate Catalog Integration\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CATALOG INTEGRATION MY_DATABRICKS_UC_CI\n    CATALOG_SOURCE = ICEBERG_REST\n    TABLE_FORMAT   = ICEBERG\n    REST_CONFIG = (\n        CATALOG_URI  = 'https://<DBX_WORKSPACE_HOST>/api/2.1/unity-catalog/iceberg-rest'\n        CATALOG_NAME = '<DBX_UC_CATALOG>'\n        ACCESS_DELEGATION_MODE = EXTERNAL_VOLUME_CREDENTIALS\n    )\n    REST_AUTHENTICATION = (\n        TYPE         = BEARER\n        BEARER_TOKEN = '<DBX_PAT_TOKEN>'\n    )\n    ENABLED = TRUE;\n\n-- Verify connectivity — must return {"success": true}\nSELECT SYSTEM$VERIFY_CATALOG_INTEGRATION('MY_DATABRICKS_UC_CI');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Catalog-Linked Database\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS DATABRICKS_DEMO_DB\n    EXTERNAL_VOLUME = 'ICEBERG_EXTERNAL_S3_VOLUME'\n    LINKED_CATALOG = ( CATALOG = 'MY_DATABRICKS_UC_CI' )\n    COMMENT = 'Iceberg Federation Demo — Federated from external Iceberg catalog';\n\n-- Wait ~30 s for auto-discovery, then verify\nSHOW ICEBERG TABLES IN DATABASE DATABRICKS_DEMO_DB;\nSELECT * FROM DATABRICKS_DEMO_DB.horizon_demo.customer_orders LIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EApply Snowflake Horizon Governance\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE MASKING POLICY HORIZON_DEMO_SFDB.GOVERNANCE_POLICIES.MASK_CREDIT_CARD\n    AS (val STRING) RETURNS STRING ->\n    CASE\n        WHEN CURRENT_ROLE() = 'ACCOUNTADMIN' THEN val\n        ELSE CONCAT('****-****-****-', RIGHT(val, 4))\n    END;\n\nALTER ICEBERG TABLE DATABRICKS_DEMO_DB.horizon_demo.sensitive_orders\n    MODIFY COLUMN "credit_card"\n    SET MASKING POLICY HORIZON_DEMO_SFDB.GOVERNANCE_POLICIES.MASK_CREDIT_CARD;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESnowflake defines and enforces this policy independently — it applies whether the data was written by Snowflake, the external catalog, or any other engine.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EScenario 2 — Snowpark Connect Governance Demo\u003C/h2\u003E\n","\u003Cp\u003EUpload \u003Ccode\u003E05_databricks_federation_demo.ipynb\u003C/code\u003E to your Snowflake workspace:\n\u003Cstrong\u003ESnowflake Workspaces → drag and drop \u003Ccode\u003E05_databricks_federation_demo.ipynb\u003C/code\u003E into the file tree\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Ccode\u003Esnowpark-connect\u003C/code\u003E is pre-installed in Snowflake Workspaces — no package picker step needed.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ESession Setup for Catalog-Linked Databases\u003C/h3\u003E\n","\u003Cp\u003EThree setup rules are required when using Snowpark Connect with a catalog-linked database:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EInitialize Snowpark Connect on a regular Snowflake database\u003C/strong\u003E — set the session context before calling \u003Ccode\u003Einit_spark_session\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEnable \u003Ccode\u003EcaseSensitive\u003C/code\u003E mode\u003C/strong\u003E — external Iceberg catalogs use lowercase schema and table names; this ensures Snowpark Connect resolves them correctly.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUse fully-qualified lowercase names\u003C/strong\u003E in \u003Ccode\u003Espark.sql()\u003C/code\u003E for catalog-linked tables.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Rule 1: session context must be a regular (non-catalog-linked) database\nsf_session.sql(f"USE DATABASE {SF_INIT_DB}").collect()\n\n# Rule 2: preserve lowercase identifiers used by the external catalog\nconf = SparkConf().set("spark.sql.caseSensitive", "true")\nspark = snowpark_connect.init_spark_session(conf=conf)\n\n# Rule 3: fully-qualified reference with lowercase schema and table names\ndf = spark.sql(f"SELECT * FROM {SF_FEDERATED_DB}.{DBX_SCHEMA}.customer_orders")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EInitialize Sessions\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.snowpark.context import get_active_session\nfrom snowflake import snowpark_connect\nfrom pyspark import SparkConf\nfrom pyspark.sql.functions import col, count, sum as spark_sum\n\nsf_session = get_active_session()\nsf_session.sql(f"USE DATABASE {SF_INIT_DB}").collect()\n\nconf = SparkConf().set("spark.sql.caseSensitive", "true")\nspark = snowpark_connect.init_spark_session(conf=conf)\n\ndef switch_role(role):\n    sf_session.sql(f"USE ROLE {role}").collect()\n    print(f"Active role → {role}")\n\nswitch_role("ACCOUNTADMIN")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDemo 1 — Read customer_orders\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edf_orders = spark.sql(f"SELECT * FROM {TBL_ORDERS}")\ndf_orders.orderBy("order_id").show(truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDemo 2–3 — Filter and Aggregate\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edf_orders.filter(col("status").isin("SHIPPED", "DELIVERED")) \\\n    .select("order_id", "product", "amount", "status") \\\n    .orderBy("amount", ascending=False).show(truncate=False)\n\ndf_orders.groupBy("status") \\\n    .agg(count("*").alias("order_count"), spark_sum("amount").alias("total_revenue")) \\\n    .orderBy("total_revenue", ascending=False).show(truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDemo 4–5 — Role-Based Masking on Federated Tables\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# ACCOUNTADMIN: credit_card unmasked\nswitch_role("ACCOUNTADMIN")\nspark.sql(f"SELECT * FROM {TBL_SENSITIVE}") \\\n    .select("order_id", "customer_name", "credit_card").show(truncate=False)\n\n# Reader role: credit_card masked by Snowflake Horizon policy\nswitch_role(SF_READER_ROLE)\nspark.sql(f"SELECT * FROM {TBL_SENSITIVE}") \\\n    .select("order_id", "customer_name", "credit_card").show(truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDemo 6 — Side-by-Side Governance\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport pandas as pd\nCOLS = ["order_id", "customer_name", "credit_card"]\n\nswitch_role("ACCOUNTADMIN")\nadmin_rows  = spark.sql(f"SELECT * FROM {TBL_SENSITIVE}").select(*COLS).collect()\n\nswitch_role(SF_READER_ROLE)\nreader_rows = spark.sql(f"SELECT * FROM {TBL_SENSITIVE}").select(*COLS).collect()\n\nadmin_pd  = pd.DataFrame([r.asDict() for r in admin_rows])\nadmin_pd.insert(0, "role", "ACCOUNTADMIN")\nreader_pd = pd.DataFrame([r.asDict() for r in reader_rows])\nreader_pd.insert(0, "role", SF_READER_ROLE)\n\ncombined = pd.concat([admin_pd, reader_pd]).sort_values(["order_id","role"]).reset_index(drop=True)\nprint(combined.to_string(index=False))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected:\u003C/strong\u003E Same federated Parquet files — ACCOUNTADMIN sees \u003Ccode\u003E4111-1111-1111-1111\u003C/code\u003E, reader role sees \u003Ccode\u003E****-****-****-1111\u003C/code\u003E. Snowflake Horizon governance applied independently of the source catalog's policies.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EScenario 2 — AI Enrichment Pipeline Setup\u003C/h2\u003E\n","\u003Cp\u003EOpen \u003Ccode\u003E06_cortex_ai_pipeline.ipynb\u003C/code\u003E in a Snowflake Workspace notebook as \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EThis creates an AI-enriched Iceberg table from the Scenario 2 federated data, applies Horizon governance to the AI-generated column, and creates a Cortex Analyst semantic view.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 — Verify Cortex Is Available\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SNOWFLAKE.CORTEX.COMPLETE('llama3.1-8b', 'Reply with exactly one word: ready') AS cortex_status;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected:\u003C/strong\u003E \u003Ccode\u003EReady\u003C/code\u003E. If this errors, your trial region does not support Cortex LLM functions — switch to \u003Ccode\u003Eus-east-1\u003C/code\u003E or \u003Ccode\u003Eus-west-2\u003C/code\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETrial availability:\u003C/strong\u003E \u003Ccode\u003ESNOWFLAKE.CORTEX.COMPLETE\u003C/code\u003E and \u003Ccode\u003ESNOWFLAKE.CORTEX.CLASSIFY_TEXT\u003C/code\u003E are available in standard 30-day trial accounts on AWS us-east-1, us-west-2, and Azure eastus2. Cortex Analyst semantic views are available on the same regions.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2 — Create AI_ORDER_INSIGHTS Iceberg Table\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.AI_ORDER_INSIGHTS\n    CATALOG = 'SNOWFLAKE'\n    AS\nWITH deduped_orders AS (\n    SELECT order_id, customer_id, product, amount, order_date, status\n    FROM DATABRICKS_DEMO_DB.horizon_demo.customer_orders\n    QUALIFY ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY order_id) = 1\n),\ndeduped_sensitive AS (\n    SELECT order_id, region\n    FROM DATABRICKS_DEMO_DB.horizon_demo.sensitive_orders\n    QUALIFY ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY order_id) = 1\n)\nSELECT\n    co.order_id, co.customer_id, co.product, co.amount, co.order_date, co.status,\n    COALESCE(so.region, 'UNKNOWN') AS region,\n    -- Deterministic risk tier (reliable for governance demo)\n    CASE\n        WHEN co.status = 'CANCELLED' THEN 'HIGH'\n        WHEN co.amount >= 500        THEN 'HIGH'\n        WHEN co.amount >= 100        THEN 'MEDIUM'\n        ELSE 'LOW'\n    END AS risk_level,\n    -- Cortex-generated operational note\n    SNOWFLAKE.CORTEX.COMPLETE(\n        'llama3.1-8b',\n        'Write one operational sentence (max 15 words) for logistics. Product: ' || co.product ||\n        ', Amount: $' || co.amount::STRING || ', Status: ' || co.status || '.'\n    )::VARCHAR AS ops_note,\n    CURRENT_TIMESTAMP()::TIMESTAMP_LTZ(6) AS enriched_at\nFROM deduped_orders co\nLEFT JOIN deduped_sensitive so ON co.order_id = so.order_id;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy \u003Ccode\u003EQUALIFY ROW_NUMBER()\u003C/code\u003E:\u003C/strong\u003E The source federated tables may contain duplicate rows from multiple Databricks INSERT runs. \u003Ccode\u003EQUALIFY\u003C/code\u003E deduplicates per \u003Ccode\u003Eorder_id\u003C/code\u003E before enrichment.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy deterministic \u003Ccode\u003ECASE WHEN\u003C/code\u003E for \u003Ccode\u003Erisk_level\u003C/code\u003E:\u003C/strong\u003E LLMs produce inconsistent results for strict classification rules. Using \u003Ccode\u003ECASE WHEN\u003C/code\u003E ensures reliable HIGH/MEDIUM/LOW splits for the governance demo. \u003Ccode\u003ECORTEX.COMPLETE\u003C/code\u003E is used for \u003Ccode\u003Eops_note\u003C/code\u003E where natural language generation excels.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 3 — Re-Apply Masking Policy\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- CREATE OR REPLACE drops all column policies — must re-apply every time\nALTER ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.AI_ORDER_INSIGHTS\n    MODIFY COLUMN risk_level\n    SET MASKING POLICY HORIZON_DEMO_SFDB.DEMO_SCHEMA.MASK_RISK_LEVEL;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E \u003Ccode\u003ECREATE OR REPLACE ICEBERG TABLE\u003C/code\u003E drops all column policies on the table. Always run this \u003Ccode\u003EALTER ICEBERG TABLE\u003C/code\u003E immediately after the CTAS. The \u003Ccode\u003E06_cortex_ai_pipeline.ipynb\u003C/code\u003E script does this automatically.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 4 — Governance Comparison on AI Output\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- ACCOUNTADMIN: sees actual risk level including HIGH\nUSE ROLE ACCOUNTADMIN;\nSELECT 'ACCOUNTADMIN' AS role_context, order_id, product, amount, region, risk_level\nFROM HORIZON_DEMO_SFDB.DEMO_SCHEMA.AI_ORDER_INSIGHTS ORDER BY order_id;\n\n-- Reader role: HIGH masked to *** RESTRICTED ***\nUSE ROLE EXT_COMPUTE_ENG_DEMO_ROLE;\nSELECT 'EXT_COMPUTE_ENG_DEMO_ROLE' AS role_context, order_id, product, amount, region, risk_level\nFROM HORIZON_DEMO_SFDB.DEMO_SCHEMA.AI_ORDER_INSIGHTS ORDER BY order_id;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003Erole\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003Eorder_id\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003Eproduct\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003Erisk_level\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EACCOUNTADMIN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELaptop\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHIGH\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EACCOUNTADMIN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMonitor\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMEDIUM\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Ereader role\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELaptop\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E*** RESTRICTED ***\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Ereader role\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMonitor\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMEDIUM\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EStep 5 — Create Cortex Analyst Semantic View\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE SEMANTIC VIEW HORIZON_DEMO_SFDB.DEMO_SCHEMA.ICEBERG_AI_SEMANTIC_VIEW\nTABLES (\n  orders     AS HORIZON_DEMO_SFDB.DEMO_SCHEMA.AI_ORDER_INSIGHTS\n               PRIMARY KEY (order_id)\n               WITH SYNONYMS = ('ai orders', 'enriched orders', 'risk orders'),\n  fed_orders AS DATABRICKS_DEMO_DB.horizon_demo.customer_orders\n               PRIMARY KEY (order_id)\n               WITH SYNONYMS = ('federated orders', 'source orders')\n)\nRELATIONSHIPS (\n  orders(order_id) REFERENCES fed_orders\n)\nFACTS (\n  orders.amount AS amount WITH SYNONYMS = ('order value', 'revenue')\n)\nDIMENSIONS (\n  orders.product    AS product,\n  orders.status     AS status     WITH SYNONYMS = ('fulfillment status'),\n  orders.region     AS region,\n  orders.risk_level AS risk_level WITH SYNONYMS = ('risk', 'risk tier'),\n  orders.order_date AS order_date,\n  orders.ops_note   AS ops_note   WITH SYNONYMS = ('operational note', 'ai note')\n)\nMETRICS (\n  orders.total_revenue   AS SUM(orders.amount)     WITH SYNONYMS = ('revenue', 'total sales'),\n  orders.order_count     AS COUNT(orders.order_id) WITH SYNONYMS = ('number of orders'),\n  orders.avg_order_value AS AVG(orders.amount)      WITH SYNONYMS = ('average order', 'AOV')\n);\n\nGRANT SELECT ON SEMANTIC VIEW HORIZON_DEMO_SFDB.DEMO_SCHEMA.ICEBERG_AI_SEMANTIC_VIEW\n    TO ROLE EXT_COMPUTE_ENG_DEMO_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic views\u003C/strong\u003E are the recommended path for Cortex Analyst — they replace legacy YAML files and appear automatically in Snowsight → AI & ML → Cortex Analyst. Note: the alias direction in TABLES is \u003Ccode\u003Ealias AS db.schema.table\u003C/code\u003E (not \u003Ccode\u003Etable AS alias\u003C/code\u003E).\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EScenario 2 — Snowpark Connect AI Pipeline Notebook\u003C/h2\u003E\n","\u003Cp\u003EUpload \u003Ccode\u003E07_ai_pipeline.ipynb\u003C/code\u003E to your Snowflake workspace:\n\u003Cstrong\u003ESnowflake Workspaces → drag and drop \u003Ccode\u003E07_ai_pipeline.ipynb\u003C/code\u003E into the file tree\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Ccode\u003Esnowpark-connect\u003C/code\u003E is pre-installed in Snowflake Workspaces — no package picker step needed.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EInitialize Session\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esf_session = get_active_session()\nsf_session.sql(f"USE DATABASE {SF_INIT_DB}").collect()\n\nconf = SparkConf().set("spark.sql.caseSensitive", "true")\nspark = snowpark_connect.init_spark_session(conf=conf)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Ccode\u003EcaseSensitive=true\u003C/code\u003E is required when reading catalog-linked (FDN) tables, which use lowercase identifiers. It is harmless when reading SF-managed tables.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2 — Snowpark Connect Reads Federated Source Tables\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Read from CLD — federated Databricks Iceberg (read only)\ndf_orders = spark.sql(f"SELECT * FROM {TBL_ORDERS}")\ndf_orders.select("order_id", "product", "amount", "status").orderBy("order_id").show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4 — Cortex Enrichment + Write via sf_session\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esf_session.sql(f"""\n    CREATE OR REPLACE ICEBERG TABLE {TBL_INSIGHTS} CATALOG = 'SNOWFLAKE' AS\n    WITH deduped_orders AS ( ... ),\n         deduped_sensitive AS ( ... )\n    SELECT ..., SNOWFLAKE.CORTEX.COMPLETE('llama3.1-8b', ...) AS ops_note ...\n    FROM deduped_orders co LEFT JOIN deduped_sensitive so ON co.order_id = so.order_id\n""").collect()\n\n# Re-apply masking — CREATE OR REPLACE drops column policies\nsf_session.sql(f"""\n    ALTER ICEBERG TABLE {TBL_INSIGHTS}\n    MODIFY COLUMN risk_level\n    SET MASKING POLICY {SF_MANAGED_ICEBERG_DB}.{SF_DEMO_SCHEMA}.MASK_RISK_LEVEL\n""").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 5 — Governance Comparison via Snowpark Connect\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003ECOLS = ["order_id", "product", "amount", "region", "risk_level"]\n\nswitch_role("ACCOUNTADMIN")\nadmin_df = sf_session.sql(f"SELECT {','.join(COLS)} FROM {TBL_INSIGHTS}").to_pandas()\nadmin_df.columns = [c.lower() for c in admin_df.columns]\n\nswitch_role(SF_READER_ROLE)\nreader_df = sf_session.sql(f"SELECT {','.join(COLS)} FROM {TBL_INSIGHTS}").to_pandas()\nreader_df.columns = [c.lower() for c in reader_df.columns]\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Ccode\u003Esf_session.sql().to_pandas()\u003C/code\u003E returns \u003Cstrong\u003Euppercase\u003C/strong\u003E column names. Call \u003Ccode\u003E.columns = [c.lower() for c in df.columns]\u003C/code\u003E immediately after \u003Ccode\u003Eto_pandas()\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EScenario 2 — Cortex Analyst Demo\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOpen:\u003C/strong\u003E Snowsight → \u003Cstrong\u003EAI & ML\u003C/strong\u003E → \u003Cstrong\u003ECortex Analyst\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESelect the semantic view:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the Cortex Analyst list, click \u003Ccode\u003EICEBERG_AI_SEMANTIC_VIEW\u003C/code\u003E under \u003Ccode\u003EHORIZON_DEMO_SFDB.DEMO_SCHEMA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003EPlayground\u003C/strong\u003E tab at the top of the screen\u003C/li\u003E\u003Cli\u003EType prompts in the \u003Cstrong\u003Echat box at the bottom\u003C/strong\u003E of the Playground and press Enter\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EDemo Prompt Sequence\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt 1 — Revenue overview\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"What is the total revenue by risk level?"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EExpected: HIGH = highest amount (Laptop $999.99), MEDIUM, LOW.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt 2 — Governance live demo (key moment)\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Which orders are classified as HIGH risk and what are their operational notes?"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EAs ACCOUNTADMIN: Laptop, $999.99, \u003Ccode\u003Erisk_level = HIGH\u003C/code\u003E with Cortex ops note.\nSwitch role to \u003Ccode\u003EEXT_COMPUTE_ENG_DEMO_ROLE\u003C/code\u003E and ask again → \u003Ccode\u003Erisk_level = *** RESTRICTED ***\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt 3 — Business intelligence\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Show me all SHIPPED orders in the US-WEST region"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt 4 — Cross-table (uses the RELATIONSHIP)\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Compare total revenue from enriched orders versus source federated orders"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe semantic view joins \u003Ccode\u003EAI_ORDER_INSIGHTS\u003C/code\u003E (SF-managed) and \u003Ccode\u003Ecustomer_orders\u003C/code\u003E (Databricks-federated CLD) in a single query — Horizon masking applies throughout.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt 5 — Analytics\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"What is the average order value by fulfillment status?"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations — you have completed all three scenarios!\u003C/p\u003E\n","\u003Ch3\u003EWhat You Built\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E✅ \u003Cstrong\u003ESnowflake-managed Iceberg tables\u003C/strong\u003E with Horizon column masking and row access policies\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003ESnowpark Connect governance demo\u003C/strong\u003E showing Horizon policies enforced through PySpark on Snowflake's engine\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EExternal engine read/write demo\u003C/strong\u003E via Horizon IRC with credential-vending write control\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003ECatalog-linked database\u003C/strong\u003E auto-federating externally-managed Iceberg tables\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003ESnowpark Connect notebook\u003C/strong\u003E with live role-based masking on federated tables\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003ECortex Analyst semantic view\u003C/strong\u003E spanning SF-managed and federated Iceberg tables with Horizon masking enforced\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow Snowflake manages Iceberg natively with Horizon governance enforced through Snowpark Connect at every query\u003C/li\u003E\u003Cli\u003EHow Horizon IRC exposes Snowflake-managed tables to any Iceberg-compatible engine, and how credential vending enforces write protection at the S3 layer\u003C/li\u003E\u003Cli\u003EHow catalog-linked databases auto-federate externally-managed Iceberg tables — and how Snowflake applies independent governance regardless of data origin\u003C/li\u003E\u003Cli\u003EHow \u003Ccode\u003ESNOWFLAKE.CORTEX.COMPLETE\u003C/code\u003E enriches federated Iceberg data inline in SQL, with Horizon masking applying to AI-generated columns exactly as it does to raw data\u003C/li\u003E\u003Cli\u003EHow Cortex Analyst semantic views enable natural language querying across SF-managed and federated Iceberg tables with role-based masking enforced\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EGovernance Summary\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake-Managed Iceberg Tables\u003C/strong\u003E — Horizon policies enforced at the Snowflake SQL layer for every access path including Snowpark Connect and Cortex Analyst.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAccess path\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETable\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPolicy\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EGovernance result\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpark Connect (\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EPROTECTED_TABLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EColumn masking + row access\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3 rows, \u003Ccode\u003Esensitive_data\u003C/code\u003E raw\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpark Connect (reader role)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EPROTECTED_TABLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EColumn masking + row access\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2 rows filtered, \u003Ccode\u003E*** MASKED ***\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpark Connect (\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EAI_ORDER_INSIGHTS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EColumn masking on AI column\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erisk_level = HIGH\u003C/code\u003E visible\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpark Connect (reader role)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EAI_ORDER_INSIGHTS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EColumn masking on AI column\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erisk_level = *** RESTRICTED ***\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Analyst (\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EICEBERG_AI_SEMANTIC_VIEW\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESemantic view + masking\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENL query → \u003Ccode\u003EHIGH\u003C/code\u003E risk visible\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Analyst (reader role)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EICEBERG_AI_SEMANTIC_VIEW\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESemantic view + masking\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENL query → \u003Ccode\u003E*** RESTRICTED ***\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExternal engine via Horizon IRC\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EPROTECTED_TABLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E❌ Bypassed (raw Parquet path)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3 rows, \u003Ccode\u003Esensitive_data\u003C/code\u003E raw\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExternal engine write to \u003Ccode\u003EOPEN_TABLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EOPEN_TABLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECredential vending\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E✅ Write-capable S3 creds vended\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExternal engine write to \u003Ccode\u003EPROTECTED_TABLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EPROTECTED_TABLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECredential vending\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E❌ S3 403 — read-only creds vended\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExternally-Managed Iceberg Tables (Catalog-Linked Database)\u003C/strong\u003E — Snowflake applies its own independent Horizon policies at query time, regardless of the source catalog's governance.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAccess path\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETable\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPolicy\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EGovernance result\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpark Connect (\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esensitive_orders\u003C/code\u003E (CLD)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake masking — independent of Databricks UC\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReal credit card numbers\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpark Connect (reader role)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esensitive_orders\u003C/code\u003E (CLD)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake masking — independent of Databricks UC\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E****-****-****-XXXX\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpark Connect (\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecustomer_orders\u003C/code\u003E (CLD)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo policy applied\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAll rows, all columns\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpark Connect (reader role)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecustomer_orders\u003C/code\u003E (CLD)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo policy applied\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAll rows, all columns\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey insight:\u003C/strong\u003E Snowflake's Horizon governance and Databricks UC governance are completely independent. Snowflake enforces its own masking policies on federated tables at query time — the source catalog's policies do not propagate, and Snowflake's policies do not reach into the source catalog.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-overview\"\u003ESnowpark Connect for Apache Spark Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-snowflake\"\u003ESnowflake Managed Iceberg Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database\"\u003ECatalog-Linked Databases\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-integration-open-api\"\u003EHorizon IRC (Iceberg REST Catalog)\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions\"\u003ESnowflake Cortex LLM Functions\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic\"\u003ECortex Analyst Semantic Views\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-snowpark-connect-for-apache-spark/\"\u003EIntro to Snowpark Connect\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-iceberg-tables/\"\u003EGetting Started with Iceberg Tables in Snowflake\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Federate and Govern Iceberg Tables Using Snowpark Connect for Apache Spark",":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\n## Overview\n\n**By the end of this quickstart, a business user opens Snowsight → Cortex Analyst, types _\"Which orders are HIGH risk and need immediate attention?\"_, and gets a governed, AI-enriched answer — drawn in real time from Iceberg tables that live across two completely separate catalogs.**\n\nBehind that single query:\n- Order data originates in **Databricks Unity Catalog** (Delta + Iceberg UniForm tables on Databricks-managed S3)\n- Snowflake federates those tables into a **Catalog-Linked Database** — no data movement, no copy jobs\n- **Snowpark Connect** reads the federated tables and calls **Snowflake Cortex** to classify risk and generate operational notes inline in SQL\n- Results are written to a new **Snowflake-managed Iceberg table** on Snowflake's own storage\n- **Horizon governance** — column masking and row access policies — applies to every table, including the AI-generated `risk_level` column\n- When a non-admin user asks the same question, Cortex Analyst returns `*** RESTRICTED ***` for HIGH risk orders — governance fires through conversational AI just as it does through SQL\n\nThis is federated Iceberg analytics with AI enrichment and end-to-end governance, all without rewriting pipelines or duplicating data.\n\n---\n\n### The Problem\n\nData teams increasingly run Iceberg tables in multiple catalogs — Snowflake, Databricks, AWS Glue. Getting a unified, governed, AI-powered analytics layer across all of them requires stitching together bespoke ETL, cross-catalog security policies, and custom ML pipelines.\n\nThis quickstart shows how to replace that with a single, open-standards approach:\n\n\u003E **Iceberg everywhere + Snowpark Connect as the unified compute layer + Horizon for governance + Cortex for AI**\n\n---\n\n### What You Build\n\n**Step 1 — Snowflake-managed Iceberg + Horizon governance** (`01`, `02`)\nCreate two Snowflake-managed Iceberg tables. Apply column masking (hide `sensitive_data` from non-admin roles) and a row access policy (filter historical rows). Use Snowpark Connect to run PySpark DataFrames against them — governance fires on every query because Snowpark Connect routes through Snowflake's SQL engine.\n\n**Step 2 — Federate external Iceberg tables + Snowpark Connect governance** (`04`, `05`)\nCreate Iceberg tables in Databricks Unity Catalog (Delta + UniForm). Federate them into Snowflake as a Catalog-Linked Database. Apply Snowflake's own Horizon masking policy to `credit_card` — independent of any Databricks policies. Snowpark Connect queries the federated tables with live role-based masking.\n\n**Step 3 — Cortex AI enrichment pipeline → Cortex Analyst** (`06`, `07`)\nSnowpark Connect reads from both catalogs. Cortex classifies each order as HIGH / MEDIUM / LOW risk and generates a one-sentence operational note. Results are written to a new Snowflake-managed Iceberg table. Horizon masking applies to `risk_level` exactly as it does to raw columns. A Cortex Analyst semantic view spans all three Iceberg tables — ask questions in natural language from Snowsight, with governance enforced per active role.\n\n\u003E **Download the code:**\n\u003E [Download all files (ZIP)](https://download-directory.github.io/?url=https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets)\n\u003E\n\u003E Or download individual files:\n\u003E - [01_sf_iceberg_catalog_setup.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/01_sf_iceberg_catalog_setup.sql)\n\u003E - [02_sf_iceberg_demo.ipynb](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/02_sf_iceberg_demo.ipynb)\n\u003E - [03_databricks_rw_sf_iceberg.py](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/03_databricks_rw_sf_iceberg.py)\n\u003E - [04_databricks_create_uc_tables.py](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/04_databricks_create_uc_tables.py)\n\u003E - [05_databricks_federation_demo.ipynb](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/05_databricks_federation_demo.ipynb)\n\u003E - [06_cortex_ai_pipeline.ipynb](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/06_cortex_ai_pipeline.ipynb)\n\u003E - [07_ai_pipeline.ipynb](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/federate-and-govern-iceberg-tables-using-snowpark-connect-for-apache-spark/assets/07_ai_pipeline.ipynb)\n\n### What You'll Learn\n\n- How **Snowpark Connect** runs PySpark DataFrames through Snowflake's SQL engine — enforcing Horizon governance on every query regardless of the caller\n- How **Catalog-Linked Databases** federate externally-managed Iceberg tables into Snowflake with independent Horizon governance applied at query time\n- How **Cortex LLM functions** enrich federated Iceberg data inline in SQL, writing AI results to Snowflake-managed Iceberg\n- Why **Horizon masking applies to AI-generated columns** exactly as it does to raw data — no extra configuration\n- How **Cortex Analyst** queries a semantic view spanning multiple Iceberg catalogs with governance enforced per role\n\n### Key Capabilities\n\n- **Federated Iceberg**: Query Iceberg tables across Snowflake and external catalogs from a single Snowflake Workspace notebook\n- **Governance at the SQL layer**: Snowpark Connect routes every query through Snowflake's SQL engine — Horizon policies apply regardless of the caller's role\n- **Independent governance on federated tables**: Snowflake applies its own masking policies on top of externally-managed tables, independently of the source catalog's policies\n- **AI enrichment on Iceberg**: Cortex LLM functions run inline in SQL on federated data, writing results to Snowflake-managed Iceberg\n- **Governed AI output**: Horizon masking applies to Cortex-generated columns exactly as it does to raw data\n- **Conversational analytics**: Cortex Analyst semantic view spans SF-managed and federated Iceberg tables — natural language queries with governance enforced per active role\n\n### What You'll Need\n\n- A [Snowflake account](https://signup.snowflake.com/) with `ACCOUNTADMIN` access and Cortex LLM functions enabled (us-east-1 or us-west-2)\n- A Databricks workspace with Unity Catalog enabled (for Step 2)\n- An S3 bucket accessible from Snowflake via an external volume (`CREATE EXTERNAL VOLUME`)\n\n### Prerequisites\n\n- Familiarity with Snowflake SQL and the Snowsight UI\n- Basic familiarity with PySpark DataFrames\n- An external volume already configured in Snowflake (`CREATE EXTERNAL VOLUME`)\n\n\u003C!-- ------------------------ --\u003E\n\n## Architecture\n\n### Scenario 1 — Snowflake-Managed Iceberg + Snowpark Connect\n\n```\nSnowflake Account\n  │  CREATE ICEBERG TABLE ... CATALOG = 'SNOWFLAKE'\n  │  Horizon governance: column masking, row access policy\n  │\n  └─ Snowpark Connect\n       spark.table(\"DEMO_SCHEMA.OPEN_TABLE\")\n       spark.table(\"DEMO_SCHEMA.PROTECTED_TABLE\")\n       → queries route through Snowflake SQL engine\n       → Horizon policies ARE enforced\n```\n\n### Scenario 2 — Federate External Iceberg + Cortex AI Enrichment\n\n```\nExternal Catalog (Unity Catalog)\n  │  Delta + Iceberg UniForm tables\n  │  Iceberg REST endpoint → Snowflake Catalog Integration\n  │\nSnowflake Catalog-Linked Database (DATABRICKS_DEMO_DB)\n  │  Auto-discovers schemas + tables every 30 s\n  │  Applies independent Horizon masking policies\n  │\n  └─ Snowpark Connect\n       spark.sql(\"SELECT * FROM DATABRICKS_DEMO_DB.horizon_demo.customer_orders\")\n       sf_session.sql(CTAS + CORTEX.COMPLETE → AI_ORDER_INSIGHTS)\n       → Snowflake SQL engine enforces masking on both raw and AI columns\n       → Cortex Analyst: natural language queries on AI-enriched Iceberg table\n```\n\n\u003E **Governance contrast (external engine vs Snowpark Connect):** An external Spark engine reading via Horizon IRC receives raw Parquet via vended S3 credentials — Snowflake SQL-layer policies do not apply to that path. Snowpark Connect routes through the Snowflake SQL engine, so Horizon masking and row access policies are always enforced. See `03_databricks_rw_sf_iceberg.py` for the full Databricks IRC demo.\n\n\u003C!-- ------------------------ --\u003E\n\n## Databricks Cluster Setup (Scenario 2 prerequisites)\n\nScenarios 1 and 2 Snowflake notebooks run entirely within Snowflake — no external cluster needed.\n\nScenario 2 requires two Databricks notebooks run once as setup:\n\n| Notebook | Cluster needed |\n|----------|---------------|\n| `04_databricks_create_uc_tables.py` | Single User + Unity Catalog, DBR 13.3 LTS+ |\n| `03_databricks_rw_sf_iceberg.py` *(optional governance contrast)* | Single User, no UC, DBR 14.3 LTS+, Maven: `org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.9.1` |\n\n\u003C!-- ------------------------ --\u003E\n\n## Scenario 1 — Snowflake Setup\n\nRun `01_sf_iceberg_catalog_setup.sql` in a Snowflake worksheet as `ACCOUNTADMIN`.\n\nThis creates two Snowflake-managed Iceberg tables and applies Horizon governance policies.\n\n### Create Database and Tables\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nUSE WAREHOUSE LOAD_WH;\n\nCREATE DATABASE IF NOT EXISTS HORIZON_DEMO_SFDB\n    EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED'\n    COMMENT = 'Snowflake-managed Iceberg catalog (Horizon IRC)';\n\nCREATE SCHEMA IF NOT EXISTS HORIZON_DEMO_SFDB.DEMO_SCHEMA;\n\n-- OPEN_TABLE: no governance restrictions\nCREATE OR REPLACE ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.OPEN_TABLE (\n    id          INT,\n    product     STRING,\n    quantity    INT,\n    price       DECIMAL(10, 2),\n    created_at  TIMESTAMP\n)\nCATALOG = 'SNOWFLAKE';\n\n-- PROTECTED_TABLE: column masking + row access policy applied below\nCREATE OR REPLACE ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.PROTECTED_TABLE (\n    id              INT,\n    customer_name   STRING,\n    sensitive_data  STRING,\n    amount          DECIMAL(10, 2),\n    created_at      TIMESTAMP\n)\nCATALOG = 'SNOWFLAKE';\n```\n\n### Apply Horizon Governance Policies\n\n```sql\n-- Column masking: hide sensitive_data from non-admin roles\nCREATE OR REPLACE MASKING POLICY HORIZON_DEMO_SFDB.DEMO_SCHEMA.MASK_SENSITIVE\n    AS (val STRING) RETURNS STRING -\u003E\n    CASE\n        WHEN CURRENT_ROLE() IN ('ACCOUNTADMIN', 'SYSADMIN') THEN val\n        ELSE '*** MASKED ***'\n    END;\n\nALTER ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.PROTECTED_TABLE\n    MODIFY COLUMN sensitive_data\n    SET MASKING POLICY HORIZON_DEMO_SFDB.DEMO_SCHEMA.MASK_SENSITIVE;\n\n-- Row access policy: non-admin roles see current-year rows only\nCREATE OR REPLACE ROW ACCESS POLICY HORIZON_DEMO_SFDB.DEMO_SCHEMA.CURRENT_YEAR_ONLY\n    AS (created_at TIMESTAMP) RETURNS BOOLEAN -\u003E\n        CURRENT_ROLE() IN ('ACCOUNTADMIN', 'SYSADMIN')\n        OR YEAR(created_at) = YEAR(CURRENT_DATE());\n\nALTER ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.PROTECTED_TABLE\n    ADD ROW ACCESS POLICY HORIZON_DEMO_SFDB.DEMO_SCHEMA.CURRENT_YEAR_ONLY\n    ON (created_at);\n```\n\n### Configure Credential Vending (for Scenario 2)\n\n```sql\n-- OPEN_TABLE_RW: SELECT + writes → Snowflake vends write-capable S3 credentials\nCREATE DATABASE ROLE IF NOT EXISTS HORIZON_DEMO_SFDB.OPEN_TABLE_RW;\nGRANT SELECT, INSERT, UPDATE, DELETE ON TABLE\n    HORIZON_DEMO_SFDB.DEMO_SCHEMA.OPEN_TABLE\n    TO DATABASE ROLE HORIZON_DEMO_SFDB.OPEN_TABLE_RW;\n\n-- PROTECTED_TABLE_RO: SELECT only → Snowflake vends read-only S3 credentials\nCREATE DATABASE ROLE IF NOT EXISTS HORIZON_DEMO_SFDB.PROTECTED_TABLE_RO;\nGRANT SELECT ON TABLE\n    HORIZON_DEMO_SFDB.DEMO_SCHEMA.PROTECTED_TABLE\n    TO DATABASE ROLE HORIZON_DEMO_SFDB.PROTECTED_TABLE_RO;\n\nGRANT DATABASE ROLE HORIZON_DEMO_SFDB.OPEN_TABLE_RW      TO ROLE $SF_EXT_COMPUTE_ROLE;\nGRANT DATABASE ROLE HORIZON_DEMO_SFDB.PROTECTED_TABLE_RO TO ROLE $SF_EXT_COMPUTE_ROLE;\n```\n\n### Generate PAT (for Scenario 2)\n\n```sql\n-- ⚠ Copy the token immediately — shown only once\nALTER USER \u003CYOUR_USERNAME\u003E\n    ADD PROGRAMMATIC ACCESS TOKEN MY_DEMO_PAT\n    COMMENT = 'Iceberg Federation Demo — Horizon IRC integration';\n```\n\n\u003C!-- ------------------------ --\u003E\n\n## Scenario 1 — Snowpark Connect Governance Demo\n\nUpload `02_sf_iceberg_demo.ipynb` to your Snowflake workspace:\n**Snowflake Workspaces → drag and drop `02_sf_iceberg_demo.ipynb` into the file tree**\n\n\u003E `snowpark-connect` is pre-installed in Snowflake Workspaces — no package picker step needed.\n\n### Initialize Session\n\n```python\nfrom snowflake.snowpark.context import get_active_session\nfrom snowflake import snowpark_connect\nfrom pyspark.sql.functions import col\nimport pandas as pd\n\nsf_session = get_active_session()\nsf_session.sql(f\"USE DATABASE {SF_MANAGED_ICEBERG_DB}\").collect()\n\nspark = snowpark_connect.init_spark_session()\n\ndef switch_role(role):\n    sf_session.sql(f\"USE ROLE {role}\").collect()\n    print(f\"Active role → {role}\")\n\nswitch_role(\"ACCOUNTADMIN\")\n```\n\n### Demo 1 — Read OPEN_TABLE\n\n```python\nspark.table(TBL_OPEN).orderBy(\"id\").show(truncate=False)\n```\n\n**Expected:** 3 rows (Laptop, Mouse, Keyboard). All columns visible. No policies applied.\n\n### Demo 2 — ACCOUNTADMIN Reads PROTECTED_TABLE\n\n```python\nswitch_role(\"ACCOUNTADMIN\")\nspark.table(TBL_PROTECTED).orderBy(\"id\").show(truncate=False)\n```\n\n**Expected:** 3 rows including the 2023 row. `sensitive_data` shows raw values (e.g. `SSN-123-45-6789`).\n\n### Demo 3 — Reader Role Reads PROTECTED_TABLE\n\n```python\nswitch_role(SF_READER_ROLE)\nspark.table(TBL_PROTECTED).orderBy(\"id\").show(truncate=False)\n```\n\n**Expected:** 2 rows (2023 row filtered by row access policy). `sensitive_data` shows `*** MASKED ***`.\n\n### Demo 4 — Side-by-Side Comparison\n\n```python\nCOLS = [\"id\", \"customer_name\", \"sensitive_data\", \"amount\"]\n\nswitch_role(\"ACCOUNTADMIN\")\nadmin_rows = spark.table(TBL_PROTECTED).select(*COLS).collect()\n\nswitch_role(SF_READER_ROLE)\nreader_rows = spark.table(TBL_PROTECTED).select(*COLS).collect()\n\nadmin_pd  = pd.DataFrame([r.asDict() for r in admin_rows])\nadmin_pd.insert(0, \"role\", \"ACCOUNTADMIN\")\n\nreader_pd = pd.DataFrame([r.asDict() for r in reader_rows])\nreader_pd.insert(0, \"role\", SF_READER_ROLE)\n\ncombined = pd.concat([admin_pd, reader_pd]).sort_values([\"id\", \"role\"]).reset_index(drop=True)\nprint(combined.to_string(index=False))\n```\n\n**Expected:** Same Iceberg table, same Parquet files — ACCOUNTADMIN sees 3 unmasked rows, reader role sees 2 masked rows. This is Snowflake Horizon governance enforced through Snowpark Connect.\n\n\u003E **Key insight:** Snowpark Connect routes queries through the Snowflake SQL engine. Horizon policies apply to every query regardless of the PySpark API used. Scenario 2 demonstrates the contrast — an external engine reading the same table bypasses these policies.\n\n\u003C!-- ------------------------ --\u003E\n\n## Governance Contrast — Snowpark Connect vs External Engine\n\nThe same Snowflake-managed Iceberg table returns different results depending on the read path.\n\n| Access path | Engine | Route | Horizon policies |\n|-------------|--------|-------|-----------------|\n| Snowpark Connect (Scenarios 1 & 2) | Snowflake SQL engine | Logical query plan | ✅ Enforced |\n| Horizon IRC (external Spark) | Databricks / any Spark | Raw Parquet via vended S3 creds | ❌ Bypassed |\n\n**Snowpark Connect — PROTECTED_TABLE (masking + row filter enforced):**\n\n```python\nswitch_role(\"EXT_COMPUTE_ENG_DEMO_ROLE\")\nspark.table(\"HORIZON_DEMO_SFDB.DEMO_SCHEMA.PROTECTED_TABLE\").show()\n# 2 rows (2023 row filtered) │ sensitive_data: *** MASKED ***\n```\n\n**External Spark via Horizon IRC — same table (raw Parquet):**\n\n```python\n# Databricks reads via vended S3 credentials\nspark.table(\"sf_horizon.DEMO_SCHEMA.PROTECTED_TABLE\").show()\n# 3 rows (all rows) │ sensitive_data: raw value visible\n```\n\nSnowflake's masking and row access policies execute inside the SQL engine. An engine that bypasses the SQL engine and reads Parquet files directly will never see those policies fire.\n\n\u003E **Credential vending also enforces write access:** `PROTECTED_TABLE_RO` (SELECT only) → Snowflake vends read-only S3 credentials → any `s3:PutObject` attempt returns 403. `OPEN_TABLE_RW` (INSERT/UPDATE/DELETE) → write-capable credentials vended. No application code required — the catalog decides.\n\n**Full Databricks IRC notebook:** `03_databricks_rw_sf_iceberg.py` (reads, writes, credential vending demo).\n\n\u003C!-- ------------------------ --\u003E\n\n## Scenario 2 — External Catalog Setup\n\nAttach `04_databricks_create_uc_tables.py` to **Cluster B** (Unity Catalog enabled).\n\n### Create Catalog, Schema, and Tables\n\n```python\nCATALOG_NAME = \"\u003CDBX_UC_CATALOG\u003E\"    # e.g. my_demo\nSCHEMA_NAME  = \"horizon_demo\"         # pre-set\n\nspark.sql(f\"CREATE CATALOG IF NOT EXISTS {CATALOG_NAME}\")\nspark.sql(f\"CREATE SCHEMA  IF NOT EXISTS {CATALOG_NAME}.{SCHEMA_NAME}\")\n```\n\n```python\nspark.sql(f\"\"\"\nCREATE OR REPLACE TABLE {CATALOG_NAME}.{SCHEMA_NAME}.customer_orders (\n    order_id BIGINT, customer_id BIGINT, product STRING,\n    amount DECIMAL(10,2), order_date DATE, status STRING\n)\nUSING DELTA\nTBLPROPERTIES (\n    'delta.universalFormat.enabledFormats' = 'iceberg',\n    'delta.enableIcebergCompatV2'          = 'true',\n    'delta.columnMapping.mode'             = 'name'\n)\n\"\"\")\n```\n\nDelta UniForm writes standard Delta files **and** Iceberg metadata in parallel — no data duplication, no ETL.\n\n### Apply External Catalog Governance\n\n```python\n# Column mask on credit_card (applies within the external catalog only)\nspark.sql(f\"\"\"\nCREATE OR REPLACE FUNCTION {CATALOG_NAME}.{SCHEMA_NAME}.mask_credit_card(cc STRING)\nRETURN CASE WHEN is_account_group_member('account_unity_admin') THEN cc\n            ELSE CONCAT('****-****-****-', RIGHT(cc, 4)) END\n\"\"\")\nspark.sql(f\"\"\"ALTER TABLE {CATALOG_NAME}.{SCHEMA_NAME}.sensitive_orders\nALTER COLUMN credit_card SET MASK {CATALOG_NAME}.{SCHEMA_NAME}.mask_credit_card\"\"\")\n```\n\n\u003E These external catalog policies apply within that catalog only. Snowflake applies its own independent governance policies in the next step.\n\n### Get the Iceberg REST Endpoint\n\n```python\nWORKSPACE_HOST = \"\u003CDBX_WORKSPACE_HOST\u003E\"\nprint(f\"CATALOG_URI  : https://{WORKSPACE_HOST}/api/2.1/unity-catalog/iceberg-rest\")\nprint(f\"CATALOG_NAME : {CATALOG_NAME}\")\n```\n\nCopy these values into `05_databricks_federation_demo.ipynb`.\n\n\u003C!-- ------------------------ --\u003E\n\n## Scenario 2 — Snowflake Setup\n\nOpen `05_databricks_federation_demo.ipynb` in a Snowflake Workspace notebook (drag and drop into the file tree).\n\n### Create Catalog Integration\n\n```sql\nCREATE OR REPLACE CATALOG INTEGRATION MY_DATABRICKS_UC_CI\n    CATALOG_SOURCE = ICEBERG_REST\n    TABLE_FORMAT   = ICEBERG\n    REST_CONFIG = (\n        CATALOG_URI  = 'https://\u003CDBX_WORKSPACE_HOST\u003E/api/2.1/unity-catalog/iceberg-rest'\n        CATALOG_NAME = '\u003CDBX_UC_CATALOG\u003E'\n        ACCESS_DELEGATION_MODE = EXTERNAL_VOLUME_CREDENTIALS\n    )\n    REST_AUTHENTICATION = (\n        TYPE         = BEARER\n        BEARER_TOKEN = '\u003CDBX_PAT_TOKEN\u003E'\n    )\n    ENABLED = TRUE;\n\n-- Verify connectivity — must return {\"success\": true}\nSELECT SYSTEM$VERIFY_CATALOG_INTEGRATION('MY_DATABRICKS_UC_CI');\n```\n\n### Create Catalog-Linked Database\n\n```sql\nCREATE DATABASE IF NOT EXISTS DATABRICKS_DEMO_DB\n    EXTERNAL_VOLUME = 'ICEBERG_EXTERNAL_S3_VOLUME'\n    LINKED_CATALOG = ( CATALOG = 'MY_DATABRICKS_UC_CI' )\n    COMMENT = 'Iceberg Federation Demo — Federated from external Iceberg catalog';\n\n-- Wait ~30 s for auto-discovery, then verify\nSHOW ICEBERG TABLES IN DATABASE DATABRICKS_DEMO_DB;\nSELECT * FROM DATABRICKS_DEMO_DB.horizon_demo.customer_orders LIMIT 5;\n```\n\n### Apply Snowflake Horizon Governance\n\n```sql\nCREATE OR REPLACE MASKING POLICY HORIZON_DEMO_SFDB.GOVERNANCE_POLICIES.MASK_CREDIT_CARD\n    AS (val STRING) RETURNS STRING -\u003E\n    CASE\n        WHEN CURRENT_ROLE() = 'ACCOUNTADMIN' THEN val\n        ELSE CONCAT('****-****-****-', RIGHT(val, 4))\n    END;\n\nALTER ICEBERG TABLE DATABRICKS_DEMO_DB.horizon_demo.sensitive_orders\n    MODIFY COLUMN \"credit_card\"\n    SET MASKING POLICY HORIZON_DEMO_SFDB.GOVERNANCE_POLICIES.MASK_CREDIT_CARD;\n```\n\nSnowflake defines and enforces this policy independently — it applies whether the data was written by Snowflake, the external catalog, or any other engine.\n\n\u003C!-- ------------------------ --\u003E\n\n## Scenario 2 — Snowpark Connect Governance Demo\n\nUpload `05_databricks_federation_demo.ipynb` to your Snowflake workspace:\n**Snowflake Workspaces → drag and drop `05_databricks_federation_demo.ipynb` into the file tree**\n\n\u003E `snowpark-connect` is pre-installed in Snowflake Workspaces — no package picker step needed.\n\n### Session Setup for Catalog-Linked Databases\n\nThree setup rules are required when using Snowpark Connect with a catalog-linked database:\n\n1. **Initialize Snowpark Connect on a regular Snowflake database** — set the session context before calling `init_spark_session`.\n2. **Enable `caseSensitive` mode** — external Iceberg catalogs use lowercase schema and table names; this ensures Snowpark Connect resolves them correctly.\n3. **Use fully-qualified lowercase names** in `spark.sql()` for catalog-linked tables.\n\n```python\n# Rule 1: session context must be a regular (non-catalog-linked) database\nsf_session.sql(f\"USE DATABASE {SF_INIT_DB}\").collect()\n\n# Rule 2: preserve lowercase identifiers used by the external catalog\nconf = SparkConf().set(\"spark.sql.caseSensitive\", \"true\")\nspark = snowpark_connect.init_spark_session(conf=conf)\n\n# Rule 3: fully-qualified reference with lowercase schema and table names\ndf = spark.sql(f\"SELECT * FROM {SF_FEDERATED_DB}.{DBX_SCHEMA}.customer_orders\")\n```\n\n### Initialize Sessions\n\n```python\nfrom snowflake.snowpark.context import get_active_session\nfrom snowflake import snowpark_connect\nfrom pyspark import SparkConf\nfrom pyspark.sql.functions import col, count, sum as spark_sum\n\nsf_session = get_active_session()\nsf_session.sql(f\"USE DATABASE {SF_INIT_DB}\").collect()\n\nconf = SparkConf().set(\"spark.sql.caseSensitive\", \"true\")\nspark = snowpark_connect.init_spark_session(conf=conf)\n\ndef switch_role(role):\n    sf_session.sql(f\"USE ROLE {role}\").collect()\n    print(f\"Active role → {role}\")\n\nswitch_role(\"ACCOUNTADMIN\")\n```\n\n### Demo 1 — Read customer_orders\n\n```python\ndf_orders = spark.sql(f\"SELECT * FROM {TBL_ORDERS}\")\ndf_orders.orderBy(\"order_id\").show(truncate=False)\n```\n\n### Demo 2–3 — Filter and Aggregate\n\n```python\ndf_orders.filter(col(\"status\").isin(\"SHIPPED\", \"DELIVERED\")) \\\n    .select(\"order_id\", \"product\", \"amount\", \"status\") \\\n    .orderBy(\"amount\", ascending=False).show(truncate=False)\n\ndf_orders.groupBy(\"status\") \\\n    .agg(count(\"*\").alias(\"order_count\"), spark_sum(\"amount\").alias(\"total_revenue\")) \\\n    .orderBy(\"total_revenue\", ascending=False).show(truncate=False)\n```\n\n### Demo 4–5 — Role-Based Masking on Federated Tables\n\n```python\n# ACCOUNTADMIN: credit_card unmasked\nswitch_role(\"ACCOUNTADMIN\")\nspark.sql(f\"SELECT * FROM {TBL_SENSITIVE}\") \\\n    .select(\"order_id\", \"customer_name\", \"credit_card\").show(truncate=False)\n\n# Reader role: credit_card masked by Snowflake Horizon policy\nswitch_role(SF_READER_ROLE)\nspark.sql(f\"SELECT * FROM {TBL_SENSITIVE}\") \\\n    .select(\"order_id\", \"customer_name\", \"credit_card\").show(truncate=False)\n```\n\n### Demo 6 — Side-by-Side Governance\n\n```python\nimport pandas as pd\nCOLS = [\"order_id\", \"customer_name\", \"credit_card\"]\n\nswitch_role(\"ACCOUNTADMIN\")\nadmin_rows  = spark.sql(f\"SELECT * FROM {TBL_SENSITIVE}\").select(*COLS).collect()\n\nswitch_role(SF_READER_ROLE)\nreader_rows = spark.sql(f\"SELECT * FROM {TBL_SENSITIVE}\").select(*COLS).collect()\n\nadmin_pd  = pd.DataFrame([r.asDict() for r in admin_rows])\nadmin_pd.insert(0, \"role\", \"ACCOUNTADMIN\")\nreader_pd = pd.DataFrame([r.asDict() for r in reader_rows])\nreader_pd.insert(0, \"role\", SF_READER_ROLE)\n\ncombined = pd.concat([admin_pd, reader_pd]).sort_values([\"order_id\",\"role\"]).reset_index(drop=True)\nprint(combined.to_string(index=False))\n```\n\n**Expected:** Same federated Parquet files — ACCOUNTADMIN sees `4111-1111-1111-1111`, reader role sees `****-****-****-1111`. Snowflake Horizon governance applied independently of the source catalog's policies.\n\n\u003C!-- ------------------------ --\u003E\n\n## Scenario 2 — AI Enrichment Pipeline Setup\n\nOpen `06_cortex_ai_pipeline.ipynb` in a Snowflake Workspace notebook as `ACCOUNTADMIN`.\n\nThis creates an AI-enriched Iceberg table from the Scenario 2 federated data, applies Horizon governance to the AI-generated column, and creates a Cortex Analyst semantic view.\n\n### Step 1 — Verify Cortex Is Available\n\n```sql\nSELECT SNOWFLAKE.CORTEX.COMPLETE('llama3.1-8b', 'Reply with exactly one word: ready') AS cortex_status;\n```\n\n**Expected:** `Ready`. If this errors, your trial region does not support Cortex LLM functions — switch to `us-east-1` or `us-west-2`.\n\n\u003E **Trial availability:** `SNOWFLAKE.CORTEX.COMPLETE` and `SNOWFLAKE.CORTEX.CLASSIFY_TEXT` are available in standard 30-day trial accounts on AWS us-east-1, us-west-2, and Azure eastus2. Cortex Analyst semantic views are available on the same regions.\n\n### Step 2 — Create AI_ORDER_INSIGHTS Iceberg Table\n\n```sql\nCREATE OR REPLACE ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.AI_ORDER_INSIGHTS\n    CATALOG = 'SNOWFLAKE'\n    AS\nWITH deduped_orders AS (\n    SELECT order_id, customer_id, product, amount, order_date, status\n    FROM DATABRICKS_DEMO_DB.horizon_demo.customer_orders\n    QUALIFY ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY order_id) = 1\n),\ndeduped_sensitive AS (\n    SELECT order_id, region\n    FROM DATABRICKS_DEMO_DB.horizon_demo.sensitive_orders\n    QUALIFY ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY order_id) = 1\n)\nSELECT\n    co.order_id, co.customer_id, co.product, co.amount, co.order_date, co.status,\n    COALESCE(so.region, 'UNKNOWN') AS region,\n    -- Deterministic risk tier (reliable for governance demo)\n    CASE\n        WHEN co.status = 'CANCELLED' THEN 'HIGH'\n        WHEN co.amount \u003E= 500        THEN 'HIGH'\n        WHEN co.amount \u003E= 100        THEN 'MEDIUM'\n        ELSE 'LOW'\n    END AS risk_level,\n    -- Cortex-generated operational note\n    SNOWFLAKE.CORTEX.COMPLETE(\n        'llama3.1-8b',\n        'Write one operational sentence (max 15 words) for logistics. Product: ' || co.product ||\n        ', Amount: $' || co.amount::STRING || ', Status: ' || co.status || '.'\n    )::VARCHAR AS ops_note,\n    CURRENT_TIMESTAMP()::TIMESTAMP_LTZ(6) AS enriched_at\nFROM deduped_orders co\nLEFT JOIN deduped_sensitive so ON co.order_id = so.order_id;\n```\n\n\u003E **Why `QUALIFY ROW_NUMBER()`:** The source federated tables may contain duplicate rows from multiple Databricks INSERT runs. `QUALIFY` deduplicates per `order_id` before enrichment.\n\n\u003E **Why deterministic `CASE WHEN` for `risk_level`:** LLMs produce inconsistent results for strict classification rules. Using `CASE WHEN` ensures reliable HIGH/MEDIUM/LOW splits for the governance demo. `CORTEX.COMPLETE` is used for `ops_note` where natural language generation excels.\n\n### Step 3 — Re-Apply Masking Policy\n\n```sql\n-- CREATE OR REPLACE drops all column policies — must re-apply every time\nALTER ICEBERG TABLE HORIZON_DEMO_SFDB.DEMO_SCHEMA.AI_ORDER_INSIGHTS\n    MODIFY COLUMN risk_level\n    SET MASKING POLICY HORIZON_DEMO_SFDB.DEMO_SCHEMA.MASK_RISK_LEVEL;\n```\n\n\u003E **Important:** `CREATE OR REPLACE ICEBERG TABLE` drops all column policies on the table. Always run this `ALTER ICEBERG TABLE` immediately after the CTAS. The `06_cortex_ai_pipeline.ipynb` script does this automatically.\n\n### Step 4 — Governance Comparison on AI Output\n\n```sql\n-- ACCOUNTADMIN: sees actual risk level including HIGH\nUSE ROLE ACCOUNTADMIN;\nSELECT 'ACCOUNTADMIN' AS role_context, order_id, product, amount, region, risk_level\nFROM HORIZON_DEMO_SFDB.DEMO_SCHEMA.AI_ORDER_INSIGHTS ORDER BY order_id;\n\n-- Reader role: HIGH masked to *** RESTRICTED ***\nUSE ROLE EXT_COMPUTE_ENG_DEMO_ROLE;\nSELECT 'EXT_COMPUTE_ENG_DEMO_ROLE' AS role_context, order_id, product, amount, region, risk_level\nFROM HORIZON_DEMO_SFDB.DEMO_SCHEMA.AI_ORDER_INSIGHTS ORDER BY order_id;\n```\n\n**Expected:**\n\n| role | order_id | product | risk_level |\n|------|----------|---------|------------|\n| ACCOUNTADMIN | 1 | Laptop | HIGH |\n| ACCOUNTADMIN | 4 | Monitor | MEDIUM |\n| reader role | 1 | Laptop | *** RESTRICTED *** |\n| reader role | 4 | Monitor | MEDIUM |\n\n### Step 5 — Create Cortex Analyst Semantic View\n\n```sql\nCREATE OR REPLACE SEMANTIC VIEW HORIZON_DEMO_SFDB.DEMO_SCHEMA.ICEBERG_AI_SEMANTIC_VIEW\nTABLES (\n  orders     AS HORIZON_DEMO_SFDB.DEMO_SCHEMA.AI_ORDER_INSIGHTS\n               PRIMARY KEY (order_id)\n               WITH SYNONYMS = ('ai orders', 'enriched orders', 'risk orders'),\n  fed_orders AS DATABRICKS_DEMO_DB.horizon_demo.customer_orders\n               PRIMARY KEY (order_id)\n               WITH SYNONYMS = ('federated orders', 'source orders')\n)\nRELATIONSHIPS (\n  orders(order_id) REFERENCES fed_orders\n)\nFACTS (\n  orders.amount AS amount WITH SYNONYMS = ('order value', 'revenue')\n)\nDIMENSIONS (\n  orders.product    AS product,\n  orders.status     AS status     WITH SYNONYMS = ('fulfillment status'),\n  orders.region     AS region,\n  orders.risk_level AS risk_level WITH SYNONYMS = ('risk', 'risk tier'),\n  orders.order_date AS order_date,\n  orders.ops_note   AS ops_note   WITH SYNONYMS = ('operational note', 'ai note')\n)\nMETRICS (\n  orders.total_revenue   AS SUM(orders.amount)     WITH SYNONYMS = ('revenue', 'total sales'),\n  orders.order_count     AS COUNT(orders.order_id) WITH SYNONYMS = ('number of orders'),\n  orders.avg_order_value AS AVG(orders.amount)      WITH SYNONYMS = ('average order', 'AOV')\n);\n\nGRANT SELECT ON SEMANTIC VIEW HORIZON_DEMO_SFDB.DEMO_SCHEMA.ICEBERG_AI_SEMANTIC_VIEW\n    TO ROLE EXT_COMPUTE_ENG_DEMO_ROLE;\n```\n\n\u003E **Semantic views** are the recommended path for Cortex Analyst — they replace legacy YAML files and appear automatically in Snowsight → AI & ML → Cortex Analyst. Note: the alias direction in TABLES is `alias AS db.schema.table` (not `table AS alias`).\n\n\u003C!-- ------------------------ --\u003E\n\n## Scenario 2 — Snowpark Connect AI Pipeline Notebook\n\nUpload `07_ai_pipeline.ipynb` to your Snowflake workspace:\n**Snowflake Workspaces → drag and drop `07_ai_pipeline.ipynb` into the file tree**\n\n\u003E `snowpark-connect` is pre-installed in Snowflake Workspaces — no package picker step needed.\n\n### Initialize Session\n\n```python\nsf_session = get_active_session()\nsf_session.sql(f\"USE DATABASE {SF_INIT_DB}\").collect()\n\nconf = SparkConf().set(\"spark.sql.caseSensitive\", \"true\")\nspark = snowpark_connect.init_spark_session(conf=conf)\n```\n\n\u003E `caseSensitive=true` is required when reading catalog-linked (FDN) tables, which use lowercase identifiers. It is harmless when reading SF-managed tables.\n\n### Step 2 — Snowpark Connect Reads Federated Source Tables\n\n```python\n# Read from CLD — federated Databricks Iceberg (read only)\ndf_orders = spark.sql(f\"SELECT * FROM {TBL_ORDERS}\")\ndf_orders.select(\"order_id\", \"product\", \"amount\", \"status\").orderBy(\"order_id\").show()\n```\n\n### Step 4 — Cortex Enrichment + Write via sf_session\n\n```python\nsf_session.sql(f\"\"\"\n    CREATE OR REPLACE ICEBERG TABLE {TBL_INSIGHTS} CATALOG = 'SNOWFLAKE' AS\n    WITH deduped_orders AS ( ... ),\n         deduped_sensitive AS ( ... )\n    SELECT ..., SNOWFLAKE.CORTEX.COMPLETE('llama3.1-8b', ...) AS ops_note ...\n    FROM deduped_orders co LEFT JOIN deduped_sensitive so ON co.order_id = so.order_id\n\"\"\").collect()\n\n# Re-apply masking — CREATE OR REPLACE drops column policies\nsf_session.sql(f\"\"\"\n    ALTER ICEBERG TABLE {TBL_INSIGHTS}\n    MODIFY COLUMN risk_level\n    SET MASKING POLICY {SF_MANAGED_ICEBERG_DB}.{SF_DEMO_SCHEMA}.MASK_RISK_LEVEL\n\"\"\").collect()\n```\n\n### Step 5 — Governance Comparison via Snowpark Connect\n\n```python\nCOLS = [\"order_id\", \"product\", \"amount\", \"region\", \"risk_level\"]\n\nswitch_role(\"ACCOUNTADMIN\")\nadmin_df = sf_session.sql(f\"SELECT {','.join(COLS)} FROM {TBL_INSIGHTS}\").to_pandas()\nadmin_df.columns = [c.lower() for c in admin_df.columns]\n\nswitch_role(SF_READER_ROLE)\nreader_df = sf_session.sql(f\"SELECT {','.join(COLS)} FROM {TBL_INSIGHTS}\").to_pandas()\nreader_df.columns = [c.lower() for c in reader_df.columns]\n```\n\n\u003E `sf_session.sql().to_pandas()` returns **uppercase** column names. Call `.columns = [c.lower() for c in df.columns]` immediately after `to_pandas()`.\n\n\u003C!-- ------------------------ --\u003E\n\n## Scenario 2 — Cortex Analyst Demo\n\n**Open:** Snowsight → **AI & ML** → **Cortex Analyst**\n\n**Select the semantic view:**\n1. In the Cortex Analyst list, click `ICEBERG_AI_SEMANTIC_VIEW` under `HORIZON_DEMO_SFDB.DEMO_SCHEMA`\n2. Click the **Playground** tab at the top of the screen\n3. Type prompts in the **chat box at the bottom** of the Playground and press Enter\n\n### Demo Prompt Sequence\n\n**Prompt 1 — Revenue overview**\n\u003E *\"What is the total revenue by risk level?\"*\n\nExpected: HIGH = highest amount (Laptop $999.99), MEDIUM, LOW.\n\n**Prompt 2 — Governance live demo (key moment)**\n\u003E *\"Which orders are classified as HIGH risk and what are their operational notes?\"*\n\nAs ACCOUNTADMIN: Laptop, $999.99, `risk_level = HIGH` with Cortex ops note.\nSwitch role to `EXT_COMPUTE_ENG_DEMO_ROLE` and ask again → `risk_level = *** RESTRICTED ***`.\n\n**Prompt 3 — Business intelligence**\n\u003E *\"Show me all SHIPPED orders in the US-WEST region\"*\n\n**Prompt 4 — Cross-table (uses the RELATIONSHIP)**\n\u003E *\"Compare total revenue from enriched orders versus source federated orders\"*\n\nThe semantic view joins `AI_ORDER_INSIGHTS` (SF-managed) and `customer_orders` (Databricks-federated CLD) in a single query — Horizon masking applies throughout.\n\n**Prompt 5 — Analytics**\n\u003E *\"What is the average order value by fulfillment status?\"*\n\n\u003C!-- ------------------------ --\u003E\n\n## Conclusion And Resources\n\nCongratulations — you have completed all three scenarios!\n\n### What You Built\n\n- ✅ **Snowflake-managed Iceberg tables** with Horizon column masking and row access policies\n- ✅ **Snowpark Connect governance demo** showing Horizon policies enforced through PySpark on Snowflake's engine\n- ✅ **External engine read/write demo** via Horizon IRC with credential-vending write control\n- ✅ **Catalog-linked database** auto-federating externally-managed Iceberg tables\n- ✅ **Snowpark Connect notebook** with live role-based masking on federated tables\n- ✅ **Cortex Analyst semantic view** spanning SF-managed and federated Iceberg tables with Horizon masking enforced\n\n### What You Learned\n\n- How Snowflake manages Iceberg natively with Horizon governance enforced through Snowpark Connect at every query\n- How Horizon IRC exposes Snowflake-managed tables to any Iceberg-compatible engine, and how credential vending enforces write protection at the S3 layer\n- How catalog-linked databases auto-federate externally-managed Iceberg tables — and how Snowflake applies independent governance regardless of data origin\n- How `SNOWFLAKE.CORTEX.COMPLETE` enriches federated Iceberg data inline in SQL, with Horizon masking applying to AI-generated columns exactly as it does to raw data\n- How Cortex Analyst semantic views enable natural language querying across SF-managed and federated Iceberg tables with role-based masking enforced\n\n### Governance Summary\n\n**Snowflake-Managed Iceberg Tables** — Horizon policies enforced at the Snowflake SQL layer for every access path including Snowpark Connect and Cortex Analyst.\n\n| Access path | Table | Policy | Governance result |\n|-------------|-------|--------|-------------------|\n| Snowpark Connect (`ACCOUNTADMIN`) | `PROTECTED_TABLE` | Column masking + row access | 3 rows, `sensitive_data` raw |\n| Snowpark Connect (reader role) | `PROTECTED_TABLE` | Column masking + row access | 2 rows filtered, `*** MASKED ***` |\n| Snowpark Connect (`ACCOUNTADMIN`) | `AI_ORDER_INSIGHTS` | Column masking on AI column | `risk_level = HIGH` visible |\n| Snowpark Connect (reader role) | `AI_ORDER_INSIGHTS` | Column masking on AI column | `risk_level = *** RESTRICTED ***` |\n| Cortex Analyst (`ACCOUNTADMIN`) | `ICEBERG_AI_SEMANTIC_VIEW` | Semantic view + masking | NL query → `HIGH` risk visible |\n| Cortex Analyst (reader role) | `ICEBERG_AI_SEMANTIC_VIEW` | Semantic view + masking | NL query → `*** RESTRICTED ***` |\n| External engine via Horizon IRC | `PROTECTED_TABLE` | ❌ Bypassed (raw Parquet path) | 3 rows, `sensitive_data` raw |\n| External engine write to `OPEN_TABLE` | `OPEN_TABLE` | Credential vending | ✅ Write-capable S3 creds vended |\n| External engine write to `PROTECTED_TABLE` | `PROTECTED_TABLE` | Credential vending | ❌ S3 403 — read-only creds vended |\n\n**Externally-Managed Iceberg Tables (Catalog-Linked Database)** — Snowflake applies its own independent Horizon policies at query time, regardless of the source catalog's governance.\n\n| Access path | Table | Policy | Governance result |\n|-------------|-------|--------|-------------------|\n| Snowpark Connect (`ACCOUNTADMIN`) | `sensitive_orders` (CLD) | Snowflake masking — independent of Databricks UC | Real credit card numbers |\n| Snowpark Connect (reader role) | `sensitive_orders` (CLD) | Snowflake masking — independent of Databricks UC | `****-****-****-XXXX` |\n| Snowpark Connect (`ACCOUNTADMIN`) | `customer_orders` (CLD) | No policy applied | All rows, all columns |\n| Snowpark Connect (reader role) | `customer_orders` (CLD) | No policy applied | All rows, all columns |\n\n\u003E **Key insight:** Snowflake's Horizon governance and Databricks UC governance are completely independent. Snowflake enforces its own masking policies on federated tables at query time — the source catalog's policies do not propagate, and Snowflake's policies do not reach into the source catalog.\n\n### Related Resources\n\n- [Snowpark Connect for Apache Spark Documentation](https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-overview)\n- [Snowflake Managed Iceberg Tables](https://docs.snowflake.com/en/user-guide/tables-iceberg-snowflake)\n- [Catalog-Linked Databases](https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database)\n- [Horizon IRC (Iceberg REST Catalog)](https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-integration-open-api)\n- [Snowflake Cortex LLM Functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions)\n- [Cortex Analyst Semantic Views](https://docs.snowflake.com/en/user-guide/views-semantic)\n- [Intro to Snowpark Connect](https://www.snowflake.com/en/developers/guides/getting-started-with-snowpark-connect-for-apache-spark/)\n- [Getting Started with Iceberg Tables in Snowflake](https://www.snowflake.com/en/developers/guides/getting-started-iceberg-tables/)\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-0ef946b719","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-d3815c09ff",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-274e30511f","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-29",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-e80c98cd3d","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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Cluster Setup (Scenario 2 prerequisites)\u003C/h2\u003E","\u003Ch2\u003EScenario 1 — Snowflake Setup\u003C/h2\u003E","\u003Ch2\u003EScenario 1 — Snowpark Connect Governance Demo\u003C/h2\u003E","\u003Ch2\u003EGovernance Contrast — Snowpark Connect vs External Engine\u003C/h2\u003E","\u003Ch2\u003EScenario 2 — External Catalog Setup\u003C/h2\u003E","\u003Ch2\u003EScenario 2 — Snowflake Setup\u003C/h2\u003E","\u003Ch2\u003EScenario 2 — Snowpark Connect Governance Demo\u003C/h2\u003E","\u003Ch2\u003EScenario 2 — AI Enrichment Pipeline Setup\u003C/h2\u003E","\u003Ch2\u003EScenario 2 — Snowpark Connect AI Pipeline Notebook\u003C/h2\u003E","\u003Ch2\u003EScenario 2 — Cortex Analyst Demo\u003C/h2\u003E","\u003Ch2\u003EConclusion And 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