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evolution, and high-performance queries to data stored in object storage like Amazon S3. Both Snowflake and AWS support the Iceberg format natively, enabling customers to drastically improve data interoperability and build open analytic environments without vendor lock-in.\u003C/p\u003E\n","\u003Cp\u003EIn this hands-on lab you will connect Snowflake to a pre-configured AWS Glue Data Catalog using a \u003Cstrong\u003ECatalog-Linked Database (CLD)\u003C/strong\u003E — Snowflake's mechanism for auto-discovering and staying in sync with external Iceberg catalogs. The AWS infrastructure (S3 bucket, Glue database, IAM role) has been set up for you. Your job is to wire Snowflake to it in four SQL statements and start querying.\u003C/p\u003E\n","\u003Cp\u003EThe dataset is a Financial Services use case: insurance quote requests collected from systems and stored as Parquet on S3, converted to Iceberg format. The data contains customer, policy, and pricing attributes — a common pattern in insurance analytics for identifying churn risk and high quote frequency.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow Snowflake connects to external Iceberg catalogs via a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database\"\u003ECatalog-Linked Database\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EHow \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-storage\"\u003EExternal Volumes\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-catalog-integration-rest-glue\"\u003ECatalog Integrations\u003C/a\u003E work together to give Snowflake access to external Iceberg tables\u003C/li\u003E\u003Cli\u003EHow the Glue Iceberg REST Catalog (IRC) API enables Snowflake to discover and read tables directly from the Glue Data Catalog\u003C/li\u003E\u003Cli\u003EHow to query Apache Iceberg data in AWS Glue from Snowflake without copying or moving data\u003C/li\u003E\u003Cli\u003EHow to apply \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-horizon\"\u003ESnowflake Horizon\u003C/a\u003E governance — RBAC and dynamic data masking — to Iceberg tables\u003C/li\u003E\u003Cli\u003EHow to create a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic\"\u003ESemantic View\u003C/a\u003E on top of Iceberg data for AI-ready analytics\u003C/li\u003E\u003Cli\u003EHow to build a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agent\"\u003ECortex Agent\u003C/a\u003E and query your data lake in natural language\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake Enterprise account with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E access, deployed in \u003Cstrong\u003EAWS US West 2 (Oregon)\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDon't have a Snowflake account?\u003C/strong\u003E Sign up for a free trial at \u003Ca href=\"https://signup.snowflake.com/summit2026\"\u003Esignup.snowflake.com/summit2026\u003C/a\u003E. Select the \u003Cstrong\u003EAI Data Cloud for Enterprise\u003C/strong\u003E option — this trial includes free access to Cortex Code CLI.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E When creating your trial account, select \u003Cstrong\u003EAmazon Web Services\u003C/strong\u003E as the cloud provider and \u003Cstrong\u003EUS West (Oregon)\u003C/strong\u003E as the region. The lab infrastructure (S3 bucket, Glue catalog, IAM role) is deployed in AWS US West 2 — your Snowflake account must be in the same region to reach it.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake \u003Cstrong\u003EExternal Volume\u003C/strong\u003E connected to the lab's S3-backed Iceberg dataset\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ECatalog Integration\u003C/strong\u003E pointing to the AWS Glue Iceberg REST endpoint using SigV4 authentication\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ECatalog-Linked Database\u003C/strong\u003E that auto-discovers and syncs Iceberg tables from the Glue Data Catalog\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDynamic data masking policies\u003C/strong\u003E protecting PII columns in the Iceberg table\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ESemantic View\u003C/strong\u003E that defines business metrics and dimensions on your Iceberg data\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ECortex Agent\u003C/strong\u003E for natural language analytics on your data lake\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELab Environment\u003C/h2\u003E\n","\u003Cp\u003EThe AWS infrastructure for this lab has been pre-configured. You do not need an AWS account or AWS credentials to complete this lab.\u003C/p\u003E\n","\u003Cp\u003EHere is what has been set up on your behalf:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EResource\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDetails\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ES3 Bucket\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Es3://sf-lab-iceberg-407539788379/iceberg/\u003C/code\u003E (us-west-2)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EGlue Database\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eiceberg\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EIceberg Table\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Equotes\u003C/code\u003E — 40 columns, insurance quote records in Iceberg V2 format\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EIAM Role\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Earn:aws:iam::407539788379:role/sf-lab-shared-role\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EArchitecture\u003C/h3\u003E\n","\u003Cp\u003EThe diagram below shows how the components connect:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERaw insurance data was converted from Parquet to Apache Iceberg V2 format using an AWS Glue ETL job and stored in Amazon S3\u003C/li\u003E\u003Cli\u003EThe table is registered in the AWS Glue Data Catalog, which exposes it via the Iceberg REST Catalog (IRC) API\u003C/li\u003E\u003Cli\u003ESnowflake connects to the Glue catalog using SigV4 authentication via a Catalog Integration, and reads data files directly from S3 via an External Volume\u003C/li\u003E\u003Cli\u003EA Catalog-Linked Database auto-discovers all tables in the Glue catalog and keeps them in sync — no manual table registration needed\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EThe quotes table\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Equotes\u003C/code\u003E dataset represents insurance quote requests with the following key columns:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EColumn\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Euuid\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Estring\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnique quote identifier\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Equote_product\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Estring\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInsurance product type\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Equotedate\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Edate\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDate the quote was requested\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Epolicyno\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Estring\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPolicy number\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecreditscore\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Edecimal\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer credit score\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Enewriskpremium\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Edecimal\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECalculated risk premium\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etotalpremiumpayable\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Edecimal\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFinal premium amount\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esurname\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Estring\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer surname (PII)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eemail\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Estring\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer email address (PII)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ephonenumber\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Estring\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer phone number (PII)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edateofbirth\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Estring\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer date of birth (PII)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Epostcodedistrict\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Estring\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer postcode district\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ERunning SQL in Snowsight\u003C/h3\u003E\n","\u003Cp\u003EAll SQL in this lab runs in \u003Ca href=\"https://app.snowflake.com\"\u003ESnowsight\u003C/a\u003E, Snowflake's web interface. Here's how to run each block:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELog in to \u003Ca href=\"https://app.snowflake.com\"\u003ESnowsight\u003C/a\u003E and click \u003Cstrong\u003EProjects → Worksheets\u003C/strong\u003E in the left nav\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+\u003C/strong\u003E (top right) to create a new SQL worksheet\u003C/li\u003E\u003Cli\u003ESet your role to \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E using the role picker in the top-left corner of the worksheet\u003C/li\u003E\u003Cli\u003ESet your warehouse to \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E (or the warehouse available in your account)\u003C/li\u003E\u003Cli\u003EPaste a SQL block from the lab into the editor\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERun all statements\u003C/strong\u003E in the block: press \u003Ccode\u003ECtrl + Shift + Enter\u003C/code\u003E (Windows/Linux) or \u003Ccode\u003ECmd + Shift + Return\u003C/code\u003E (Mac), or click the \u003Cstrong\u003ERun All\u003C/strong\u003E button\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERun a single statement\u003C/strong\u003E: place your cursor inside it and press \u003Ccode\u003ECtrl + Enter\u003C/code\u003E (Windows/Linux) or \u003Ccode\u003ECmd + Return\u003C/code\u003E (Mac)\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E Each section in this lab uses a separate SQL block. You can run everything in a single worksheet in order, or create one worksheet per section to keep things organized.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild with Cortex Code\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECortex Code\u003C/a\u003E is Snowflake's AI coding assistant. It connects directly to your Snowflake account, reads schema context, and can execute SQL on your behalf — so you can complete this entire lab from the terminal with a single prompt instead of running each block manually.\u003C/p\u003E\n","\u003Ch3\u003ESetup\u003C/h3\u003E\n","\u003Cp\u003EInstall the Snowflake CLI and start a Cortex Code session connected to your trial account:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Install Snowflake CLI (macOS/Linux)\npip install snowflake-cli\n\n# Configure a connection to your trial account\nsnow connection add\n\n# Start Cortex Code\ncortex code\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStarter Prompt\u003C/h3\u003E\n","\u003Cp\u003EOnce inside Cortex Code, paste the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EI want to complete the Summit HOL Lakehouse Analytics lab end to end.\n\nMy Snowflake account is on AWS US West 2 (Oregon). I have ACCOUNTADMIN access\nand my warehouse is COMPUTE_WH.\n\nThe lab has pre-configured AWS infrastructure:\n- S3 bucket: s3://sf-lab-iceberg-407539788379/iceberg/\n- IAM role: arn:aws:iam::407539788379:role/sf-lab-shared-role\n- Glue database: iceberg, table: quotes (~56K insurance quote records)\n\nPlease run through all sections in order:\n\n1. Create External Volume my_iceberg_vol, Catalog Integration my_glue_int, and\n   Catalog-Linked Database my_iceberg_db. Verify the quotes table syncs.\n\n2. Query the quotes table — row count, product breakdown by volume, and premium\n   by credit score band.\n\n3. Set up governance:\n   - Create roles lab_analyst and lab_data_engineer with grants + warehouse access\n   - Create view iceberg_lab_db.analytics.quotes_vw over the Iceberg table\n   - Apply dynamic data masking to email, phonenumber, surname, and dateofbirth\n     columns on the view using ALTER VIEW\n   - Verify masking by querying as lab_analyst vs lab_data_engineer\n\n4. Create Semantic View iceberg_lab_db.analytics.quotes_sv on top of quotes_vw\n   with facts, dimensions, and metrics.\n\n5. Create Cortex Agent iceberg_lab_db.analytics.quotes_agent backed by quotes_sv.\n\nRun each SQL block, show me results, and explain what each step built.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code will run each section end to end — including role switching for masking verification. To resume at any step, tell it where you left off.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E If you prefer to run the SQL manually, continue to the next section.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Snowflake Objects\u003C/h2\u003E\n","\u003Cp\u003EIn this section you will create three Snowflake objects that wire your Snowflake account to the pre-configured AWS infrastructure. All three steps require \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to run:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELog in to \u003Ca href=\"https://app.snowflake.com\"\u003ESnowsight\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EGo to \u003Cstrong\u003EProjects → Worksheets\u003C/strong\u003E and click \u003Cstrong\u003E+\u003C/strong\u003E to open a new SQL worksheet\u003C/li\u003E\u003Cli\u003ESet your role to \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E and your warehouse to \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECopy each SQL block below, paste it into the worksheet, and click \u003Cstrong\u003ERun All\u003C/strong\u003E (or press \u003Ccode\u003ECtrl + Shift + Enter\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EConfirm the status message shows \u003Ccode\u003Esuccessfully created\u003C/code\u003E before moving to the next block\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EExternal Volume\u003C/h3\u003E\n","\u003Cp\u003EAn \u003Cstrong\u003EExternal Volume\u003C/strong\u003E tells Snowflake where the Iceberg data files live in cloud storage and which IAM role to use to access them. It is the credential layer between Snowflake and S3.\u003C/p\u003E\n","\u003Cp\u003ECopy and run in your Snowsight worksheet:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE EXTERNAL VOLUME my_iceberg_vol\n  STORAGE_LOCATIONS = (\n    (\n      NAME             = 'us-west-2'\n      STORAGE_PROVIDER = 'S3'\n      STORAGE_BASE_URL = 's3://sf-lab-iceberg-407539788379/iceberg/'\n      STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::407539788379:role/sf-lab-shared-role'\n    )\n  )\n  ALLOW_WRITES = FALSE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003EALLOW_WRITES = FALSE\u003C/code\u003E marks this volume as read-only — Snowflake will not attempt to write metadata or data back to S3.\u003C/p\u003E\n","\u003Ch3\u003ECatalog Integration\u003C/h3\u003E\n","\u003Cp\u003EA \u003Cstrong\u003ECatalog Integration\u003C/strong\u003E tells Snowflake how to reach the external Iceberg catalog — in this case, the AWS Glue Iceberg REST Catalog (IRC) endpoint. It handles authentication (SigV4) and points to the correct AWS account and region.\u003C/p\u003E\n","\u003Cp\u003ECopy and run in your Snowsight worksheet:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CATALOG INTEGRATION my_glue_int\n  CATALOG_SOURCE = ICEBERG_REST\n  TABLE_FORMAT   = ICEBERG\n  REST_CONFIG = (\n    CATALOG_URI      = 'https://glue.us-west-2.amazonaws.com/iceberg'\n    CATALOG_API_TYPE = AWS_GLUE\n    CATALOG_NAME     = '407539788379'\n  )\n  REST_AUTHENTICATION = (\n    TYPE                 = SIGV4\n    SIGV4_IAM_ROLE       = 'arn:aws:iam::407539788379:role/sf-lab-shared-role'\n    SIGV4_SIGNING_REGION = 'us-west-2'\n  )\n  ENABLED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECatalog-Linked Database\u003C/h3\u003E\n","\u003Cp\u003EA \u003Cstrong\u003ECatalog-Linked Database\u003C/strong\u003E connects to the Catalog Integration and automatically discovers every namespace and table registered in the Glue Data Catalog. Snowflake polls the catalog on the interval you specify and keeps its local view in sync — no manual \u003Ccode\u003EALTER ICEBERG TABLE ... REFRESH\u003C/code\u003E needed.\u003C/p\u003E\n","\u003Cp\u003ECopy and run in your Snowsight worksheet:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DATABASE my_iceberg_db\n  LINKED_CATALOG = (\n    CATALOG                  = 'my_glue_int',\n    SYNC_INTERVAL_SECONDS    = 3600,\n    ALLOWED_WRITE_OPERATIONS = NONE\n  )\n  EXTERNAL_VOLUME = 'my_iceberg_vol';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003ESYNC_INTERVAL_SECONDS = 3600\u003C/code\u003E sets the catalog poll interval to 1 hour. \u003Ccode\u003EALLOWED_WRITE_OPERATIONS = NONE\u003C/code\u003E enforces read-only access at the database level.\u003C/p\u003E\n","\u003Ch3\u003EVerify Sync\u003C/h3\u003E\n","\u003Cp\u003EAfter creating the database, Snowflake starts discovering tables from the Glue catalog. Run this in your worksheet to check the sync status:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECopy and run the statement below\u003C/li\u003E\u003Cli\u003ELook for \u003Ccode\u003E"failureDetails":[]\u003C/code\u003E and \u003Ccode\u003E"executionState":"RUNNING"\u003C/code\u003E or \u003Ccode\u003E"SUCCEEDED"\u003C/code\u003E in the output\u003C/li\u003E\u003Cli\u003EIf you see failures, wait 30 seconds and re-run\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SYSTEM$CATALOG_LINK_STATUS('my_iceberg_db');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The \u003Ccode\u003Equotes\u003C/code\u003E table should appear within 60 seconds of creating the database.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuery Data\u003C/h2\u003E\n","\u003Cp\u003EWith the Catalog-Linked Database created, the \u003Ccode\u003Equotes\u003C/code\u003E Iceberg table is available to query like any native Snowflake table. The data is read directly from S3 — nothing is copied into Snowflake storage.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to run:\u003C/strong\u003E In your Snowsight worksheet, make sure your role is \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E and your warehouse is active. Copy each query below and run it with \u003Ccode\u003ECtrl + Enter\u003C/code\u003E (or \u003Ccode\u003ECmd + Return\u003C/code\u003E on Mac). You can run all queries in the same worksheet.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E AWS Glue uses case-insensitive, lowercase identifiers. Always wrap schema and table names in double quotes when querying a Catalog-Linked Database.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EExplore the table\u003C/h3\u003E\n","\u003Cp\u003ERun these two queries to confirm the table is accessible and check row count:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM my_iceberg_db."iceberg"."quotes" LIMIT 10;\n\nSELECT COUNT(*) AS total_quotes FROM my_iceberg_db."iceberg"."quotes";\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EQuote volume by product\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    quote_product,\n    COUNT(*)                           AS total_quotes,\n    ROUND(AVG(totalpremiumpayable), 2) AS avg_premium\nFROM my_iceberg_db."iceberg"."quotes"\nGROUP BY quote_product\nORDER BY total_quotes DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EHigh-Frequency Customers\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    surname,\n    postcodedistrict,\n    COUNT(*)                           AS quote_count,\n    MIN(quotedate)                     AS first_quote,\n    MAX(quotedate)                     AS last_quote,\n    ROUND(AVG(totalpremiumpayable), 2) AS avg_premium\nFROM my_iceberg_db."iceberg"."quotes"\nGROUP BY surname, postcodedistrict\nHAVING COUNT(*) > 1\nORDER BY quote_count DESC\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPremium by Credit Score\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    CASE\n        WHEN creditscore >= 8 THEN 'High (8-10)'\n        WHEN creditscore >= 5 THEN 'Medium (5-7)'\n        ELSE 'Low (0-4)'\n    END                                AS credit_band,\n    COUNT(*)                           AS quote_count,\n    ROUND(AVG(newriskpremium), 2)      AS avg_risk_premium,\n    ROUND(AVG(totalpremiumpayable), 2) AS avg_total_premium\nFROM my_iceberg_db."iceberg"."quotes"\nGROUP BY credit_band\nORDER BY avg_risk_premium DESC;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EData Governance\u003C/h2\u003E\n","\u003Cp\u003ESnowflake Horizon governance policies apply natively to Iceberg tables in a Catalog-Linked Database — the data never needs to move into Snowflake storage for policies to take effect.\u003C/p\u003E\n","\u003Cp\u003EIn this section you will create two roles with different data access levels, then apply dynamic data masking to PII columns in the \u003Ccode\u003Equotes\u003C/code\u003E table. Analysts see partially masked data; data engineers see the full values.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to run:\u003C/strong\u003E Continue in your existing Snowsight worksheet (or open a new one). Each subsection below has its own SQL block — run them in order from top to bottom. The role must be \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E at the start of each block; the SQL includes \u003Ccode\u003EUSE ROLE ACCOUNTADMIN\u003C/code\u003E where needed.\u003C/p\u003E\n","\u003Ch3\u003ECreate roles\u003C/h3\u003E\n","\u003Cp\u003ERun this block to create both roles, set up the role hierarchy, grant them to your user, and give them warehouse access:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE ROLE IF NOT EXISTS lab_data_engineer;\nCREATE ROLE IF NOT EXISTS lab_analyst;\n\n-- Hierarchy: analyst is a subset of data engineer\nGRANT ROLE lab_analyst TO ROLE lab_data_engineer;\n\n-- Grant both roles to your user\nGRANT ROLE lab_data_engineer TO USER IDENTIFIER(CURRENT_USER());\nGRANT ROLE lab_analyst TO USER IDENTIFIER(CURRENT_USER());\n\n-- Grant warehouse access so roles can run queries\n-- Replace COMPUTE_WH with your warehouse name if different\nGRANT USAGE ON WAREHOUSE COMPUTE_WH TO ROLE lab_data_engineer;\nGRANT USAGE ON WAREHOUSE COMPUTE_WH TO ROLE lab_analyst;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGrant Access\u003C/h3\u003E\n","\u003Cp\u003ERun this block to give both roles access to the External Volume, Catalog Integration, and the Iceberg table in the CLD:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- External volume and catalog integration\nGRANT USAGE ON EXTERNAL VOLUME my_iceberg_vol TO ROLE lab_data_engineer;\nGRANT USAGE ON INTEGRATION my_glue_int TO ROLE lab_data_engineer;\n\n-- Catalog-linked database\nGRANT USAGE ON DATABASE my_iceberg_db TO ROLE lab_data_engineer;\nGRANT USAGE ON DATABASE my_iceberg_db TO ROLE lab_analyst;\nGRANT USAGE ON SCHEMA my_iceberg_db."iceberg" TO ROLE lab_data_engineer;\nGRANT USAGE ON SCHEMA my_iceberg_db."iceberg" TO ROLE lab_analyst;\nGRANT SELECT ON ALL ICEBERG TABLES IN SCHEMA my_iceberg_db."iceberg" TO ROLE lab_data_engineer;\nGRANT SELECT ON ALL ICEBERG TABLES IN SCHEMA my_iceberg_db."iceberg" TO ROLE lab_analyst;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EView Wrapper\u003C/h3\u003E\n","\u003Cp\u003EBefore applying masking policies, create a thin view over the Iceberg table. Masking policies cannot be applied directly to tables in a Catalog-Linked Database — they must be attached to a view wrapper. This same view is also used by the Semantic View in the next section.\u003C/p\u003E\n","\u003Cp\u003ERun this block to create the supporting database, schema, and view:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS iceberg_lab_db;\nCREATE SCHEMA IF NOT EXISTS iceberg_lab_db.analytics;\n\nCREATE OR REPLACE VIEW iceberg_lab_db.analytics.quotes_vw AS\nSELECT * FROM my_iceberg_db."iceberg"."quotes";\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMasking Policies\u003C/h3\u003E\n","\u003Cp\u003EEach policy partially masks a PII column. \u003Ccode\u003ELAB_DATA_ENGINEER\u003C/code\u003E and \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E see the real value — all other roles see a redacted version.\u003C/p\u003E\n","\u003Cp\u003ERun this block to create all four masking policies:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE iceberg_lab_db;\nUSE SCHEMA analytics;\n\n-- Email: john.doe@example.com → j***@***.com\nCREATE OR REPLACE MASKING POLICY mask_email\n  AS (val STRING) RETURNS STRING ->\n    CASE\n      WHEN CURRENT_ROLE() IN ('LAB_DATA_ENGINEER', 'ACCOUNTADMIN') THEN val\n      ELSE CONCAT(LEFT(val, 1), '***@***.', SPLIT_PART(val, '.', -1))\n    END;\n\n-- Phone: 07123456789 → 071*****789\nCREATE OR REPLACE MASKING POLICY mask_phone\n  AS (val STRING) RETURNS STRING ->\n    CASE\n      WHEN CURRENT_ROLE() IN ('LAB_DATA_ENGINEER', 'ACCOUNTADMIN') THEN val\n      ELSE CONCAT(LEFT(val, 3), REPEAT('*', LENGTH(val) - 6), RIGHT(val, 3))\n    END;\n\n-- Surname: Smith → S****\nCREATE OR REPLACE MASKING POLICY mask_surname\n  AS (val STRING) RETURNS STRING ->\n    CASE\n      WHEN CURRENT_ROLE() IN ('LAB_DATA_ENGINEER', 'ACCOUNTADMIN') THEN val\n      ELSE CONCAT(LEFT(val, 1), REPEAT('*', GREATEST(LENGTH(val) - 1, 4)))\n    END;\n\n-- Date of birth: 1985-03-15 → ****-**-15\nCREATE OR REPLACE MASKING POLICY mask_dob\n  AS (val STRING) RETURNS STRING ->\n    CASE\n      WHEN CURRENT_ROLE() IN ('LAB_DATA_ENGINEER', 'ACCOUNTADMIN') THEN val\n      ELSE CONCAT('****-**-', RIGHT(val, 2))\n    END;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EApply Policies\u003C/h3\u003E\n","\u003Cp\u003EApply each masking policy to the corresponding column on the view wrapper. Masking is enforced whenever the view is queried — directly in SQL, via the semantic view, or through the Cortex Agent.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nALTER VIEW iceberg_lab_db.analytics.quotes_vw\n  ALTER COLUMN email SET MASKING POLICY iceberg_lab_db.analytics.mask_email;\n\nALTER VIEW iceberg_lab_db.analytics.quotes_vw\n  ALTER COLUMN phonenumber SET MASKING POLICY iceberg_lab_db.analytics.mask_phone;\n\nALTER VIEW iceberg_lab_db.analytics.quotes_vw\n  ALTER COLUMN surname SET MASKING POLICY iceberg_lab_db.analytics.mask_surname;\n\nALTER VIEW iceberg_lab_db.analytics.quotes_vw\n  ALTER COLUMN dateofbirth SET MASKING POLICY iceberg_lab_db.analytics.mask_dob;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Masking\u003C/h3\u003E\n","\u003Cp\u003ESwitch to each role and run the same query to see the difference. You can switch roles two ways:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EIn the SQL block:\u003C/strong\u003E run \u003Ccode\u003EUSE ROLE lab_analyst;\u003C/code\u003E before the SELECT (as shown below)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIn the Snowsight UI:\u003C/strong\u003E use the role picker at the top-left of the worksheet\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESwitch to the analyst role — PII is partially masked:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE lab_analyst;\n\nSELECT uuid, surname, email, phonenumber, dateofbirth, totalpremiumpayable\nFROM iceberg_lab_db.analytics.quotes_vw\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESwitch to the data engineer role — full values visible:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE lab_data_engineer;\n\nSELECT uuid, surname, email, phonenumber, dateofbirth, totalpremiumpayable\nFROM iceberg_lab_db.analytics.quotes_vw\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe same Iceberg data in AWS Glue, governed entirely by Snowflake — no data movement, no copies, no AWS-side policy changes needed.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESemantic View\u003C/h2\u003E\n","\u003Cp\u003EA \u003Cstrong\u003ESemantic View\u003C/strong\u003E defines the business meaning of your data — dimensions, metrics, and facts — so that Snowflake's AI can understand and answer questions about it in natural language. The semantic view respects masking policies automatically: an analyst querying via natural language sees the same masked values as they would in SQL.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to run:\u003C/strong\u003E Continue in your Snowsight worksheet with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E. Run the blocks below in order.\u003C/p\u003E\n","\u003Ch3\u003EView Wrapper\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Equotes_vw\u003C/code\u003E view was created in the Data Governance section with masking policies already applied. Semantic views require a plain view reference in the same schema — this view serves that role here too.\u003C/p\u003E\n","\u003Cp\u003ENo additional SQL needed. The view \u003Ccode\u003Eiceberg_lab_db.analytics.quotes_vw\u003C/code\u003E is ready to use.\u003C/p\u003E\n","\u003Ch3\u003ECreate View\u003C/h3\u003E\n","\u003Cp\u003ERun this block to create the semantic view. It defines the table mapping, facts, dimensions, and metrics that Cortex Analyst will use to translate natural language into SQL:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE iceberg_lab_db;\nUSE SCHEMA analytics;\n\nCREATE OR REPLACE SEMANTIC VIEW quotes_sv\n  TABLES (\n    quotes as quotes_vw\n      primary key (uuid)\n      comment='Insurance quote requests from the Iceberg data lake in AWS Glue'\n  )\n  FACTS (\n    quotes.newriskpremium      as newriskpremium      comment='Calculated risk premium',\n    quotes.totalpremiumpayable as totalpremiumpayable comment='Total premium payable',\n    quotes.iptamount           as iptamount           comment='Insurance premium tax',\n    quotes.quote_record        as 1                   comment='One record per quote'\n  )\n  DIMENSIONS (\n    quotes.quote_product    as quote_product    with synonyms=('product type', 'insurance product', 'cover type') comment='Insurance product type',\n    quotes.quotedate        as quotedate        with synonyms=('quote date', 'date', 'when requested')             comment='Date the quote was requested',\n    quotes.maritalstatus    as maritalstatus    with synonyms=('marital status', 'married', 'single')              comment='Customer marital status',\n    quotes.homeownerind     as homeownerind     with synonyms=('homeowner', 'owns home', 'property owner')         comment='Whether customer owns home',\n    quotes.sex              as sex              with synonyms=('gender', 'customer gender')                         comment='Customer gender',\n    quotes.postcodedistrict as postcodedistrict with synonyms=('district', 'location', 'area', 'region')           comment='Customer postcode district',\n    quotes.previnsr         as previnsr         with synonyms=('previous insurer', 'prior insurer')                 comment='Previous insurance provider'\n  )\n  METRICS (\n    quotes.total_quotes         as COUNT(quotes.quote_record)      comment='Total number of quote requests',\n    quotes.avg_total_premium    as AVG(quotes.totalpremiumpayable)  comment='Average total premium payable',\n    quotes.avg_risk_premium     as AVG(quotes.newriskpremium)       comment='Average risk premium',\n    quotes.total_premium_volume as SUM(quotes.totalpremiumpayable)  comment='Total premium volume'\n  )\n  comment='Insurance quote analytics — Iceberg data in AWS Glue via Catalog-Linked Database';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The semantic view uses a \u003Ccode\u003Equotes as quotes_vw\u003C/code\u003E mapping where \u003Ccode\u003Equotes\u003C/code\u003E is the internal reference name and \u003Ccode\u003Equotes_vw\u003C/code\u003E is the physical view. Column references in FACTS, DIMENSIONS, and METRICS use the reference name \u003Ccode\u003Equotes\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EGrant Access\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EGRANT USAGE ON DATABASE iceberg_lab_db TO ROLE lab_analyst;\nGRANT USAGE ON SCHEMA iceberg_lab_db.analytics TO ROLE lab_analyst;\nGRANT SELECT ON VIEW iceberg_lab_db.analytics.quotes_vw TO ROLE lab_analyst;\nGRANT SELECT ON SEMANTIC VIEW iceberg_lab_db.analytics.quotes_sv TO ROLE lab_analyst;\nGRANT USAGE ON DATABASE iceberg_lab_db TO ROLE lab_data_engineer;\nGRANT USAGE ON SCHEMA iceberg_lab_db.analytics TO ROLE lab_data_engineer;\nGRANT SELECT ON VIEW iceberg_lab_db.analytics.quotes_vw TO ROLE lab_data_engineer;\nGRANT SELECT ON SEMANTIC VIEW iceberg_lab_db.analytics.quotes_sv TO ROLE lab_data_engineer;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify the semantic view:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW SEMANTIC VIEWS IN SCHEMA iceberg_lab_db.analytics;\nSHOW SEMANTIC METRICS IN iceberg_lab_db.analytics.quotes_sv;\nSHOW SEMANTIC DIMENSIONS IN iceberg_lab_db.analytics.quotes_sv;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ENatural Language Queries\u003C/h2\u003E\n","\u003Cp\u003EA \u003Cstrong\u003ECortex Agent\u003C/strong\u003E wraps the semantic view and exposes it as a natural language interface. Snowflake translates plain English questions into SQL against your Iceberg data — masking policies are enforced automatically based on the querying role.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to run:\u003C/strong\u003E Run the SQL blocks below in your Snowsight worksheet with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E. After creating the agent, open it directly in Snowsight (no SQL needed) to ask questions.\u003C/p\u003E\n","\u003Ch3\u003ECreate the agent\u003C/h3\u003E\n","\u003Cp\u003ERun this block to create the agent backed by your semantic view:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nUSE DATABASE iceberg_lab_db;\nUSE SCHEMA analytics;\n\nCREATE OR REPLACE AGENT iceberg_lab_db.analytics.quotes_agent\n  WITH PROFILE = '{"display_name": "Insurance Quotes Analyst"}'\n  COMMENT = 'Natural language analytics on Iceberg insurance quotes data in AWS Glue'\nFROM SPECIFICATION $$\n{\n  "instructions": {\n    "response": "Answer questions about insurance quote data. Use the quotes tool to query premium amounts, product types, homeowner status, marital status, and postcode district. Summarize results clearly and suggest follow-up questions."\n  },\n  "tools": [\n    {\n      "tool_spec": {\n        "type": "cortex_analyst_text_to_sql",\n        "name": "Query Insurance Quotes",\n        "description": "Query insurance quote data including premiums, product types, homeowner status, marital status, and postcode district."\n      }\n    }\n  ],\n  "tool_resources": {\n    "Query Insurance Quotes": {\n      "semantic_view": "iceberg_lab_db.analytics.quotes_sv"\n    }\n  }\n}\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESnowflake CoWork\u003C/h3\u003E\n","\u003Cp\u003ETo make the agent accessible via \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-intelligence\"\u003ESnowflake CoWork\u003C/a\u003E, grant the Snowflake service role access to the underlying objects:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nGRANT USAGE ON DATABASE iceberg_lab_db TO ROLE SNOWFLAKE;\nGRANT USAGE ON SCHEMA iceberg_lab_db.analytics TO ROLE SNOWFLAKE;\nGRANT SELECT ON VIEW iceberg_lab_db.analytics.quotes_vw TO ROLE SNOWFLAKE;\nGRANT SELECT ON SEMANTIC VIEW iceberg_lab_db.analytics.quotes_sv TO ROLE SNOWFLAKE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The \u003Ccode\u003ESNOWFLAKE\u003C/code\u003E service role is available in Snowflake Enterprise and Business Critical accounts. Trial accounts do not include this role — if you receive a "Role does not exist" error, skip this step. The agent is still fully accessible via \u003Cstrong\u003EAI & ML > Agents\u003C/strong\u003E in Snowsight.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EAsk Questions\u003C/h3\u003E\n","\u003Cp\u003ETo open the agent in Snowsight:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the left nav, click \u003Cstrong\u003EAI & ML → Agents\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EFind \u003Cstrong\u003EInsurance Quotes Analyst\u003C/strong\u003E in the list and click \u003Cstrong\u003EOpen\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EType a question in the chat input and press Enter\u003C/li\u003E\u003Cli\u003EThe agent translates your question into SQL against \u003Ccode\u003Equotes_sv\u003C/code\u003E and returns the result\u003C/li\u003E\u003Cli\u003ETo test masking enforcement: change your active role (top-left role picker) to \u003Ccode\u003Elab_analyst\u003C/code\u003E or \u003Ccode\u003Elab_data_engineer\u003C/code\u003E and ask the same question — PII fields will be masked or unmasked based on your role\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe agent is also available in Snowflake CoWork (enterprise accounts only).\u003C/p\u003E\n","\u003Cp\u003ETry these questions:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-text\"\u003EWhat are the top 5 insurance products by number of quotes?\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-text\"\u003EWhat is the average premium for homeowners vs non-homeowners?\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-text\"\u003EWhich postcode district has the highest average risk premium?\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-text\"\u003EShow me quote volume broken down by marital status and product type.\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-text\"\u003EWhat is the total premium volume by previous insurer?\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ESwitch between the \u003Ccode\u003Elab_analyst\u003C/code\u003E and \u003Ccode\u003Elab_data_engineer\u003C/code\u003E roles to see how the same natural language question returns different results — masking policies are enforced end-to-end, from Iceberg through the semantic view to the agent response.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully connected Snowflake to an Apache Iceberg data lake in AWS Glue — querying, governing, and analyzing the data entirely from Snowflake without moving a single byte.\u003C/p\u003E\n","\u003Cp\u003EIn this lab you built a complete open lakehouse analytics stack:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EConnected Snowflake to AWS Glue via a Catalog-Linked Database using the Iceberg REST Catalog API\u003C/li\u003E\u003Cli\u003EApplied Snowflake Horizon governance — RBAC and dynamic data masking — to Iceberg tables in AWS\u003C/li\u003E\u003Cli\u003ECreated a Semantic View to define business metrics and dimensions on your Iceberg data\u003C/li\u003E\u003Cli\u003EBuilt a Cortex Agent for natural language analytics, with masking enforced end-to-end\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow External Volumes, Catalog Integrations, and Catalog-Linked Databases work together\u003C/li\u003E\u003Cli\u003EHow Snowflake Horizon governance applies natively to external Iceberg tables\u003C/li\u003E\u003Cli\u003EHow Semantic Views bridge raw Iceberg data and AI-powered analytics\u003C/li\u003E\u003Cli\u003EHow Cortex Agents translate natural language into governed SQL against your data lake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EDocumentation:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg\"\u003EApache Iceberg tables in Snowflake\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-configure-catalog-integration-rest-glue\"\u003EConfigure a catalog integration for AWS Glue Iceberg REST\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-column-ddm-intro\"\u003EDynamic Data Masking\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic\"\u003ESemantic Views\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agent\"\u003ECortex Agents\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ERelated guides:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue\"\u003EBuild Data Lakes using Apache Iceberg with Snowflake and AWS Glue\u003C/a\u003E — Go deeper on the full AWS setup: CloudFormation, Lake Formation credential vending, and the complete Glue IRC integration with Cortex Code fast path\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/get-started-snowflake-managed-iceberg-tables\"\u003EGet Started with Snowflake-Managed Iceberg Tables\u003C/a\u003E — Create Snowflake-managed Iceberg tables, stream and transform fleet data, query with Snowflake CoWork, and read the same tables from DuckDB and Apache Spark\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/iceberg-v3-tables-comprehensive-guide\"\u003EIceberg V3 Tables Comprehensive Guide\u003C/a\u003E — Build an end-to-end enterprise lakehouse platform using Iceberg V3 tables with streaming, variant data, time-series, geospatial analytics, governance, and AI\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Bring Snowflake to your data: Powering Analytics and AI on the Lakehouse","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\n[Apache Iceberg](https://iceberg.apache.org/) is an open table format for large analytical datasets — it brings ACID transactions, schema evolution, and high-performance queries to data stored in object storage like Amazon S3. Both Snowflake and AWS support the Iceberg format natively, enabling customers to drastically improve data interoperability and build open analytic environments without vendor lock-in.\n\nIn this hands-on lab you will connect Snowflake to a pre-configured AWS Glue Data Catalog using a **Catalog-Linked Database (CLD)** — Snowflake's mechanism for auto-discovering and staying in sync with external Iceberg catalogs. The AWS infrastructure (S3 bucket, Glue database, IAM role) has been set up for you. Your job is to wire Snowflake to it in four SQL statements and start querying.\n\nThe dataset is a Financial Services use case: insurance quote requests collected from systems and stored as Parquet on S3, converted to Iceberg format. The data contains customer, policy, and pricing attributes — a common pattern in insurance analytics for identifying churn risk and high quote frequency.\n\n### What You'll Learn\n- How Snowflake connects to external Iceberg catalogs via a [Catalog-Linked Database](https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database)\n- How [External Volumes](https://docs.snowflake.com/en/user-guide/tables-iceberg-storage) and [Catalog Integrations](https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-catalog-integration-rest-glue) work together to give Snowflake access to external Iceberg tables\n- How the Glue Iceberg REST Catalog (IRC) API enables Snowflake to discover and read tables directly from the Glue Data Catalog\n- How to query Apache Iceberg data in AWS Glue from Snowflake without copying or moving data\n- How to apply [Snowflake Horizon](https://docs.snowflake.com/en/user-guide/snowflake-horizon) governance — RBAC and dynamic data masking — to Iceberg tables\n- How to create a [Semantic View](https://docs.snowflake.com/en/user-guide/views-semantic) on top of Iceberg data for AI-ready analytics\n- How to build a [Cortex Agent](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agent) and query your data lake in natural language\n\n### What You'll Need\n- A Snowflake Enterprise account with `ACCOUNTADMIN` access, deployed in **AWS US West 2 (Oregon)**\n\n\u003E **Don't have a Snowflake account?** Sign up for a free trial at [signup.snowflake.com/summit2026](https://signup.snowflake.com/summit2026). Select the **AI Data Cloud for Enterprise** option — this trial includes free access to Cortex Code CLI.\n\n\u003E **Important:** When creating your trial account, select **Amazon Web Services** as the cloud provider and **US West (Oregon)** as the region. The lab infrastructure (S3 bucket, Glue catalog, IAM role) is deployed in AWS US West 2 — your Snowflake account must be in the same region to reach it.\n\n### What You'll Build\n- A Snowflake **External Volume** connected to the lab's S3-backed Iceberg dataset\n- A **Catalog Integration** pointing to the AWS Glue Iceberg REST endpoint using SigV4 authentication\n- A **Catalog-Linked Database** that auto-discovers and syncs Iceberg tables from the Glue Data Catalog\n- **Dynamic data masking policies** protecting PII columns in the Iceberg table\n- A **Semantic View** that defines business metrics and dimensions on your Iceberg data\n- A **Cortex Agent** for natural language analytics on your data lake\n\n\u003C!-- ------------------------ --\u003E\n## Lab Environment\n\nThe AWS infrastructure for this lab has been pre-configured. You do not need an AWS account or AWS credentials to complete this lab.\n\nHere is what has been set up on your behalf:\n\n| Resource | Details |\n|---|---|\n| **S3 Bucket** | `s3://sf-lab-iceberg-407539788379/iceberg/` (us-west-2) |\n| **Glue Database** | `iceberg` |\n| **Iceberg Table** | `quotes` — 40 columns, insurance quote records in Iceberg V2 format |\n| **IAM Role** | `arn:aws:iam::407539788379:role/sf-lab-shared-role` |\n\n### Architecture\n\nThe diagram below shows how the components connect:\n\n- Raw insurance data was converted from Parquet to Apache Iceberg V2 format using an AWS Glue ETL job and stored in Amazon S3\n- The table is registered in the AWS Glue Data Catalog, which exposes it via the Iceberg REST Catalog (IRC) API\n- Snowflake connects to the Glue catalog using SigV4 authentication via a Catalog Integration, and reads data files directly from S3 via an External Volume\n- A Catalog-Linked Database auto-discovers all tables in the Glue catalog and keeps them in sync — no manual table registration needed\n\n### The quotes table\n\nThe `quotes` dataset represents insurance quote requests with the following key columns:\n\n| Column | Type | Description |\n|---|---|---|\n| `uuid` | string | Unique quote identifier |\n| `quote_product` | string | Insurance product type |\n| `quotedate` | date | Date the quote was requested |\n| `policyno` | string | Policy number |\n| `creditscore` | decimal | Customer credit score |\n| `newriskpremium` | decimal | Calculated risk premium |\n| `totalpremiumpayable` | decimal | Final premium amount |\n| `surname` | string | Customer surname (PII) |\n| `email` | string | Customer email address (PII) |\n| `phonenumber` | string | Customer phone number (PII) |\n| `dateofbirth` | string | Customer date of birth (PII) |\n| `postcodedistrict` | string | Customer postcode district |\n\n### Running SQL in Snowsight\n\nAll SQL in this lab runs in [Snowsight](https://app.snowflake.com), Snowflake's web interface. Here's how to run each block:\n\n1. Log in to [Snowsight](https://app.snowflake.com) and click **Projects → Worksheets** in the left nav\n2. Click **+** (top right) to create a new SQL worksheet\n3. Set your role to `ACCOUNTADMIN` using the role picker in the top-left corner of the worksheet\n4. Set your warehouse to `COMPUTE_WH` (or the warehouse available in your account)\n5. Paste a SQL block from the lab into the editor\n6. **Run all statements** in the block: press `Ctrl + Shift + Enter` (Windows/Linux) or `Cmd + Shift + Return` (Mac), or click the **Run All** button\n7. **Run a single statement**: place your cursor inside it and press `Ctrl + Enter` (Windows/Linux) or `Cmd + Return` (Mac)\n\n\u003E **Tip:** Each section in this lab uses a separate SQL block. You can run everything in a single worksheet in order, or create one worksheet per section to keep things organized.\n\n\u003C!-- ------------------------ --\u003E\n## Build with Cortex Code\n\n[Cortex Code](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) is Snowflake's AI coding assistant. It connects directly to your Snowflake account, reads schema context, and can execute SQL on your behalf — so you can complete this entire lab from the terminal with a single prompt instead of running each block manually.\n\n### Setup\n\nInstall the Snowflake CLI and start a Cortex Code session connected to your trial account:\n\n```bash\n# Install Snowflake CLI (macOS/Linux)\npip install snowflake-cli\n\n# Configure a connection to your trial account\nsnow connection add\n\n# Start Cortex Code\ncortex code\n```\n\n### Starter Prompt\n\nOnce inside Cortex Code, paste the following:\n\n```\nI want to complete the Summit HOL Lakehouse Analytics lab end to end.\n\nMy Snowflake account is on AWS US West 2 (Oregon). I have ACCOUNTADMIN access\nand my warehouse is COMPUTE_WH.\n\nThe lab has pre-configured AWS infrastructure:\n- S3 bucket: s3://sf-lab-iceberg-407539788379/iceberg/\n- IAM role: arn:aws:iam::407539788379:role/sf-lab-shared-role\n- Glue database: iceberg, table: quotes (~56K insurance quote records)\n\nPlease run through all sections in order:\n\n1. Create External Volume my_iceberg_vol, Catalog Integration my_glue_int, and\n   Catalog-Linked Database my_iceberg_db. Verify the quotes table syncs.\n\n2. Query the quotes table — row count, product breakdown by volume, and premium\n   by credit score band.\n\n3. Set up governance:\n   - Create roles lab_analyst and lab_data_engineer with grants + warehouse access\n   - Create view iceberg_lab_db.analytics.quotes_vw over the Iceberg table\n   - Apply dynamic data masking to email, phonenumber, surname, and dateofbirth\n     columns on the view using ALTER VIEW\n   - Verify masking by querying as lab_analyst vs lab_data_engineer\n\n4. Create Semantic View iceberg_lab_db.analytics.quotes_sv on top of quotes_vw\n   with facts, dimensions, and metrics.\n\n5. Create Cortex Agent iceberg_lab_db.analytics.quotes_agent backed by quotes_sv.\n\nRun each SQL block, show me results, and explain what each step built.\n```\n\nCortex Code will run each section end to end — including role switching for masking verification. To resume at any step, tell it where you left off.\n\n\u003E **Note:** If you prefer to run the SQL manually, continue to the next section.\n\n\u003C!-- ------------------------ --\u003E\n## Create Snowflake Objects\n\nIn this section you will create three Snowflake objects that wire your Snowflake account to the pre-configured AWS infrastructure. All three steps require `ACCOUNTADMIN`.\n\n**How to run:**\n1. Log in to [Snowsight](https://app.snowflake.com)\n2. Go to **Projects → Worksheets** and click **+** to open a new SQL worksheet\n3. Set your role to `ACCOUNTADMIN` and your warehouse to `COMPUTE_WH`\n4. Copy each SQL block below, paste it into the worksheet, and click **Run All** (or press `Ctrl + Shift + Enter`)\n5. Confirm the status message shows `successfully created` before moving to the next block\n\n### External Volume\n\nAn **External Volume** tells Snowflake where the Iceberg data files live in cloud storage and which IAM role to use to access them. It is the credential layer between Snowflake and S3.\n\nCopy and run in your Snowsight worksheet:\n\n```sql\nCREATE OR REPLACE EXTERNAL VOLUME my_iceberg_vol\n  STORAGE_LOCATIONS = (\n    (\n      NAME             = 'us-west-2'\n      STORAGE_PROVIDER = 'S3'\n      STORAGE_BASE_URL = 's3://sf-lab-iceberg-407539788379/iceberg/'\n      STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::407539788379:role/sf-lab-shared-role'\n    )\n  )\n  ALLOW_WRITES = FALSE;\n```\n\n`ALLOW_WRITES = FALSE` marks this volume as read-only — Snowflake will not attempt to write metadata or data back to S3.\n\n### Catalog Integration\n\nA **Catalog Integration** tells Snowflake how to reach the external Iceberg catalog — in this case, the AWS Glue Iceberg REST Catalog (IRC) endpoint. It handles authentication (SigV4) and points to the correct AWS account and region.\n\nCopy and run in your Snowsight worksheet:\n\n```sql\nCREATE OR REPLACE CATALOG INTEGRATION my_glue_int\n  CATALOG_SOURCE = ICEBERG_REST\n  TABLE_FORMAT   = ICEBERG\n  REST_CONFIG = (\n    CATALOG_URI      = 'https://glue.us-west-2.amazonaws.com/iceberg'\n    CATALOG_API_TYPE = AWS_GLUE\n    CATALOG_NAME     = '407539788379'\n  )\n  REST_AUTHENTICATION = (\n    TYPE                 = SIGV4\n    SIGV4_IAM_ROLE       = 'arn:aws:iam::407539788379:role/sf-lab-shared-role'\n    SIGV4_SIGNING_REGION = 'us-west-2'\n  )\n  ENABLED = TRUE;\n```\n\n### Catalog-Linked Database\n\nA **Catalog-Linked Database** connects to the Catalog Integration and automatically discovers every namespace and table registered in the Glue Data Catalog. Snowflake polls the catalog on the interval you specify and keeps its local view in sync — no manual `ALTER ICEBERG TABLE ... REFRESH` needed.\n\nCopy and run in your Snowsight worksheet:\n\n```sql\nCREATE OR REPLACE DATABASE my_iceberg_db\n  LINKED_CATALOG = (\n    CATALOG                  = 'my_glue_int',\n    SYNC_INTERVAL_SECONDS    = 3600,\n    ALLOWED_WRITE_OPERATIONS = NONE\n  )\n  EXTERNAL_VOLUME = 'my_iceberg_vol';\n```\n\n`SYNC_INTERVAL_SECONDS = 3600` sets the catalog poll interval to 1 hour. `ALLOWED_WRITE_OPERATIONS = NONE` enforces read-only access at the database level.\n\n### Verify Sync\n\nAfter creating the database, Snowflake starts discovering tables from the Glue catalog. Run this in your worksheet to check the sync status:\n\n1. Copy and run the statement below\n2. Look for `\"failureDetails\":[]` and `\"executionState\":\"RUNNING\"` or `\"SUCCEEDED\"` in the output\n3. If you see failures, wait 30 seconds and re-run\n\n```sql\nSELECT SYSTEM$CATALOG_LINK_STATUS('my_iceberg_db');\n```\n\n\u003E **Note:** The `quotes` table should appear within 60 seconds of creating the database.\n\n\u003C!-- ------------------------ --\u003E\n## Query Data\n\nWith the Catalog-Linked Database created, the `quotes` Iceberg table is available to query like any native Snowflake table. The data is read directly from S3 — nothing is copied into Snowflake storage.\n\n**How to run:** In your Snowsight worksheet, make sure your role is `ACCOUNTADMIN` and your warehouse is active. Copy each query below and run it with `Ctrl + Enter` (or `Cmd + Return` on Mac). You can run all queries in the same worksheet.\n\n\u003E **Important:** AWS Glue uses case-insensitive, lowercase identifiers. Always wrap schema and table names in double quotes when querying a Catalog-Linked Database.\n\n### Explore the table\n\nRun these two queries to confirm the table is accessible and check row count:\n\n```sql\nSELECT * FROM my_iceberg_db.\"iceberg\".\"quotes\" LIMIT 10;\n\nSELECT COUNT(*) AS total_quotes FROM my_iceberg_db.\"iceberg\".\"quotes\";\n```\n\n### Quote volume by product\n\n```sql\nSELECT\n    quote_product,\n    COUNT(*)                           AS total_quotes,\n    ROUND(AVG(totalpremiumpayable), 2) AS avg_premium\nFROM my_iceberg_db.\"iceberg\".\"quotes\"\nGROUP BY quote_product\nORDER BY total_quotes DESC;\n```\n\n### High-Frequency Customers\n\n```sql\nSELECT\n    surname,\n    postcodedistrict,\n    COUNT(*)                           AS quote_count,\n    MIN(quotedate)                     AS first_quote,\n    MAX(quotedate)                     AS last_quote,\n    ROUND(AVG(totalpremiumpayable), 2) AS avg_premium\nFROM my_iceberg_db.\"iceberg\".\"quotes\"\nGROUP BY surname, postcodedistrict\nHAVING COUNT(*) \u003E 1\nORDER BY quote_count DESC\nLIMIT 20;\n```\n\n### Premium by Credit Score\n\n```sql\nSELECT\n    CASE\n        WHEN creditscore \u003E= 8 THEN 'High (8-10)'\n        WHEN creditscore \u003E= 5 THEN 'Medium (5-7)'\n        ELSE 'Low (0-4)'\n    END                                AS credit_band,\n    COUNT(*)                           AS quote_count,\n    ROUND(AVG(newriskpremium), 2)      AS avg_risk_premium,\n    ROUND(AVG(totalpremiumpayable), 2) AS avg_total_premium\nFROM my_iceberg_db.\"iceberg\".\"quotes\"\nGROUP BY credit_band\nORDER BY avg_risk_premium DESC;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Data Governance\n\nSnowflake Horizon governance policies apply natively to Iceberg tables in a Catalog-Linked Database — the data never needs to move into Snowflake storage for policies to take effect.\n\nIn this section you will create two roles with different data access levels, then apply dynamic data masking to PII columns in the `quotes` table. Analysts see partially masked data; data engineers see the full values.\n\n**How to run:** Continue in your existing Snowsight worksheet (or open a new one). Each subsection below has its own SQL block — run them in order from top to bottom. The role must be `ACCOUNTADMIN` at the start of each block; the SQL includes `USE ROLE ACCOUNTADMIN` where needed.\n\n### Create roles\n\nRun this block to create both roles, set up the role hierarchy, grant them to your user, and give them warehouse access:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE ROLE IF NOT EXISTS lab_data_engineer;\nCREATE ROLE IF NOT EXISTS lab_analyst;\n\n-- Hierarchy: analyst is a subset of data engineer\nGRANT ROLE lab_analyst TO ROLE lab_data_engineer;\n\n-- Grant both roles to your user\nGRANT ROLE lab_data_engineer TO USER IDENTIFIER(CURRENT_USER());\nGRANT ROLE lab_analyst TO USER IDENTIFIER(CURRENT_USER());\n\n-- Grant warehouse access so roles can run queries\n-- Replace COMPUTE_WH with your warehouse name if different\nGRANT USAGE ON WAREHOUSE COMPUTE_WH TO ROLE lab_data_engineer;\nGRANT USAGE ON WAREHOUSE COMPUTE_WH TO ROLE lab_analyst;\n```\n\n### Grant Access\n\nRun this block to give both roles access to the External Volume, Catalog Integration, and the Iceberg table in the CLD:\n\n```sql\n-- External volume and catalog integration\nGRANT USAGE ON EXTERNAL VOLUME my_iceberg_vol TO ROLE lab_data_engineer;\nGRANT USAGE ON INTEGRATION my_glue_int TO ROLE lab_data_engineer;\n\n-- Catalog-linked database\nGRANT USAGE ON DATABASE my_iceberg_db TO ROLE lab_data_engineer;\nGRANT USAGE ON DATABASE my_iceberg_db TO ROLE lab_analyst;\nGRANT USAGE ON SCHEMA my_iceberg_db.\"iceberg\" TO ROLE lab_data_engineer;\nGRANT USAGE ON SCHEMA my_iceberg_db.\"iceberg\" TO ROLE lab_analyst;\nGRANT SELECT ON ALL ICEBERG TABLES IN SCHEMA my_iceberg_db.\"iceberg\" TO ROLE lab_data_engineer;\nGRANT SELECT ON ALL ICEBERG TABLES IN SCHEMA my_iceberg_db.\"iceberg\" TO ROLE lab_analyst;\n```\n\n### View Wrapper\n\nBefore applying masking policies, create a thin view over the Iceberg table. Masking policies cannot be applied directly to tables in a Catalog-Linked Database — they must be attached to a view wrapper. This same view is also used by the Semantic View in the next section.\n\nRun this block to create the supporting database, schema, and view:\n\n```sql\nCREATE DATABASE IF NOT EXISTS iceberg_lab_db;\nCREATE SCHEMA IF NOT EXISTS iceberg_lab_db.analytics;\n\nCREATE OR REPLACE VIEW iceberg_lab_db.analytics.quotes_vw AS\nSELECT * FROM my_iceberg_db.\"iceberg\".\"quotes\";\n```\n\n### Masking Policies\n\nEach policy partially masks a PII column. `LAB_DATA_ENGINEER` and `ACCOUNTADMIN` see the real value — all other roles see a redacted version.\n\nRun this block to create all four masking policies:\n\n```sql\nUSE DATABASE iceberg_lab_db;\nUSE SCHEMA analytics;\n\n-- Email: john.doe@example.com → j***@***.com\nCREATE OR REPLACE MASKING POLICY mask_email\n  AS (val STRING) RETURNS STRING -\u003E\n    CASE\n      WHEN CURRENT_ROLE() IN ('LAB_DATA_ENGINEER', 'ACCOUNTADMIN') THEN val\n      ELSE CONCAT(LEFT(val, 1), '***@***.', SPLIT_PART(val, '.', -1))\n    END;\n\n-- Phone: 07123456789 → 071*****789\nCREATE OR REPLACE MASKING POLICY mask_phone\n  AS (val STRING) RETURNS STRING -\u003E\n    CASE\n      WHEN CURRENT_ROLE() IN ('LAB_DATA_ENGINEER', 'ACCOUNTADMIN') THEN val\n      ELSE CONCAT(LEFT(val, 3), REPEAT('*', LENGTH(val) - 6), RIGHT(val, 3))\n    END;\n\n-- Surname: Smith → S****\nCREATE OR REPLACE MASKING POLICY mask_surname\n  AS (val STRING) RETURNS STRING -\u003E\n    CASE\n      WHEN CURRENT_ROLE() IN ('LAB_DATA_ENGINEER', 'ACCOUNTADMIN') THEN val\n      ELSE CONCAT(LEFT(val, 1), REPEAT('*', GREATEST(LENGTH(val) - 1, 4)))\n    END;\n\n-- Date of birth: 1985-03-15 → ****-**-15\nCREATE OR REPLACE MASKING POLICY mask_dob\n  AS (val STRING) RETURNS STRING -\u003E\n    CASE\n      WHEN CURRENT_ROLE() IN ('LAB_DATA_ENGINEER', 'ACCOUNTADMIN') THEN val\n      ELSE CONCAT('****-**-', RIGHT(val, 2))\n    END;\n```\n\n### Apply Policies\n\nApply each masking policy to the corresponding column on the view wrapper. Masking is enforced whenever the view is queried — directly in SQL, via the semantic view, or through the Cortex Agent.\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nALTER VIEW iceberg_lab_db.analytics.quotes_vw\n  ALTER COLUMN email SET MASKING POLICY iceberg_lab_db.analytics.mask_email;\n\nALTER VIEW iceberg_lab_db.analytics.quotes_vw\n  ALTER COLUMN phonenumber SET MASKING POLICY iceberg_lab_db.analytics.mask_phone;\n\nALTER VIEW iceberg_lab_db.analytics.quotes_vw\n  ALTER COLUMN surname SET MASKING POLICY iceberg_lab_db.analytics.mask_surname;\n\nALTER VIEW iceberg_lab_db.analytics.quotes_vw\n  ALTER COLUMN dateofbirth SET MASKING POLICY iceberg_lab_db.analytics.mask_dob;\n```\n\n### Verify Masking\n\nSwitch to each role and run the same query to see the difference. You can switch roles two ways:\n- **In the SQL block:** run `USE ROLE lab_analyst;` before the SELECT (as shown below)\n- **In the Snowsight UI:** use the role picker at the top-left of the worksheet\n\nSwitch to the analyst role — PII is partially masked:\n\n```sql\nUSE ROLE lab_analyst;\n\nSELECT uuid, surname, email, phonenumber, dateofbirth, totalpremiumpayable\nFROM iceberg_lab_db.analytics.quotes_vw\nLIMIT 5;\n```\n\nSwitch to the data engineer role — full values visible:\n\n```sql\nUSE ROLE lab_data_engineer;\n\nSELECT uuid, surname, email, phonenumber, dateofbirth, totalpremiumpayable\nFROM iceberg_lab_db.analytics.quotes_vw\nLIMIT 5;\n```\n\n\u003E The same Iceberg data in AWS Glue, governed entirely by Snowflake — no data movement, no copies, no AWS-side policy changes needed.\n\n\u003C!-- ------------------------ --\u003E\n## Semantic View\n\nA **Semantic View** defines the business meaning of your data — dimensions, metrics, and facts — so that Snowflake's AI can understand and answer questions about it in natural language. The semantic view respects masking policies automatically: an analyst querying via natural language sees the same masked values as they would in SQL.\n\n**How to run:** Continue in your Snowsight worksheet with `ACCOUNTADMIN`. Run the blocks below in order.\n\n### View Wrapper\n\nThe `quotes_vw` view was created in the Data Governance section with masking policies already applied. Semantic views require a plain view reference in the same schema — this view serves that role here too.\n\nNo additional SQL needed. The view `iceberg_lab_db.analytics.quotes_vw` is ready to use.\n\n### Create View\n\nRun this block to create the semantic view. It defines the table mapping, facts, dimensions, and metrics that Cortex Analyst will use to translate natural language into SQL:\n\n```sql\nUSE DATABASE iceberg_lab_db;\nUSE SCHEMA analytics;\n\nCREATE OR REPLACE SEMANTIC VIEW quotes_sv\n  TABLES (\n    quotes as quotes_vw\n      primary key (uuid)\n      comment='Insurance quote requests from the Iceberg data lake in AWS Glue'\n  )\n  FACTS (\n    quotes.newriskpremium      as newriskpremium      comment='Calculated risk premium',\n    quotes.totalpremiumpayable as totalpremiumpayable comment='Total premium payable',\n    quotes.iptamount           as iptamount           comment='Insurance premium tax',\n    quotes.quote_record        as 1                   comment='One record per quote'\n  )\n  DIMENSIONS (\n    quotes.quote_product    as quote_product    with synonyms=('product type', 'insurance product', 'cover type') comment='Insurance product type',\n    quotes.quotedate        as quotedate        with synonyms=('quote date', 'date', 'when requested')             comment='Date the quote was requested',\n    quotes.maritalstatus    as maritalstatus    with synonyms=('marital status', 'married', 'single')              comment='Customer marital status',\n    quotes.homeownerind     as homeownerind     with synonyms=('homeowner', 'owns home', 'property owner')         comment='Whether customer owns home',\n    quotes.sex              as sex              with synonyms=('gender', 'customer gender')                         comment='Customer gender',\n    quotes.postcodedistrict as postcodedistrict with synonyms=('district', 'location', 'area', 'region')           comment='Customer postcode district',\n    quotes.previnsr         as previnsr         with synonyms=('previous insurer', 'prior insurer')                 comment='Previous insurance provider'\n  )\n  METRICS (\n    quotes.total_quotes         as COUNT(quotes.quote_record)      comment='Total number of quote requests',\n    quotes.avg_total_premium    as AVG(quotes.totalpremiumpayable)  comment='Average total premium payable',\n    quotes.avg_risk_premium     as AVG(quotes.newriskpremium)       comment='Average risk premium',\n    quotes.total_premium_volume as SUM(quotes.totalpremiumpayable)  comment='Total premium volume'\n  )\n  comment='Insurance quote analytics — Iceberg data in AWS Glue via Catalog-Linked Database';\n```\n\n\u003E **Note:** The semantic view uses a `quotes as quotes_vw` mapping where `quotes` is the internal reference name and `quotes_vw` is the physical view. Column references in FACTS, DIMENSIONS, and METRICS use the reference name `quotes`.\n\n### Grant Access\n\n```sql\nGRANT USAGE ON DATABASE iceberg_lab_db TO ROLE lab_analyst;\nGRANT USAGE ON SCHEMA iceberg_lab_db.analytics TO ROLE lab_analyst;\nGRANT SELECT ON VIEW iceberg_lab_db.analytics.quotes_vw TO ROLE lab_analyst;\nGRANT SELECT ON SEMANTIC VIEW iceberg_lab_db.analytics.quotes_sv TO ROLE lab_analyst;\nGRANT USAGE ON DATABASE iceberg_lab_db TO ROLE lab_data_engineer;\nGRANT USAGE ON SCHEMA iceberg_lab_db.analytics TO ROLE lab_data_engineer;\nGRANT SELECT ON VIEW iceberg_lab_db.analytics.quotes_vw TO ROLE lab_data_engineer;\nGRANT SELECT ON SEMANTIC VIEW iceberg_lab_db.analytics.quotes_sv TO ROLE lab_data_engineer;\n```\n\nVerify the semantic view:\n\n```sql\nSHOW SEMANTIC VIEWS IN SCHEMA iceberg_lab_db.analytics;\nSHOW SEMANTIC METRICS IN iceberg_lab_db.analytics.quotes_sv;\nSHOW SEMANTIC DIMENSIONS IN iceberg_lab_db.analytics.quotes_sv;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Natural Language Queries\n\nA **Cortex Agent** wraps the semantic view and exposes it as a natural language interface. Snowflake translates plain English questions into SQL against your Iceberg data — masking policies are enforced automatically based on the querying role.\n\n**How to run:** Run the SQL blocks below in your Snowsight worksheet with `ACCOUNTADMIN`. After creating the agent, open it directly in Snowsight (no SQL needed) to ask questions.\n\n### Create the agent\n\nRun this block to create the agent backed by your semantic view:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nUSE DATABASE iceberg_lab_db;\nUSE SCHEMA analytics;\n\nCREATE OR REPLACE AGENT iceberg_lab_db.analytics.quotes_agent\n  WITH PROFILE = '{\"display_name\": \"Insurance Quotes Analyst\"}'\n  COMMENT = 'Natural language analytics on Iceberg insurance quotes data in AWS Glue'\nFROM SPECIFICATION $$\n{\n  \"instructions\": {\n    \"response\": \"Answer questions about insurance quote data. Use the quotes tool to query premium amounts, product types, homeowner status, marital status, and postcode district. Summarize results clearly and suggest follow-up questions.\"\n  },\n  \"tools\": [\n    {\n      \"tool_spec\": {\n        \"type\": \"cortex_analyst_text_to_sql\",\n        \"name\": \"Query Insurance Quotes\",\n        \"description\": \"Query insurance quote data including premiums, product types, homeowner status, marital status, and postcode district.\"\n      }\n    }\n  ],\n  \"tool_resources\": {\n    \"Query Insurance Quotes\": {\n      \"semantic_view\": \"iceberg_lab_db.analytics.quotes_sv\"\n    }\n  }\n}\n$$;\n```\n\n### Snowflake CoWork\n\nTo make the agent accessible via [Snowflake CoWork](https://docs.snowflake.com/en/user-guide/snowflake-intelligence), grant the Snowflake service role access to the underlying objects:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nGRANT USAGE ON DATABASE iceberg_lab_db TO ROLE SNOWFLAKE;\nGRANT USAGE ON SCHEMA iceberg_lab_db.analytics TO ROLE SNOWFLAKE;\nGRANT SELECT ON VIEW iceberg_lab_db.analytics.quotes_vw TO ROLE SNOWFLAKE;\nGRANT SELECT ON SEMANTIC VIEW iceberg_lab_db.analytics.quotes_sv TO ROLE SNOWFLAKE;\n```\n\n\u003E **Note:** The `SNOWFLAKE` service role is available in Snowflake Enterprise and Business Critical accounts. Trial accounts do not include this role — if you receive a \"Role does not exist\" error, skip this step. The agent is still fully accessible via **AI & ML \u003E Agents** in Snowsight.\n\n### Ask Questions\n\nTo open the agent in Snowsight:\n\n1. In the left nav, click **AI & ML → Agents**\n2. Find **Insurance Quotes Analyst** in the list and click **Open**\n3. Type a question in the chat input and press Enter\n4. The agent translates your question into SQL against `quotes_sv` and returns the result\n5. To test masking enforcement: change your active role (top-left role picker) to `lab_analyst` or `lab_data_engineer` and ask the same question — PII fields will be masked or unmasked based on your role\n\nThe agent is also available in Snowflake CoWork (enterprise accounts only).\n\nTry these questions:\n\n```text\nWhat are the top 5 insurance products by number of quotes?\n```\n\n```text\nWhat is the average premium for homeowners vs non-homeowners?\n```\n\n```text\nWhich postcode district has the highest average risk premium?\n```\n\n```text\nShow me quote volume broken down by marital status and product type.\n```\n\n```text\nWhat is the total premium volume by previous insurer?\n```\n\n\u003E Switch between the `lab_analyst` and `lab_data_engineer` roles to see how the same natural language question returns different results — masking policies are enforced end-to-end, from Iceberg through the semantic view to the agent response.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations! You've successfully connected Snowflake to an Apache Iceberg data lake in AWS Glue — querying, governing, and analyzing the data entirely from Snowflake without moving a single byte.\n\nIn this lab you built a complete open lakehouse analytics stack:\n- Connected Snowflake to AWS Glue via a Catalog-Linked Database using the Iceberg REST Catalog API\n- Applied Snowflake Horizon governance — RBAC and dynamic data masking — to Iceberg tables in AWS\n- Created a Semantic View to define business metrics and dimensions on your Iceberg data\n- Built a Cortex Agent for natural language analytics, with masking enforced end-to-end\n\n### What You Learned\n- How External Volumes, Catalog Integrations, and Catalog-Linked Databases work together\n- How Snowflake Horizon governance applies natively to external Iceberg tables\n- How Semantic Views bridge raw Iceberg data and AI-powered analytics\n- How Cortex Agents translate natural language into governed SQL against your data lake\n\n### Related Resources\n\nDocumentation:\n- [Apache Iceberg tables in Snowflake](https://docs.snowflake.com/en/user-guide/tables-iceberg)\n- [Catalog-Linked Databases](https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database)\n- [Configure a catalog integration for AWS Glue Iceberg REST](https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-catalog-integration-rest-glue)\n- [Dynamic Data Masking](https://docs.snowflake.com/en/user-guide/security-column-ddm-intro)\n- [Semantic Views](https://docs.snowflake.com/en/user-guide/views-semantic)\n- [Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agent)\n\nRelated guides:\n- [Build Data Lakes using Apache Iceberg with Snowflake and AWS Glue](https://quickstarts.snowflake.com/guide/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue) — Go deeper on the full AWS setup: CloudFormation, Lake Formation credential vending, and the complete Glue IRC integration with Cortex Code fast path\n- [Get Started with Snowflake-Managed Iceberg Tables](https://quickstarts.snowflake.com/guide/get-started-snowflake-managed-iceberg-tables) — Create Snowflake-managed Iceberg tables, stream and transform fleet data, query with Snowflake CoWork, and read the same tables from DuckDB and Apache Spark\n- [Iceberg V3 Tables Comprehensive Guide](https://quickstarts.snowflake.com/guide/iceberg-v3-tables-comprehensive-guide) — Build an end-to-end enterprise lakehouse platform using Iceberg V3 tables with streaming, variant data, time-series, geospatial analytics, governance, and AI\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-d87c3da143","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-5267f3d67a",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-8791cd1672","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-02",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-13891fd16a","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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