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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://iceberg.apache.org/\"\u003EApache Iceberg\u003C/a\u003E is an open table format for huge analytical datasets that enables high performance analytics on open data formats with ACID compliance. Snowflake and AWS both support Iceberg format that enables customers to drastically improve data interoperability, speed of implementation and performance for integrated data lakes.\u003C/p\u003E\n","\u003Cp\u003EThis guide will take you through the steps of converting existing parquet data to Iceberg and using it to build open analytic environments using Snowflake and \u003Ca href=\"https://aws.amazon.com/glue/\"\u003EAWS Glue\u003C/a\u003E with \u003Ca href=\"https://aws.amazon.com/lake-formation/\"\u003EAWS Lake Formation\u003C/a\u003E providing fine-grained access controls and \u003Ca href=\"https://docs.aws.amazon.com/lake-formation/latest/dg/aws-lake-formation-api-credential-vending.html\"\u003Etemporary access token\u003C/a\u003E to Iceberg tables.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/Workflow.png?v=f3d82d45\" alt=\"Workflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor this guide we will use a Financial Services use case where Insurance data is analyzed. The Quotes data is collected from systems and stored as parquet on S3, while Customer and Policy data is already available as internal Snowflake tables. We will try to identify customers who are likely to churn or potential fraud with a high number of recent quote requests.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Snowflake, basic SQL, and the Snowsight UI\u003C/li\u003E\u003Cli\u003EFamiliarity with AWS services — S3, Glue, Lake Formation, IAM, and CloudFormation\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to convert Parquet files on S3 to Apache Iceberg format using an \u003Ca href=\"https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-format-iceberg.html\"\u003EAWS Glue ETL job\u003C/a\u003E — no data rewrite, in-place conversion\u003C/li\u003E\u003Cli\u003EHow to configure \u003Ca href=\"https://aws.amazon.com/lake-formation/\"\u003EAWS Lake Formation\u003C/a\u003E for fine-grained access control on Iceberg tables using credential vending\u003C/li\u003E\u003Cli\u003EHow to create a Snowflake \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-catalog-integration-rest-glue\"\u003ECatalog Integration\u003C/a\u003E that connects to AWS Glue via the Iceberg REST Catalog (IRC) API using SIGV4 authentication and Lake Formation vended credentials\u003C/li\u003E\u003Cli\u003EHow to create a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database\"\u003ECatalog-Linked Database\u003C/a\u003E in Snowflake to auto-discover and sync all Iceberg tables from the Glue Data Catalog\u003C/li\u003E\u003Cli\u003EHow to read from, write to, and create Iceberg tables in Snowflake via the Catalog-Linked Database\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/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=build_datalake_with_glue_and_iceberg\"\u003ESnowflake Enterprise Account\u003C/a\u003E with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E access, deployed in \u003Cstrong\u003EAWS US West 2 (Oregon)\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EAn \u003Ca href=\"https://aws.amazon.com/free/\"\u003EAWS Account\u003C/a\u003E with Administrator access\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn AWS Glue Data Catalog database with Apache Iceberg tables backed by S3\u003C/li\u003E\u003Cli\u003EAWS Lake Formation permissions with credential vending for fine-grained Iceberg access control\u003C/li\u003E\u003Cli\u003EA Snowflake External Volume, Catalog Integration, and Catalog-Linked Database connected to the Glue catalog\u003C/li\u003E\u003Cli\u003EAn end-to-end read/write workflow: query, create, and write Iceberg tables from Snowflake using the Catalog-Linked Database\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up with Snowflake CoCo (Fast Path)\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ESnowflake CoCo\u003C/a\u003E is Snowflake's AI coding agent, built into the Snowflake CLI. You can use it to automate the entire setup in this guide — from deploying the AWS infrastructure to creating the Snowflake catalog integration and Catalog-Linked Database.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrerequisites for this path:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAWS CLI installed and configured with Administrator access (\u003Ccode\u003Eaws configure\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation\"\u003ESnowflake CLI\u003C/a\u003E installed and connected to your Snowflake account\u003C/li\u003E\u003Cli\u003ESnowflake Enterprise account with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E access in \u003Cstrong\u003EAWS us-west-2\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EUse the prompts below in sequence. Each one picks up where the previous left off.\u003C/p\u003E\n","\u003Cp\u003EFor the Snowflake-side steps, Snowflake CoCo automatically loads the right skill based on your prompt — you don't need to invoke anything manually.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. Deploy the AWS infrastructure (CloudFormation):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EDeploy this CloudFormation template in us-west-2 and show me the stack outputs when complete — stack name: Glue-IRC-Int, template: https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Iceberg_SNOW_AWS/setup/glue-snflk-devday-v1.5.yaml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003ECoCo runs this via AWS CLI — no specific skill, uses your \u003Ccode\u003Eaws configure\u003C/code\u003E credentials directly.\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2. Create the Glue Data Catalog database and Iceberg table:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate a Glue Data Catalog database named 'iceberg' in us-west-2, then create a Glue ETL job that reads Parquet files from s3://aws-data-analytics-workshops/aws_glue/glue-snowflake/raw-data/quotes/ and writes them as Apache Iceberg to s3://<your-bucket>/iceberg/quotes/ — use the IAM role from the CloudFormation outputs and register the table in the 'iceberg' database\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003ECoCo uses AWS CLI and boto3 to create the Glue database, table definition, and ETL job script.\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3. Configure Lake Formation for credential vending:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EConfigure Lake Formation in us-west-2 to enable credential vending for the Iceberg tables in the 'iceberg' Glue database: enable external engine access, register the S3 bucket, and grant the CloudFormation IAM role SUPER permissions on the iceberg database\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003ECoCo runs \u003Ccode\u003Eaws lakeformation\u003C/code\u003E CLI commands — no specific skill.\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4. Create the Snowflake catalog integration:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EHelp me create a catalog integration for AWS Glue Iceberg REST with vended credentials\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003ECoCo auto-invokes the \u003Cstrong\u003E\u003Ccode\u003Eglueirc-catalog-integration-setup\u003C/code\u003E\u003C/strong\u003E skill — collects your AWS account ID, region, IAM role ARN, and access delegation preference, generates and executes the SQL, then guides you through the trust policy update.\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E5. Create the Catalog-Linked Database:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate a catalog-linked database from my Glue catalog integration\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003ECoCo auto-invokes the \u003Cstrong\u003E\u003Ccode\u003Ecatalog-linked-database\u003C/code\u003E\u003C/strong\u003E skill — creates the CLD, verifies the connection, and lists the auto-discovered Iceberg tables from the Glue catalog.\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce Snowflake CoCo completes step 5, your Snowflake environment is connected to the Glue catalog and ready to query. Skip directly to the \u003Ca href=\"#query-iceberg-tables\"\u003EQuery Iceberg Tables\u003C/a\u003E section.\u003C/p\u003E\n\u003Chr\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHave your own S3 data instead of the sample dataset?\u003C/strong\u003E Use \u003Ccode\u003EHelp me set up my S3 data as Glue Iceberg tables\u003C/code\u003E — CoCo auto-invokes the \u003Cstrong\u003E\u003Ccode\u003Eaws-setup\u003C/code\u003E\u003C/strong\u003E workflow to walk you through S3 discovery, Glue crawler setup, schema validation, and Parquet-to-Iceberg conversion. Pick up at step 4 above once your Glue tables are registered.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrefer to follow the steps manually?\u003C/strong\u003E Continue to the next section for the full console walkthrough.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfigure the AWS Account\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsed the Snowflake CoCo fast path?\u003C/strong\u003E Skip this section and the next two — go directly to \u003Ca href=\"#setup-snowflake-account-and-configure-the-aws-integrations\"\u003ESetup Snowflake account and configure the AWS integrations\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn this step you'll use a CloudFormation template to provision the AWS resources needed for this quickstart: an S3 bucket and an IAM role with the policies that Glue needs to access it.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELog in to your \u003Ca href=\"https://console.aws.amazon.com/\"\u003EAWS Console\u003C/a\u003E and confirm the region in the top-right corner is \u003Cstrong\u003EUS West (Oregon) us-west-2\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AWSRegion.png?v=f3d82d45\" alt=\"AWSRegion\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EDeploy the CloudFormation stack by clicking \u003Ca href=\"https://console.aws.amazon.com/cloudformation/home#/stacks/new?stackName=Glue-IRC-Int&templateURL=https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Iceberg_SNOW_AWS/setup/glue-snflk-devday-v1.5.yaml\"\u003Ehere\u003C/a\u003E. This creates the S3 bucket and IAM role used throughout the guide.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/CreateStack.png?v=f3d82d45\" alt=\"CreateStack\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E on the \u003Cstrong\u003ECreate stack\u003C/strong\u003E screen.\u003C/li\u003E\u003Cli\u003EOn \u003Cstrong\u003ESpecify stack details\u003C/strong\u003E, leave the default stack name \u003Ccode\u003EGlue-IRC-Int\u003C/code\u003E and click \u003Cstrong\u003ENext\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EOn \u003Cstrong\u003EConfigure stack options\u003C/strong\u003E, scroll to the bottom, check \u003Ccode\u003EI acknowledge that AWS CloudFormation might create IAM resources\u003C/code\u003E, and click \u003Cstrong\u003ENext\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EOn \u003Cstrong\u003EReview and create\u003C/strong\u003E, click \u003Cstrong\u003ESubmit\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EStack creation takes about a minute. Use the \u003Cstrong\u003EEvents\u003C/strong\u003E tab and the refresh button to monitor progress.\u003C/p\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003EOnce the stack status shows \u003Cstrong\u003ECREATE_COMPLETE\u003C/strong\u003E, click the \u003Cstrong\u003EOutputs\u003C/strong\u003E tab. Copy the \u003Cstrong\u003ERole ARN\u003C/strong\u003E and \u003Cstrong\u003ES3 bucket name\u003C/strong\u003E — you'll need both in later steps.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/CFoutput.png?v=f3d82d45\" alt=\"CFoutput\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUse AWS Glue to create the Iceberg table\u003C/h2\u003E\n","\u003Cp\u003EIn this step you'll create a Glue Data Catalog database and table, then run a Glue ETL job to convert raw Parquet data on S3 to Apache Iceberg format.\u003C/p\u003E\n","\u003Ch3\u003ECreate a Glue Database\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EIn the AWS Console, navigate to \u003Cstrong\u003EAWS Glue\u003C/strong\u003E and confirm the region is \u003Cstrong\u003EUS West (Oregon) us-west-2\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AWSRegion.png?v=f3d82d45\" alt=\"AWSRegion\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EIn the left navigation pane under \u003Cstrong\u003EData Catalog\u003C/strong\u003E, click \u003Cstrong\u003EDatabases\u003C/strong\u003E, then click \u003Cstrong\u003EAdd database\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AddDatabase.png?v=f3d82d45\" alt=\"AddDatabase\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EName the database \u003Ccode\u003Eiceberg\u003C/code\u003E and click \u003Cstrong\u003ECreate database\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/CreateDatabase.png?v=f3d82d45\" alt=\"CreateDatabase\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate a Glue Table\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EIn the left menu, click \u003Cstrong\u003EData Catalog > Databases > Tables\u003C/strong\u003E, then click \u003Cstrong\u003EAdd table\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AddTable.png?v=f3d82d45\" alt=\"AddTable\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003ESet the following table properties and click \u003Cstrong\u003ENext\u003C/strong\u003E:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003Equotes\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase:\u003C/strong\u003E \u003Ccode\u003Eiceberg\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETable format:\u003C/strong\u003E Apache Iceberg table\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIAM role:\u003C/strong\u003E Select the role from the CloudFormation Outputs (e.g. \u003Ccode\u003EGlue-IRC-Int-GlueSnowflakeLabRole-xxxx\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003ECheck the acknowledgment checkbox\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData location:\u003C/strong\u003E \u003Ccode\u003Es3://glue-snowflake-lab-xxxx/iceberg/quotes/\u003C/code\u003E — replace \u003Ccode\u003Eglue-snowflake-lab-xxxx\u003C/code\u003E with your S3 bucket name from the CloudFormation Outputs\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/TableProp2.png?v=f3d82d45\" alt=\"TableProp\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EOn the schema screen, click \u003Cstrong\u003EEdit schema as JSON\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/Schema.png?v=f3d82d45\" alt=\"Schema\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EDownload \u003Ca href=\"https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Iceberg_SNOW_AWS/setup/quotes_schema.json\"\u003Equotes_schema.json\u003C/a\u003E. Clear the \u003Ccode\u003E[ ]\u003C/code\u003E placeholder, paste in the file contents, and click \u003Cstrong\u003ESave\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/delete.png?v=f3d82d45\" alt=\"delete\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/SaveSchema.png?v=f3d82d45\" alt=\"SaveSchema\"\u003E\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003EVerify the schema looks correct and click \u003Cstrong\u003ENext\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/SchemaNext.png?v=f3d82d45\" alt=\"SchemaNext\"\u003E\u003C/p\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate\u003C/strong\u003E to finish creating the table.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ECreate and Run the Glue ETL Job\u003C/h3\u003E\n","\u003Cp\u003EThis job reads raw Parquet quotes data from a public S3 bucket and writes it to your S3 bucket as an Iceberg table — no data copy or rewrite. The Glue Data Catalog table you just created is updated in place.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the left menu under \u003Cstrong\u003EETL jobs\u003C/strong\u003E, click \u003Cstrong\u003EVisual ETL\u003C/strong\u003E, then click the \u003Cstrong\u003EVisual ETL\u003C/strong\u003E button.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/ETLJob.png?v=f3d82d45\" alt=\"ETLJob\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003ERename the job to \u003Ccode\u003EQuotes - Raw to Iceberg\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EUnder the \u003Cstrong\u003EJob details\u003C/strong\u003E tab, select the IAM role from the CloudFormation Outputs.\u003C/li\u003E\u003Cli\u003EOptionally reduce \u003Cstrong\u003ERequested number of workers\u003C/strong\u003E to 2.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/ETLJobConfig.png?v=f3d82d45\" alt=\"ETLJobConfig\"\u003E\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave\u003C/strong\u003E. A green banner confirms the job is saved.\u003C/li\u003E\u003Cli\u003EOn the \u003Cstrong\u003EVisual\u003C/strong\u003E tab, click the \u003Cstrong\u003E+\u003C/strong\u003E button and add an \u003Cstrong\u003ES3\u003C/strong\u003E source node. Configure it:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003EQuotes - Raw\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ES3 URL:\u003C/strong\u003E \u003Ccode\u003Es3://aws-data-analytics-workshops/aws_glue/glue-snowflake/raw-data/quotes/\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData format:\u003C/strong\u003E Parquet\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/QuotesRaw.png?v=f3d82d45\" alt=\"QuotesRaw\"\u003E\u003C/p\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003EWith the \u003Cstrong\u003EQuotes - Raw\u003C/strong\u003E node selected, click \u003Cstrong\u003E+\u003C/strong\u003E and add a \u003Cstrong\u003ETransform - Change Schema\u003C/strong\u003E node.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/Transform.png?v=f3d82d45\" alt=\"Transform\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/ChangeSchema.png?v=f3d82d45\" alt=\"ChangeSchema\"\u003E\u003C/p\u003E\n\u003Col start=\"8\"\u003E\u003Cli\u003EIn \u003Cstrong\u003EChange Schema\u003C/strong\u003E, change the data type of \u003Ccode\u003Euuid\u003C/code\u003E to \u003Cstrong\u003Evarchar\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/UpdateSchema.png?v=f3d82d45\" alt=\"UpdateSchema\"\u003E\u003C/p\u003E\n\u003Col start=\"9\"\u003E\u003Cli\u003EWith \u003Cstrong\u003EChange Schema\u003C/strong\u003E selected, click \u003Cstrong\u003E+\u003C/strong\u003E and add an \u003Cstrong\u003EAmazon S3\u003C/strong\u003E target node from the \u003Cstrong\u003ETargets\u003C/strong\u003E tab. Configure it:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003EQuotes - Iceberg\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFormat:\u003C/strong\u003E Apache Iceberg\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECompression Type:\u003C/strong\u003E Snappy\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ES3 Target Location:\u003C/strong\u003E \u003Ccode\u003Es3://glue-snowflake-lab-xxxx/iceberg/quotes/\u003C/code\u003E — replace \u003Ccode\u003Eglue-snowflake-lab-xxxx\u003C/code\u003E with your bucket name\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData Catalog update options:\u003C/strong\u003E Create a table in the Data Catalog and on subsequent runs, keep existing schema and add new partitions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase:\u003C/strong\u003E \u003Ccode\u003Eiceberg\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETable name:\u003C/strong\u003E \u003Ccode\u003Equotes\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/TargetOptions.png?v=f3d82d45\" alt=\"TargetOptions\"\u003E\u003C/p\u003E\n\u003Col start=\"10\"\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave\u003C/strong\u003E, then click \u003Cstrong\u003ERun\u003C/strong\u003E. Navigate to the \u003Cstrong\u003ERuns\u003C/strong\u003E tab to monitor progress.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/RunETL.png?v=f3d82d45\" alt=\"RunETL\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce the job status shows \u003Cstrong\u003ESucceeded\u003C/strong\u003E, the Iceberg table is live in your Glue Data Catalog.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/ETLComplete.png?v=f3d82d45\" alt=\"RETLComplete\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs a bonus step, open the S3 console and browse your bucket — you'll see the Iceberg data and metadata/manifest files Glue created under \u003Ccode\u003Eiceberg/quotes/\u003C/code\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfigure AWS Lake Formation\u003C/h2\u003E\n","\u003Cp\u003EIn this step you'll configure AWS Lake Formation to grant fine-grained access to the Iceberg table and enable credential vending. Credential vending allows Snowflake to request temporary S3 credentials from Lake Formation at query time — no external volume configuration needed on the Snowflake side.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Enable external engine access\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ESign in to the \u003Ca href=\"https://console.aws.amazon.com/lakeformation/\"\u003ELake Formation console\u003C/a\u003E as a data lake administrator.\u003C/li\u003E\u003Cli\u003EIn the left navigation pane, choose \u003Cstrong\u003EAdministration > Application integration settings\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EEnable \u003Cstrong\u003EAllow external engines to access data in Amazon S3 locations with full table access\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 2: Grant data location permissions\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EUnder \u003Cstrong\u003EPermissions\u003C/strong\u003E, select \u003Cstrong\u003EData locations\u003C/strong\u003E and click \u003Cstrong\u003EGrant\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003EIAM users and roles\u003C/strong\u003E, select the role created by CloudFormation.\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003EStorage locations\u003C/strong\u003E, click \u003Cstrong\u003EBrowse\u003C/strong\u003E and select your S3 bucket.\u003C/li\u003E\u003Cli\u003ECheck \u003Cstrong\u003EGrantable\u003C/strong\u003E and click \u003Cstrong\u003EGrant\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 3: Register the data lake location\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EUnder \u003Cstrong\u003EAdministration\u003C/strong\u003E, select \u003Cstrong\u003EData lake locations\u003C/strong\u003E and click \u003Cstrong\u003ERegister location\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003EAmazon S3 path\u003C/strong\u003E, enter the S3 bucket path from the CloudFormation Outputs.\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003EIAM role\u003C/strong\u003E, select the role created by CloudFormation.\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003EPermission mode\u003C/strong\u003E, select \u003Cstrong\u003ELake Formation\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ERegister location\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 4: Grant table permissions to the IAM role\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EUnder \u003Cstrong\u003EPermissions\u003C/strong\u003E, choose \u003Cstrong\u003EData permissions\u003C/strong\u003E and click \u003Cstrong\u003EGrant\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EConfigure the following:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPrincipals:\u003C/strong\u003E IAM users and roles → select the role created by CloudFormation\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EResources:\u003C/strong\u003E Named Data Catalog resources\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECatalog:\u003C/strong\u003E your AWS account ID\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase:\u003C/strong\u003E \u003Ccode\u003Eiceberg\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase permissions:\u003C/strong\u003E SUPER\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGrantable permissions:\u003C/strong\u003E SUPER\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EGrant\u003C/strong\u003E and confirm the permission appears in the list.\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E SUPER permission is required for Snowflake to mount the Iceberg table via the Catalog-Linked Database using credential vending.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EYour AWS setup is complete. Continue to the next step to configure Snowflake.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Snowflake account and configure the AWS integrations\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsed the Snowflake CoCo fast path?\u003C/strong\u003E Skip this section — go directly to \u003Ca href=\"#working-with-iceberg-tables-in-snowflake\"\u003EWorking with Iceberg tables in Snowflake\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn this step you'll create the Snowflake role, database, warehouse, and internal tables used in the lab, then create the Catalog Integration and Catalog-Linked Database that connect Snowflake to the Glue Data Catalog.\u003C/p\u003E\n","\u003Ch3\u003EConfigure your Snowflake account\u003C/h3\u003E\n","\u003Cp\u003EDownload the two SQL files for this lab:\u003C/p\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Iceberg_SNOW_AWS/setup/hol_ice_setup_v1.2.sql\"\u003ESetup SQL\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Iceberg_SNOW_AWS/setup/hol_ice_workflow_v1.6.sql\"\u003EWorkflow SQL\u003C/a\u003E\n</button>\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELog into your Snowflake account as a user with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E privileges.\u003C/li\u003E\u003Cli\u003EIn the left menu, go to \u003Cstrong\u003EProjects > Workspaces\u003C/strong\u003E, click \u003Cstrong\u003E+ Add new\u003C/strong\u003E, and select \u003Cstrong\u003EUpload Files\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AddNewFiles.png?v=f3d82d45\" alt=\"AddNewFiles\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EUpload both SQL files. They'll appear under \u003Cstrong\u003EMy Workspace\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EOpen \u003Ccode\u003Ehol_ice_setup_vxx.sql\u003C/code\u003E. Run the following sections in order by selecting each block and clicking \u003Cstrong\u003ERun\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/run2.png?v=f3d82d45\" alt=\"run2\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECreate the role and grant account-level privileges:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE SECURITYADMIN;\n\nCREATE OR REPLACE ROLE HOL_ICE_RL COMMENT='Iceberg Role';\nGRANT ROLE HOL_ICE_RL TO ROLE SYSADMIN;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nGRANT CREATE INTEGRATION ON ACCOUNT TO ROLE HOL_ICE_RL;\nGRANT CREATE EXTERNAL VOLUME ON ACCOUNT TO ROLE HOL_ICE_RL;\nGRANT CREATE DATABASE ON ACCOUNT TO ROLE HOL_ICE_RL;\nGRANT CREATE WAREHOUSE ON ACCOUNT TO ROLE HOL_ICE_RL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate the database and warehouse:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE HOL_ICE_RL;\n\nCREATE OR REPLACE DATABASE HOL_ICE_DB;\n\nCREATE OR REPLACE WAREHOUSE HOL_ICE_WH\n  WITH WAREHOUSE_SIZE = 'XSMALL'\n  INITIALLY_SUSPENDED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ESelect lines 44–110 and run them to create the internal \u003Ccode\u003ECUSTOMER\u003C/code\u003E and \u003Ccode\u003EPOLICIES\u003C/code\u003E tables and load data.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EConfigure the Snowflake Catalog Integration with Glue\u003C/h3\u003E\n","\u003Cp\u003EOpen \u003Ccode\u003Ehol_ice_workflow_vxx.sql\u003C/code\u003E from \u003Cstrong\u003EMy Workspace\u003C/strong\u003E. Set the worksheet context (lines 16–20):\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/WorkflowUI.png?v=f3d82d45\" alt=\"WorkflowUI\"\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE HOL_ICE_RL;\n\nUSE HOL_ICE_DB.PUBLIC;\n\nUSE WAREHOUSE HOL_ICE_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou'll need two values from your AWS environment:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAWS Account ID\u003C/strong\u003E — from the top-right corner of the AWS Console\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AWSAccountID2.png?v=f3d82d45\" alt=\"accountID2\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EIAM Role ARN\u003C/strong\u003E — from the CloudFormation Outputs tab\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E This guide uses \u003Ccode\u003ECATALOG_SOURCE = ICEBERG_REST\u003C/code\u003E with \u003Ccode\u003ECATALOG_API_TYPE = AWS_GLUE\u003C/code\u003E — the current recommended approach for connecting to the Glue Data Catalog via the Iceberg REST API. If you have an older integration using \u003Ccode\u003ECATALOG_SOURCE = GLUE\u003C/code\u003E, migrate to this approach to get access to Catalog-Linked Databases, vended credentials, and full IRC feature support.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EReplace the placeholders and run the \u003Ccode\u003ECREATE CATALOG INTEGRATION\u003C/code\u003E statement (lines 37–53):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CATALOG INTEGRATION glue_catalog_irc_int\n  CATALOG_SOURCE = ICEBERG_REST\n  TABLE_FORMAT = ICEBERG\n  CATALOG_NAMESPACE = 'iceberg'\n  REST_CONFIG = (\n    CATALOG_URI = 'https://glue.us-west-2.amazonaws.com/iceberg'\n    CATALOG_API_TYPE = AWS_GLUE\n    CATALOG_NAME = '<your-aws-account-id>'\n    ACCESS_DELEGATION_MODE = VENDED_CREDENTIALS\n  )\n  REST_AUTHENTICATION = (\n    TYPE = SIGV4\n    SIGV4_IAM_ROLE = 'arn:aws:iam::<your-aws-account-id>:role/<your-cloudformation-role-name>'\n    SIGV4_SIGNING_REGION = 'us-west-2'\n    SIGV4_EXTERNAL_ID = 'snow-glue-ext-id'\n  )\n  ENABLED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003ESIGV4_EXTERNAL_ID\u003C/code\u003E\u003C/strong\u003E: This is a custom value you set — it ties to the trust policy in the next step. \u003Ccode\u003Esnow-glue-ext-id\u003C/code\u003E is the value used in this lab. You can use any string, or omit it to have Snowflake auto-generate one (retrieve the generated value via \u003Ccode\u003EDESC CATALOG INTEGRATION\u003C/code\u003E afterward).\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ERun \u003Ccode\u003EDESC CATALOG INTEGRATION\u003C/code\u003E to retrieve the value needed for the IAM trust policy:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDESC CATALOG INTEGRATION glue_catalog_irc_int;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote the \u003Cstrong\u003E\u003Ccode\u003EGLUE_AWS_IAM_USER_ARN\u003C/code\u003E\u003C/strong\u003E value from the output — you'll paste it into the trust policy next.\u003C/p\u003E\n","\u003Ch3\u003EUpdate the IAM trust policy\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EIn the AWS Console, navigate to \u003Cstrong\u003EIAM > Roles\u003C/strong\u003E and open the CloudFormation role.\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003ETrust relationships\u003C/strong\u003E tab, then \u003Cstrong\u003EEdit trust policy\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/TrustPolicy2.png?v=f3d82d45\" alt=\"TrustPolicy2\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EReplace the entire policy with the JSON below, substituting \u003Ccode\u003EGLUE_AWS_IAM_USER_ARN\u003C/code\u003E from the Snowflake output:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "Version": "2012-10-17",\n  "Statement": [\n    {\n      "Effect": "Allow",\n      "Principal": {\n        "Service": [\n          "lakeformation.amazonaws.com",\n          "glue.amazonaws.com"\n        ]\n      },\n      "Action": "sts:AssumeRole"\n    },\n    {\n      "Effect": "Allow",\n      "Principal": {\n        "AWS": "<GLUE_AWS_IAM_USER_ARN>"\n      },\n      "Action": "sts:AssumeRole",\n      "Condition": {\n        "StringEquals": {\n          "sts:ExternalId": ["snow-glue-ext-id"]\n        }\n      }\n    }\n  ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EClick \u003Cstrong\u003EUpdate policy\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EVerify the catalog integration is working:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SYSTEM$VERIFY_CATALOG_INTEGRATION('glue_catalog_irc_int');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe result should return success with no error codes or messages.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWorking with Iceberg tables in Snowflake\u003C/h2\u003E\n","\u003Cp\u003EIn this step you'll create a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database\"\u003ECatalog-Linked Database\u003C/a\u003E (CLD) that auto-discovers the Iceberg tables in your Glue catalog, query the data from Snowflake, create a new Iceberg table, and write aggregate results back to S3.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Catalog-Linked Database\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DATABASE iceberg_linked_db\n  LINKED_CATALOG = (\n    CATALOG = 'glue_catalog_irc_int',\n    NAMESPACE_MODE = FLATTEN_NESTED_NAMESPACE,\n    NAMESPACE_FLATTEN_DELIMITER = '-',\n    ALLOWED_NAMESPACES = ('iceberg')\n  );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck sync status:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SYSTEM$CATALOG_LINK_STATUS('iceberg_linked_db');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe result shows \u003Ccode\u003E"executionState":"RUNNING"\u003C/code\u003E while tables are being discovered. Once sync completes, the Glue \u003Ccode\u003Eiceberg\u003C/code\u003E database and its tables appear in Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EQuery Iceberg tables\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Glue object names (databases, schemas, tables) require double quotes in Snowflake SQL — Glue names are case-sensitive and may contain characters that conflict with Snowflake identifiers.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE iceberg_linked_db;\nUSE SCHEMA "iceberg";\nSELECT * FROM "quotes" LIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate a new Iceberg table and write data\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy no external volume?\u003C/strong\u003E This lab uses \u003Ccode\u003EACCESS_DELEGATION_MODE = VENDED_CREDENTIALS\u003C/code\u003E — Lake Formation issues temporary S3 credentials at query time. Snowflake uses those credentials to write Iceberg data directly, so no separate external volume configuration is needed.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EReplace \u003Ccode\u003E<your-s3-bucket>\u003C/code\u003E with your bucket name from the CloudFormation Outputs:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ICEBERG TABLE iceberg_linked_db."iceberg"."quote_analysis_ice" (\n  "fullname"            STRING,\n  "postcode"            STRING,\n  "custid"              STRING,\n  "ipid"                NUMBER(18,0),\n  "productname"         STRING,\n  "quotecount"          NUMBER(18,0),\n  "policyno"            STRING,\n  "quotedate"           DATE,\n  "quote_product"       STRING,\n  "originalpremium"     NUMBER(28,2),\n  "totalpremiumpayable" NUMBER(28,2),\n  "createddate"         DATE,\n  "brand"               STRING,\n  "branchcode"          STRING,\n  "policy_status_desc"  STRING,\n  "typeofcover_desc"    STRING,\n  "insurer_name"        STRING,\n  "inceptiondate"       DATE,\n  "renewaldate"         DATE\n)\nBASE_LOCATION = 's3://<your-s3-bucket>/iceberg/quote-analysis-iceberg';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECombine the internal \u003Ccode\u003ECUSTOMER\u003C/code\u003E and \u003Ccode\u003EPOLICIES\u003C/code\u003E tables with the Glue-backed \u003Ccode\u003Equotes\u003C/code\u003E Iceberg table and write results to the new table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO iceberg_linked_db."iceberg"."quote_analysis_ice"\nSELECT\n  c.fullname, c.postcode, c.custid, c.ipid, c.productname, c.quotecount,\n  q."policyno", q."quotedate", q."quote_product", q."originalpremium", q."totalpremiumpayable",\n  p.createddate, p.brand, p.branchcode, p.policy_status_desc,\n  p.typeofcover_desc, p.insurer_name, p.inceptiondate, p.renewaldate\nFROM\n  hol_ice_db.public.customer c,\n  "quotes" q,\n  hol_ice_db.public.policies p\nWHERE\n  c.fullname   = q."fullname"\n  AND c.postcode   = q."postcode"\n  AND c.quotecount > 5\n  AND c.custid     = p.custid;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EQuery the aggregate Iceberg table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM iceberg_linked_db."iceberg"."quote_analysis_ice" LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis data is written as Iceberg to S3 and immediately visible in the Glue Data Catalog — other AWS query engines (Athena, EMR, etc.) can read it directly.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOptional:\u003C/strong\u003E Refresh the Database Explorer in Snowsight to see \u003Ccode\u003Eiceberg_linked_db\u003C/code\u003E and its tables. Open the S3 console to see the Iceberg data and metadata files Snowflake created under \u003Ccode\u003Eiceberg/quote-analysis-iceberg/\u003C/code\u003E. Open the Glue console to confirm the new table is registered.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EFollow the steps below to ensure the deployed resources are cleaned up.\nSnowflake:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDrop Snowflake warehouse\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP DATABASE HOL_ICE_WH;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EDrop Snowflake database\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP DATABASE HOL_ICE_DB;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EDrop Catalog-linked database\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP DATABASE iceberg_linked_db;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EDrop Glue catalog integration\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP CATALOG INTEGRATION glue_catalog_irc_int;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAWS:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDelete the Glue Studio Job\u003C/li\u003E\u003Cli\u003EDelete the tables and database in Glue\u003C/li\u003E\u003Cli\u003ERemove data locations in Lake Formation\u003C/li\u003E\u003Cli\u003EDe-register data lake locations in Lake Formation\u003C/li\u003E\u003Cli\u003EEmpty the S3 bucket\u003C/li\u003E\u003Cli\u003EDelete Cloudformation Template\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EYou've converted Parquet data to Iceberg format using AWS Glue, connected Snowflake to the Glue Data Catalog via the Iceberg REST API with Lake Formation vended credentials, and used a Catalog-Linked Database to query, create, and write Iceberg tables — all from Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to convert Parquet files to Iceberg format in-place using an AWS Glue ETL job\u003C/li\u003E\u003Cli\u003EHow to configure Lake Formation credential vending for fine-grained Iceberg access control\u003C/li\u003E\u003Cli\u003EHow to create a Snowflake Catalog Integration for AWS Glue using SIGV4 authentication and vended credentials\u003C/li\u003E\u003Cli\u003EHow to create a Catalog-Linked Database to auto-discover and sync Iceberg tables from the Glue catalog\u003C/li\u003E\u003Cli\u003EHow to read from, write to, and create Iceberg tables in Snowflake via the Catalog-Linked Database\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg\"\u003ESnowflake Iceberg documentation\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/tables-iceberg-catalog-linked-database\"\u003ESnowflake Catalog-Linked Database\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-format-iceberg.html\"\u003EAWS Glue Iceberg Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.aws.amazon.com/lake-formation/latest/dg/aws-lake-formation-api-credential-vending.html\"\u003EAWS Lake Formation Credential Vending\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build open table format data lakes with Apache Iceberg using Snowflake Catalog-Linked Datbases, Vended Credentials, AWS S3, Lake Formation and Glue Data Catalog.","title":"Build Data Lakes using Apache Iceberg with Snowflake and AWS Glue",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","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 huge analytical datasets that enables high performance analytics on open data formats with ACID compliance. Snowflake and AWS both support Iceberg format that enables customers to drastically improve data interoperability, speed of implementation and performance for integrated data lakes.\n\nThis guide will take you through the steps of converting existing parquet data to Iceberg and using it to build open analytic environments using Snowflake and [AWS Glue](https://aws.amazon.com/glue/) with [AWS Lake Formation](https://aws.amazon.com/lake-formation/) providing fine-grained access controls and [temporary access token](https://docs.aws.amazon.com/lake-formation/latest/dg/aws-lake-formation-api-credential-vending.html) to Iceberg tables.\n\n![Workflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/Workflow.png?v=f3d82d45)\n\nFor this guide we will use a Financial Services use case where Insurance data is analyzed. The Quotes data is collected from systems and stored as parquet on S3, while Customer and Policy data is already available as internal Snowflake tables. We will try to identify customers who are likely to churn or potential fraud with a high number of recent quote requests. \n\n### Prerequisites\n- Familiarity with Snowflake, basic SQL, and the Snowsight UI\n- Familiarity with AWS services — S3, Glue, Lake Formation, IAM, and CloudFormation\n\n### What You'll Learn\n- How to convert Parquet files on S3 to Apache Iceberg format using an [AWS Glue ETL job](https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-format-iceberg.html) — no data rewrite, in-place conversion\n- How to configure [AWS Lake Formation](https://aws.amazon.com/lake-formation/) for fine-grained access control on Iceberg tables using credential vending\n- How to create a Snowflake [Catalog Integration](https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-catalog-integration-rest-glue) that connects to AWS Glue via the Iceberg REST Catalog (IRC) API using SIGV4 authentication and Lake Formation vended credentials\n- How to create a [Catalog-Linked Database](https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database) in Snowflake to auto-discover and sync all Iceberg tables from the Glue Data Catalog\n- How to read from, write to, and create Iceberg tables in Snowflake via the Catalog-Linked Database\n\n### What You'll Need\n- A [Snowflake Enterprise Account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=build_datalake_with_glue_and_iceberg) with `ACCOUNTADMIN` access, deployed in **AWS US West 2 (Oregon)**\n- An [AWS Account](https://aws.amazon.com/free/) with Administrator access\n\n### What You'll Build\n- An AWS Glue Data Catalog database with Apache Iceberg tables backed by S3\n- AWS Lake Formation permissions with credential vending for fine-grained Iceberg access control\n- A Snowflake External Volume, Catalog Integration, and Catalog-Linked Database connected to the Glue catalog\n- An end-to-end read/write workflow: query, create, and write Iceberg tables from Snowflake using the Catalog-Linked Database\n\n\u003C!-- ------------------------ --\u003E\n## Set Up with Snowflake CoCo (Fast Path)\n\n[Snowflake CoCo](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) is Snowflake's AI coding agent, built into the Snowflake CLI. You can use it to automate the entire setup in this guide — from deploying the AWS infrastructure to creating the Snowflake catalog integration and Catalog-Linked Database.\n\n**Prerequisites for this path:**\n- AWS CLI installed and configured with Administrator access (`aws configure`)\n- [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation) installed and connected to your Snowflake account\n- Snowflake Enterprise account with `ACCOUNTADMIN` access in **AWS us-west-2**\n\nUse the prompts below in sequence. Each one picks up where the previous left off.\n\nFor the Snowflake-side steps, Snowflake CoCo automatically loads the right skill based on your prompt — you don't need to invoke anything manually.\n\n---\n\n**1. Deploy the AWS infrastructure (CloudFormation):**\n```\nDeploy this CloudFormation template in us-west-2 and show me the stack outputs when complete — stack name: Glue-IRC-Int, template: https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Iceberg_SNOW_AWS/setup/glue-snflk-devday-v1.5.yaml\n```\n*CoCo runs this via AWS CLI — no specific skill, uses your `aws configure` credentials directly.*\n\n**2. Create the Glue Data Catalog database and Iceberg table:**\n```\nCreate a Glue Data Catalog database named 'iceberg' in us-west-2, then create a Glue ETL job that reads Parquet files from s3://aws-data-analytics-workshops/aws_glue/glue-snowflake/raw-data/quotes/ and writes them as Apache Iceberg to s3://\u003Cyour-bucket\u003E/iceberg/quotes/ — use the IAM role from the CloudFormation outputs and register the table in the 'iceberg' database\n```\n*CoCo uses AWS CLI and boto3 to create the Glue database, table definition, and ETL job script.*\n\n**3. Configure Lake Formation for credential vending:**\n```\nConfigure Lake Formation in us-west-2 to enable credential vending for the Iceberg tables in the 'iceberg' Glue database: enable external engine access, register the S3 bucket, and grant the CloudFormation IAM role SUPER permissions on the iceberg database\n```\n*CoCo runs `aws lakeformation` CLI commands — no specific skill.*\n\n**4. Create the Snowflake catalog integration:**\n```\nHelp me create a catalog integration for AWS Glue Iceberg REST with vended credentials\n```\n*CoCo auto-invokes the **`glueirc-catalog-integration-setup`** skill — collects your AWS account ID, region, IAM role ARN, and access delegation preference, generates and executes the SQL, then guides you through the trust policy update.*\n\n**5. Create the Catalog-Linked Database:**\n```\nCreate a catalog-linked database from my Glue catalog integration\n```\n*CoCo auto-invokes the **`catalog-linked-database`** skill — creates the CLD, verifies the connection, and lists the auto-discovered Iceberg tables from the Glue catalog.*\n\nOnce Snowflake CoCo completes step 5, your Snowflake environment is connected to the Glue catalog and ready to query. Skip directly to the [Query Iceberg Tables](#query-iceberg-tables) section.\n\n---\n\n\u003E **Have your own S3 data instead of the sample dataset?** Use `Help me set up my S3 data as Glue Iceberg tables` — CoCo auto-invokes the **`aws-setup`** workflow to walk you through S3 discovery, Glue crawler setup, schema validation, and Parquet-to-Iceberg conversion. Pick up at step 4 above once your Glue tables are registered.\n\n\u003E **Prefer to follow the steps manually?** Continue to the next section for the full console walkthrough.\n\n\u003C!-- ------------------------ --\u003E\n## Configure the AWS Account\n\n\u003E **Used the Snowflake CoCo fast path?** Skip this section and the next two — go directly to [Setup Snowflake account and configure the AWS integrations](#setup-snowflake-account-and-configure-the-aws-integrations).\n\nIn this step you'll use a CloudFormation template to provision the AWS resources needed for this quickstart: an S3 bucket and an IAM role with the policies that Glue needs to access it.\n\n1. Log in to your [AWS Console](https://console.aws.amazon.com/) and confirm the region in the top-right corner is **US West (Oregon) us-west-2**.\n\n![AWSRegion](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AWSRegion.png?v=f3d82d45)\n\n2. Deploy the CloudFormation stack by clicking [here](https://console.aws.amazon.com/cloudformation/home#/stacks/new?stackName=Glue-IRC-Int&templateURL=https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Iceberg_SNOW_AWS/setup/glue-snflk-devday-v1.5.yaml). This creates the S3 bucket and IAM role used throughout the guide.\n\n![CreateStack](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/CreateStack.png?v=f3d82d45)\n\n3. Click **Next** on the **Create stack** screen.\n4. On **Specify stack details**, leave the default stack name `Glue-IRC-Int` and click **Next**.\n5. On **Configure stack options**, scroll to the bottom, check `I acknowledge that AWS CloudFormation might create IAM resources`, and click **Next**.\n6. On **Review and create**, click **Submit**.\n\nStack creation takes about a minute. Use the **Events** tab and the refresh button to monitor progress.\n\n7. Once the stack status shows **CREATE_COMPLETE**, click the **Outputs** tab. Copy the **Role ARN** and **S3 bucket name** — you'll need both in later steps.\n\n![CFoutput](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/CFoutput.png?v=f3d82d45)\n\n\n\u003C!-- ------------------------ --\u003E\n## Use AWS Glue to create the Iceberg table\n\nIn this step you'll create a Glue Data Catalog database and table, then run a Glue ETL job to convert raw Parquet data on S3 to Apache Iceberg format.\n\n### Create a Glue Database\n\n1. In the AWS Console, navigate to **AWS Glue** and confirm the region is **US West (Oregon) us-west-2**.\n\n![AWSRegion](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AWSRegion.png?v=f3d82d45)\n\n2. In the left navigation pane under **Data Catalog**, click **Databases**, then click **Add database**.\n\n![AddDatabase](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AddDatabase.png?v=f3d82d45)\n\n3. Name the database `iceberg` and click **Create database**.\n\n![CreateDatabase](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/CreateDatabase.png?v=f3d82d45)\n\n### Create a Glue Table\n\n1. In the left menu, click **Data Catalog \u003E Databases \u003E Tables**, then click **Add table**.\n\n![AddTable](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AddTable.png?v=f3d82d45)\n\n2. Set the following table properties and click **Next**:\n   - **Name:** `quotes`\n   - **Database:** `iceberg`\n   - **Table format:** Apache Iceberg table\n   - **IAM role:** Select the role from the CloudFormation Outputs (e.g. `Glue-IRC-Int-GlueSnowflakeLabRole-xxxx`)\n   - Check the acknowledgment checkbox\n   - **Data location:** `s3://glue-snowflake-lab-xxxx/iceberg/quotes/` — replace `glue-snowflake-lab-xxxx` with your S3 bucket name from the CloudFormation Outputs\n\n![TableProp](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/TableProp2.png?v=f3d82d45)\n\n3. On the schema screen, click **Edit schema as JSON**.\n\n![Schema](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/Schema.png?v=f3d82d45)\n\n4. Download [quotes_schema.json](https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Iceberg_SNOW_AWS/setup/quotes_schema.json). Clear the `[ ]` placeholder, paste in the file contents, and click **Save**.\n\n![delete](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/delete.png?v=f3d82d45)\n\n![SaveSchema](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/SaveSchema.png?v=f3d82d45)\n\n5. Verify the schema looks correct and click **Next**.\n\n![SchemaNext](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/SchemaNext.png?v=f3d82d45)\n\n6. Click **Create** to finish creating the table.\n\n### Create and Run the Glue ETL Job\n\nThis job reads raw Parquet quotes data from a public S3 bucket and writes it to your S3 bucket as an Iceberg table — no data copy or rewrite. The Glue Data Catalog table you just created is updated in place.\n\n1. In the left menu under **ETL jobs**, click **Visual ETL**, then click the **Visual ETL** button.\n\n![ETLJob](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/ETLJob.png?v=f3d82d45)\n\n2. Rename the job to `Quotes - Raw to Iceberg`.\n3. Under the **Job details** tab, select the IAM role from the CloudFormation Outputs.\n4. Optionally reduce **Requested number of workers** to 2.\n\n![ETLJobConfig](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/ETLJobConfig.png?v=f3d82d45)\n\n5. Click **Save**. A green banner confirms the job is saved.\n6. On the **Visual** tab, click the **+** button and add an **S3** source node. Configure it:\n   - **Name:** `Quotes - Raw`\n   - **S3 URL:** `s3://aws-data-analytics-workshops/aws_glue/glue-snowflake/raw-data/quotes/`\n   - **Data format:** Parquet\n\n![QuotesRaw](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/QuotesRaw.png?v=f3d82d45)\n\n7. With the **Quotes - Raw** node selected, click **+** and add a **Transform - Change Schema** node.\n\n![Transform](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/Transform.png?v=f3d82d45)\n\n![ChangeSchema](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/ChangeSchema.png?v=f3d82d45)\n\n8. In **Change Schema**, change the data type of `uuid` to **varchar**.\n\n![UpdateSchema](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/UpdateSchema.png?v=f3d82d45)\n\n9. With **Change Schema** selected, click **+** and add an **Amazon S3** target node from the **Targets** tab. Configure it:\n   - **Name:** `Quotes - Iceberg`\n   - **Format:** Apache Iceberg\n   - **Compression Type:** Snappy\n   - **S3 Target Location:** `s3://glue-snowflake-lab-xxxx/iceberg/quotes/` — replace `glue-snowflake-lab-xxxx` with your bucket name\n   - **Data Catalog update options:** Create a table in the Data Catalog and on subsequent runs, keep existing schema and add new partitions\n   - **Database:** `iceberg`\n   - **Table name:** `quotes`\n\n![TargetOptions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/TargetOptions.png?v=f3d82d45)\n\n10. Click **Save**, then click **Run**. Navigate to the **Runs** tab to monitor progress.\n\n![RunETL](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/RunETL.png?v=f3d82d45)\n\nOnce the job status shows **Succeeded**, the Iceberg table is live in your Glue Data Catalog.\n\n![RETLComplete](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/ETLComplete.png?v=f3d82d45)\n\nAs a bonus step, open the S3 console and browse your bucket — you'll see the Iceberg data and metadata/manifest files Glue created under `iceberg/quotes/`.\n\n\u003C!-- ------------------------ --\u003E\n## Configure AWS Lake Formation\n\nIn this step you'll configure AWS Lake Formation to grant fine-grained access to the Iceberg table and enable credential vending. Credential vending allows Snowflake to request temporary S3 credentials from Lake Formation at query time — no external volume configuration needed on the Snowflake side.\n\n### Step 1: Enable external engine access\n\n1. Sign in to the [Lake Formation console](https://console.aws.amazon.com/lakeformation/) as a data lake administrator.\n2. In the left navigation pane, choose **Administration \u003E Application integration settings**.\n3. Enable **Allow external engines to access data in Amazon S3 locations with full table access**.\n4. Click **Save**.\n\n### Step 2: Grant data location permissions\n\n1. Under **Permissions**, select **Data locations** and click **Grant**.\n2. For **IAM users and roles**, select the role created by CloudFormation.\n3. For **Storage locations**, click **Browse** and select your S3 bucket.\n4. Check **Grantable** and click **Grant**.\n\n### Step 3: Register the data lake location\n\n1. Under **Administration**, select **Data lake locations** and click **Register location**.\n2. For **Amazon S3 path**, enter the S3 bucket path from the CloudFormation Outputs.\n3. For **IAM role**, select the role created by CloudFormation.\n4. For **Permission mode**, select **Lake Formation**.\n5. Click **Register location**.\n\n### Step 4: Grant table permissions to the IAM role\n\n1. Under **Permissions**, choose **Data permissions** and click **Grant**.\n2. Configure the following:\n   - **Principals:** IAM users and roles → select the role created by CloudFormation\n   - **Resources:** Named Data Catalog resources\n   - **Catalog:** your AWS account ID\n   - **Database:** `iceberg`\n   - **Database permissions:** SUPER\n   - **Grantable permissions:** SUPER\n3. Click **Grant** and confirm the permission appears in the list.\n\n\u003E **Note:** SUPER permission is required for Snowflake to mount the Iceberg table via the Catalog-Linked Database using credential vending.\n\nYour AWS setup is complete. Continue to the next step to configure Snowflake.\n\n\u003C!-- ------------------------ --\u003E\n## Setup Snowflake account and configure the AWS integrations\n\n\u003E **Used the Snowflake CoCo fast path?** Skip this section — go directly to [Working with Iceberg tables in Snowflake](#working-with-iceberg-tables-in-snowflake).\n\nIn this step you'll create the Snowflake role, database, warehouse, and internal tables used in the lab, then create the Catalog Integration and Catalog-Linked Database that connect Snowflake to the Glue Data Catalog.\n\n### Configure your Snowflake account\n\nDownload the two SQL files for this lab:\n\n\u003Cbutton\u003E\n\n  [Setup SQL](https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Iceberg_SNOW_AWS/setup/hol_ice_setup_v1.2.sql)\n\u003C/button\u003E\n\n\u003Cbutton\u003E\n\n  [Workflow SQL](https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Iceberg_SNOW_AWS/setup/hol_ice_workflow_v1.6.sql)\n\u003C/button\u003E\n\n1. Log into your Snowflake account as a user with `ACCOUNTADMIN` privileges.\n2. In the left menu, go to **Projects \u003E Workspaces**, click **+ Add new**, and select **Upload Files**.\n\n![AddNewFiles](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AddNewFiles.png?v=f3d82d45)\n\n3. Upload both SQL files. They'll appear under **My Workspace**.\n4. Open `hol_ice_setup_vxx.sql`. Run the following sections in order by selecting each block and clicking **Run**.\n\n![run2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/run2.png?v=f3d82d45)\n\nCreate the role and grant account-level privileges:\n\n```sql\nUSE ROLE SECURITYADMIN;\n\nCREATE OR REPLACE ROLE HOL_ICE_RL COMMENT='Iceberg Role';\nGRANT ROLE HOL_ICE_RL TO ROLE SYSADMIN;\n```\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nGRANT CREATE INTEGRATION ON ACCOUNT TO ROLE HOL_ICE_RL;\nGRANT CREATE EXTERNAL VOLUME ON ACCOUNT TO ROLE HOL_ICE_RL;\nGRANT CREATE DATABASE ON ACCOUNT TO ROLE HOL_ICE_RL;\nGRANT CREATE WAREHOUSE ON ACCOUNT TO ROLE HOL_ICE_RL;\n```\n\nCreate the database and warehouse:\n\n```sql\nUSE ROLE HOL_ICE_RL;\n\nCREATE OR REPLACE DATABASE HOL_ICE_DB;\n\nCREATE OR REPLACE WAREHOUSE HOL_ICE_WH\n  WITH WAREHOUSE_SIZE = 'XSMALL'\n  INITIALLY_SUSPENDED = TRUE;\n```\n\n5. Select lines 44–110 and run them to create the internal `CUSTOMER` and `POLICIES` tables and load data.\n\n### Configure the Snowflake Catalog Integration with Glue\n\nOpen `hol_ice_workflow_vxx.sql` from **My Workspace**. Set the worksheet context (lines 16–20):\n\n![WorkflowUI](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/WorkflowUI.png?v=f3d82d45)\n\n```sql\nUSE ROLE HOL_ICE_RL;\n\nUSE HOL_ICE_DB.PUBLIC;\n\nUSE WAREHOUSE HOL_ICE_WH;\n```\n\nYou'll need two values from your AWS environment:\n- **AWS Account ID** — from the top-right corner of the AWS Console\n\n![accountID2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/AWSAccountID2.png?v=f3d82d45)\n\n- **IAM Role ARN** — from the CloudFormation Outputs tab\n\n\u003E **Note:** This guide uses `CATALOG_SOURCE = ICEBERG_REST` with `CATALOG_API_TYPE = AWS_GLUE` — the current recommended approach for connecting to the Glue Data Catalog via the Iceberg REST API. If you have an older integration using `CATALOG_SOURCE = GLUE`, migrate to this approach to get access to Catalog-Linked Databases, vended credentials, and full IRC feature support.\n\nReplace the placeholders and run the `CREATE CATALOG INTEGRATION` statement (lines 37–53):\n\n```sql\nCREATE OR REPLACE CATALOG INTEGRATION glue_catalog_irc_int\n  CATALOG_SOURCE = ICEBERG_REST\n  TABLE_FORMAT = ICEBERG\n  CATALOG_NAMESPACE = 'iceberg'\n  REST_CONFIG = (\n    CATALOG_URI = 'https://glue.us-west-2.amazonaws.com/iceberg'\n    CATALOG_API_TYPE = AWS_GLUE\n    CATALOG_NAME = '\u003Cyour-aws-account-id\u003E'\n    ACCESS_DELEGATION_MODE = VENDED_CREDENTIALS\n  )\n  REST_AUTHENTICATION = (\n    TYPE = SIGV4\n    SIGV4_IAM_ROLE = 'arn:aws:iam::\u003Cyour-aws-account-id\u003E:role/\u003Cyour-cloudformation-role-name\u003E'\n    SIGV4_SIGNING_REGION = 'us-west-2'\n    SIGV4_EXTERNAL_ID = 'snow-glue-ext-id'\n  )\n  ENABLED = TRUE;\n```\n\n\u003E **`SIGV4_EXTERNAL_ID`**: This is a custom value you set — it ties to the trust policy in the next step. `snow-glue-ext-id` is the value used in this lab. You can use any string, or omit it to have Snowflake auto-generate one (retrieve the generated value via `DESC CATALOG INTEGRATION` afterward).\n\nRun `DESC CATALOG INTEGRATION` to retrieve the value needed for the IAM trust policy:\n\n```sql\nDESC CATALOG INTEGRATION glue_catalog_irc_int;\n```\n\nNote the **`GLUE_AWS_IAM_USER_ARN`** value from the output — you'll paste it into the trust policy next.\n\n### Update the IAM trust policy\n\n1. In the AWS Console, navigate to **IAM \u003E Roles** and open the CloudFormation role.\n2. Click the **Trust relationships** tab, then **Edit trust policy**.\n\n![TrustPolicy2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-lake-using-apache-iceberg-with-snowflake-and-aws-glue/TrustPolicy2.png?v=f3d82d45)\n\n3. Replace the entire policy with the JSON below, substituting `GLUE_AWS_IAM_USER_ARN` from the Snowflake output:\n\n```json\n{\n  \"Version\": \"2012-10-17\",\n  \"Statement\": [\n    {\n      \"Effect\": \"Allow\",\n      \"Principal\": {\n        \"Service\": [\n          \"lakeformation.amazonaws.com\",\n          \"glue.amazonaws.com\"\n        ]\n      },\n      \"Action\": \"sts:AssumeRole\"\n    },\n    {\n      \"Effect\": \"Allow\",\n      \"Principal\": {\n        \"AWS\": \"\u003CGLUE_AWS_IAM_USER_ARN\u003E\"\n      },\n      \"Action\": \"sts:AssumeRole\",\n      \"Condition\": {\n        \"StringEquals\": {\n          \"sts:ExternalId\": [\"snow-glue-ext-id\"]\n        }\n      }\n    }\n  ]\n}\n```\n\n4. Click **Update policy**.\n\nVerify the catalog integration is working:\n\n```sql\nSELECT SYSTEM$VERIFY_CATALOG_INTEGRATION('glue_catalog_irc_int');\n```\n\nThe result should return success with no error codes or messages.\n\n\u003C!-- ------------------------ --\u003E\n## Working with Iceberg tables in Snowflake\n\nIn this step you'll create a [Catalog-Linked Database](https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database) (CLD) that auto-discovers the Iceberg tables in your Glue catalog, query the data from Snowflake, create a new Iceberg table, and write aggregate results back to S3.\n\n### Create the Catalog-Linked Database\n\n```sql\nCREATE OR REPLACE DATABASE iceberg_linked_db\n  LINKED_CATALOG = (\n    CATALOG = 'glue_catalog_irc_int',\n    NAMESPACE_MODE = FLATTEN_NESTED_NAMESPACE,\n    NAMESPACE_FLATTEN_DELIMITER = '-',\n    ALLOWED_NAMESPACES = ('iceberg')\n  );\n```\n\nCheck sync status:\n```sql\nSELECT SYSTEM$CATALOG_LINK_STATUS('iceberg_linked_db');\n```\n\nThe result shows `\"executionState\":\"RUNNING\"` while tables are being discovered. Once sync completes, the Glue `iceberg` database and its tables appear in Snowflake.\n\n### Query Iceberg tables\n\n\u003E **Note:** Glue object names (databases, schemas, tables) require double quotes in Snowflake SQL — Glue names are case-sensitive and may contain characters that conflict with Snowflake identifiers.\n\n```sql\nUSE DATABASE iceberg_linked_db;\nUSE SCHEMA \"iceberg\";\nSELECT * FROM \"quotes\" LIMIT 20;\n```\n\n### Create a new Iceberg table and write data\n\n\u003E **Why no external volume?** This lab uses `ACCESS_DELEGATION_MODE = VENDED_CREDENTIALS` — Lake Formation issues temporary S3 credentials at query time. Snowflake uses those credentials to write Iceberg data directly, so no separate external volume configuration is needed.\n\nReplace `\u003Cyour-s3-bucket\u003E` with your bucket name from the CloudFormation Outputs:\n\n```sql\nCREATE OR REPLACE ICEBERG TABLE iceberg_linked_db.\"iceberg\".\"quote_analysis_ice\" (\n  \"fullname\"            STRING,\n  \"postcode\"            STRING,\n  \"custid\"              STRING,\n  \"ipid\"                NUMBER(18,0),\n  \"productname\"         STRING,\n  \"quotecount\"          NUMBER(18,0),\n  \"policyno\"            STRING,\n  \"quotedate\"           DATE,\n  \"quote_product\"       STRING,\n  \"originalpremium\"     NUMBER(28,2),\n  \"totalpremiumpayable\" NUMBER(28,2),\n  \"createddate\"         DATE,\n  \"brand\"               STRING,\n  \"branchcode\"          STRING,\n  \"policy_status_desc\"  STRING,\n  \"typeofcover_desc\"    STRING,\n  \"insurer_name\"        STRING,\n  \"inceptiondate\"       DATE,\n  \"renewaldate\"         DATE\n)\nBASE_LOCATION = 's3://\u003Cyour-s3-bucket\u003E/iceberg/quote-analysis-iceberg';\n```\n\nCombine the internal `CUSTOMER` and `POLICIES` tables with the Glue-backed `quotes` Iceberg table and write results to the new table:\n\n```sql\nINSERT INTO iceberg_linked_db.\"iceberg\".\"quote_analysis_ice\"\nSELECT\n  c.fullname, c.postcode, c.custid, c.ipid, c.productname, c.quotecount,\n  q.\"policyno\", q.\"quotedate\", q.\"quote_product\", q.\"originalpremium\", q.\"totalpremiumpayable\",\n  p.createddate, p.brand, p.branchcode, p.policy_status_desc,\n  p.typeofcover_desc, p.insurer_name, p.inceptiondate, p.renewaldate\nFROM\n  hol_ice_db.public.customer c,\n  \"quotes\" q,\n  hol_ice_db.public.policies p\nWHERE\n  c.fullname   = q.\"fullname\"\n  AND c.postcode   = q.\"postcode\"\n  AND c.quotecount \u003E 5\n  AND c.custid     = p.custid;\n```\n\nQuery the aggregate Iceberg table:\n\n```sql\nSELECT * FROM iceberg_linked_db.\"iceberg\".\"quote_analysis_ice\" LIMIT 10;\n```\n\nThis data is written as Iceberg to S3 and immediately visible in the Glue Data Catalog — other AWS query engines (Athena, EMR, etc.) can read it directly.\n\n\u003E **Optional:** Refresh the Database Explorer in Snowsight to see `iceberg_linked_db` and its tables. Open the S3 console to see the Iceberg data and metadata files Snowflake created under `iceberg/quote-analysis-iceberg/`. Open the Glue console to confirm the new table is registered.\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\nFollow the steps below to ensure the deployed resources are cleaned up.\nSnowflake:\n  - Drop Snowflake warehouse\n  ```sql\n  DROP DATABASE HOL_ICE_WH;\n  ```\n  - Drop Snowflake database\n  ```sql\n  DROP DATABASE HOL_ICE_DB;\n  ```\n  - Drop Catalog-linked database\n  ```sql\n  DROP DATABASE iceberg_linked_db;\n  ```\n  - Drop Glue catalog integration \n  ```sql\n  DROP CATALOG INTEGRATION glue_catalog_irc_int;\n  ```\nAWS:\n  - Delete the Glue Studio Job\n  - Delete the tables and database in Glue\n  - Remove data locations in Lake Formation\n  - De-register data lake locations in Lake Formation\n  - Empty the S3 bucket\n  - Delete Cloudformation Template\n\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nYou've converted Parquet data to Iceberg format using AWS Glue, connected Snowflake to the Glue Data Catalog via the Iceberg REST API with Lake Formation vended credentials, and used a Catalog-Linked Database to query, create, and write Iceberg tables — all from Snowflake.\n\n### What You Learned\n- How to convert Parquet files to Iceberg format in-place using an AWS Glue ETL job\n- How to configure Lake Formation credential vending for fine-grained Iceberg access control\n- How to create a Snowflake Catalog Integration for AWS Glue using SIGV4 authentication and vended credentials\n- How to create a Catalog-Linked Database to auto-discover and sync Iceberg tables from the Glue catalog\n- How to read from, write to, and create Iceberg tables in Snowflake via the Catalog-Linked Database\n\n### Related Resources\n- [Snowflake Iceberg documentation](https://docs.snowflake.com/en/user-guide/tables-iceberg)\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- [Snowflake Catalog-Linked Database](https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database)\n- [AWS Glue Iceberg Documentation](https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-format-iceberg.html)\n- [AWS Lake Formation Credential Vending](https://docs.aws.amazon.com/lake-formation/latest/dg/aws-lake-formation-api-credential-vending.html)\n\n\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-54afafd494","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-09b8b97649",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-c7cfee427c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-05",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-722f813224","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake 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Up with Snowflake CoCo (Fast Path)\u003C/h2\u003E","\u003Ch2\u003EConfigure the AWS Account\u003C/h2\u003E","\u003Ch2\u003EUse AWS Glue to create the Iceberg table\u003C/h2\u003E","\u003Ch2\u003EConfigure AWS Lake Formation\u003C/h2\u003E","\u003Ch2\u003ESetup Snowflake account and configure the AWS integrations\u003C/h2\u003E","\u003Ch2\u003EWorking with Iceberg tables in Snowflake\u003C/h2\u003E","\u003Ch2\u003ECleanup\u003C/h2\u003E","\u003Ch2\u003EConclusion and 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