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Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-06d3a043f4","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-cc1c1a2e77",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-cf014850a7","paragraphs":["<!-- ------------------------ -->\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. With Apache Iceberg's broad adoption across major data platforms, it is designed for vendor-neutral interoperability. You can write data once and query it from any compatible engine—no proprietary formats, no data migration required.\u003C/p\u003E\n","\u003Cp\u003EThis guide demonstrates how to build a modern, interoperable lakehouse architecture that seamlessly integrates \u003Cstrong\u003EAWS Glue\u003C/strong\u003E, \u003Cstrong\u003EApache Iceberg\u003C/strong\u003E, and \u003Cstrong\u003ESnowflake\u003C/strong\u003E to enable data processing workloads across multiple compute engines while maintaining a single source of truth.\u003C/p\u003E\n","\u003Cp\u003EThe solution showcases how organizations can leverage best-of-breed tools without vendor lock-in, allowing Spark code written for AWS Glue to run unmodified on Snowflake using \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-overview\"\u003ESnowpark Connect\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/arch.jpeg?v=f3d82d45\" alt=\"Lakehouse Architecture\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYour organization's data lives everywhere -- across tools, platforms, and cloud providers. If you use both AWS and Snowflake, you can build a connected and interoperable architecture that focuses on getting value from your data without costly and complex data movement.\u003C/p\u003E\n","\u003Cp\u003EFor this guide, we will use a Sales Analytics use case where 1M+ transaction records are processed. We'll load sales and customer data into Iceberg tables via AWS Glue, then demonstrate how the exact same Spark code can run on both AWS Glue and Snowflake to generate identical Top 10 product sales reports.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Snowflake, basic SQL, Snowsight UI, and Snowflake Objects\u003C/li\u003E\u003Cli\u003EFamiliarity with AWS Services (S3, Glue, CloudFormation, IAM) and the Management Console\u003C/li\u003E\u003Cli\u003EBasic knowledge of Python and PySpark\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create Iceberg tables in the \u003Ca href=\"https://docs.aws.amazon.com/prescriptive-guidance/latest/serverless-etl-aws-glue/aws-glue-data-catalog.html\"\u003EAWS Glue Data Catalog\u003C/a\u003E using Glue ETL jobs\u003C/li\u003E\u003Cli\u003EHow to configure a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database\"\u003ESnowflake Catalog-Linked Database\u003C/a\u003E with the Glue Data Catalog via Iceberg REST Catalog APIs\u003C/li\u003E\u003Cli\u003EHow to use \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-overview\"\u003ESnowpark Connect\u003C/a\u003E to run identical Spark code on Snowflake\u003C/li\u003E\u003Cli\u003EHow to achieve true code portability between AWS Glue and Snowflake with zero data movement\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=interoperable_lakehouse_glue_iceberg\"\u003ESnowflake Account\u003C/a\u003E with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E access (Enterprise Edition or higher recommended)\u003C/li\u003E\u003Cli\u003EAn \u003Ca href=\"https://aws.amazon.com/free/\"\u003EAWS Account\u003C/a\u003E with administrative access\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://aws.amazon.com/cli/\"\u003EAWS CLI\u003C/a\u003E installed and configured (optional, for command-line deployment)\u003C/li\u003E\u003Cli\u003ESnowflake Notebook feature enabled (requires Snowflake Runtime 2.0+)\u003C/li\u003E\u003Cli\u003EThe \u003Ca href=\"https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main\"\u003Eaccompanying GitHub repository\u003C/a\u003E cloned or downloaded to your local machine\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn AWS Glue Database with Iceberg tables stored on S3\u003C/li\u003E\u003Cli\u003ECloudFormation-deployed infrastructure including IAM roles and Glue ETL jobs\u003C/li\u003E\u003Cli\u003ESnowflake External Volume and Catalog Integration with AWS Glue\u003C/li\u003E\u003Cli\u003ESnowflake Catalog-Linked Database for automatic table discovery\u003C/li\u003E\u003Cli\u003ESales analytics pipeline demonstrating 95%+ code reuse between platforms\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESetup\u003C/h3\u003E\n","\u003Cp\u003EThis guide has an accompanying GitHub repository that contains all the files, scripts, data, and resources you need to complete this quickstart:\u003C/p\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main\"\u003EGitHub Repository: glue_cld_snowflake\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERepository Contents:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eglue_cld_snowflake/\n├── cloudformation/\n│   └── iceberg_glue_stack_cloudformation_template.yaml  # AWS infrastructure template\n├── data/\n│   ├── customers.json                                    # Customer data\n│   └── sales_data_1m.json.gz                            # 1M sales records (compressed)\n├── scripts/\n│   ├── iceberg_cld_demo_sales_table_script_1.py         # Load sales data\n│   ├── iceberg_cld_demo_customer_table_script_2.py      # Load customer data\n│   ├── iceberg_cld_demo_sales_report_table_script_3.py  # Generate sales report\n│   └── iceberg_cld_demo_snow_report_table_script_4.py   # Create Snowflake report table\n├── snowflake_notebook/\n│   └── 01_GLUE_LH_SNOWPARK_CONNECT_DEMO.ipynb           # Snowflake notebook\n├── diagrams/                                             # Architecture diagrams\n└── trusted_policy/\n    └── IAM_trust_policy.json                             # IAM trust policy template\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClone or download this repository before starting the quickstart to have all necessary files readily available.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EArchitecture and Data Flow\u003C/h2\u003E\n","\u003Cp\u003EThis solution implements an \u003Cstrong\u003Eopen lakehouse architecture\u003C/strong\u003E that separates storage from compute and uses open standards (Apache Iceberg) to enable seamless interoperability.\u003C/p\u003E\n","\u003Ch3\u003EKey Components\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. AWS Glue with Apache Iceberg\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EInfrastructure as Code via CloudFormation template\u003C/li\u003E\u003Cli\u003EGlue Database for Iceberg table metadata\u003C/li\u003E\u003Cli\u003E4 pre-configured Glue ETL jobs (Glue 4.0 with Spark 3.5)\u003C/li\u003E\u003Cli\u003EIAM roles with appropriate S3 and Glue permissions\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2. Snowflake Catalog-Linked Database\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAutomatically syncs with AWS Glue Data Catalog\u003C/li\u003E\u003Cli\u003EDiscovers Iceberg tables without manual configuration\u003C/li\u003E\u003Cli\u003ESupports both read and write operations to the remote catalog\u003C/li\u003E\u003Cli\u003ESync interval configurable (default 60 seconds)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3. Snowpark Connect\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EApache Spark DataFrame API implementation for Snowflake\u003C/li\u003E\u003Cli\u003EEnables existing Spark applications to run unchanged\u003C/li\u003E\u003Cli\u003EProvides Spark-compatible API with Snowflake's optimized query engine\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EData Flow Diagram\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E┌─────────────────────────────────────────────────────────────────┐\n│                     S3 Data Bucket                              │\n│  • raw_data/sales_data_1m.json.gz (1M records)                 │\n│  • raw_data/customers.json                                      │\n│  • iceberg-warehouse/ (Iceberg table storage)                   │\n└────────────┬──────────────────────────────────┬─────────────────┘\n             │                                  │\n             ▼                                  ▼\n┌─────────────────────────┐        ┌──────────────────────────────┐\n│   AWS Glue ETL Jobs     │        │  AWS Glue Data Catalog       │\n│  (Spark on AWS)         │◄──────►│  (Iceberg REST Catalog)      │\n│                         │        │  • Metadata for all tables   │\n│  Script 1: Load Sales   │        │  • Schema definitions        │\n│  Script 2: Load Customers│       │  • Partition info            │\n│  Script 3: Sales Report │        └──────────┬───────────────────┘\n│  Script 4: Snow Report  │                   │\n└─────────────────────────┘                   │ Iceberg REST API\n                                              │ (Catalog Integration)\n                                              ▼\n                                   ┌──────────────────────────────┐\n                                   │ Snowflake Catalog-Linked DB  │\n                                   │  (Snowpark Connect)          │\n                                   │                              │\n                                   │  • Auto-discovers tables     │\n                                   │  • Notebook with Spark code  │\n                                   │  • Generates same report     │\n                                   │  • Zero data movement        │\n                                   └──────────────────────────────┘\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUpload Data into S3\u003C/h2\u003E\n","\u003Cp\u003EIn this step, we will create an S3 bucket and upload the data files and Glue scripts needed for the demo.\u003C/p\u003E\n","\u003Ch3\u003ECreate S3 Bucket\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to AWS S3 Console and create a new bucket\u003C/li\u003E\u003Cli\u003EChoose a unique name (e.g., \u003Ccode\u003Emy-iceberg-lakehouse-demo-<your-account-id>\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003ESelect the same region as your Snowflake account (to minimize egress charges)\u003C/li\u003E\u003Cli\u003EKeep default settings for encryption and versioning\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Option: Using AWS CLI\nexport DATA_BUCKET=my-iceberg-lakehouse-demo-<your-account-id>\nexport AWS_REGION=us-west-2  # Match with your Snowflake region\n\naws s3 mb s3://$DATA_BUCKET --region $AWS_REGION\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Folder Structure\u003C/h3\u003E\n","\u003Cp\u003ECreate the required folders in your bucket:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Eraw_data/\u003C/code\u003E - for source data files\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Escripts/\u003C/code\u003E - for Glue ETL scripts\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eiceberg-warehouse/\u003C/code\u003E - for Iceberg table storage\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Create folder structure\naws s3api put-object --bucket $DATA_BUCKET --key raw_data/\naws s3api put-object --bucket $DATA_BUCKET --key scripts/\naws s3api put-object --bucket $DATA_BUCKET --key iceberg-warehouse/\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUpload Data Files\u003C/h3\u003E\n","\u003Cp\u003EUpload the data files from the \u003Ca href=\"https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/data\"\u003E\u003Ccode\u003Edata/\u003C/code\u003E folder\u003C/a\u003E in the repository:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Navigate to the cloned repository directory\n# (https://github.com/sfc-gh-pjain/glue_cld_snowflake)\ncd glue_cld_snowflake\n\n# Upload data files from the cloned repository\n# (https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/data)\naws s3 cp data/customers.json s3://$DATA_BUCKET/raw_data/customers.json\naws s3 cp data/sales_data_1m.json.gz s3://$DATA_BUCKET/raw_data/sales_data_1m.json.gz\n\n# Verify upload\naws s3 ls s3://$DATA_BUCKET/raw_data/\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/raw.jpeg?v=f3d82d45\" alt=\"Raw Data Folder\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EUpload Glue Scripts\u003C/h3\u003E\n","\u003Cp\u003EUpload all Glue ETL scripts from the \u003Ca href=\"https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/scripts\"\u003E\u003Ccode\u003Escripts/\u003C/code\u003E folder\u003C/a\u003E in the repository:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Upload all Glue scripts from the cloned repository\n# (https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/scripts)\naws s3 sync scripts/ s3://$DATA_BUCKET/scripts/ --exclude "*" --include "*.py"\n\n# Verify scripts upload\naws s3 ls s3://$DATA_BUCKET/scripts/\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/scripts.jpeg?v=f3d82d45\" alt=\"Scripts Folder\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eiceberg_cld_demo_customer_table_script_2.py\niceberg_cld_demo_sales_table_script_1.py\niceberg_cld_demo_sales_report_table_script_3.py\niceberg_cld_demo_snow_report_table_script_4.py\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Snowflake External Volume\u003C/h2\u003E\n","\u003Cp\u003EIn this step, we will create the Snowflake database and External Volume to enable S3 access.\u003C/p\u003E\n","\u003Ch3\u003ERun Initial Setup Cells\u003C/h3\u003E\n","\u003Cp\u003ERun the first cells of the Snowflake notebook to create the database and external volume.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECell 0: Create Database and Schema\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DATABASE ICEBERG_LAKE;\nCREATE OR REPLACE SCHEMA ICEBERG_LAKE.DEMO;\nUSE SCHEMA ICEBERG_LAKE.DEMO;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECell 2: Create External Volume\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EUpdate the following parameters in Cell 2 before running:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPlaceholder\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EReplace With\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<Your dataBucket name>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYour S3 bucket name from Step 1\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<AWS ROLE ARN>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELeave as placeholder (update after CloudFormation)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<Any secret word you want>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChoose a secret external ID (e.g., \u003Ccode\u003Emy_iceberg_ext_id_2024\u003C/code\u003E)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE EXTERNAL VOLUME extvol_iceberg_demo\nSTORAGE_LOCATIONS =\n      (\n         (\n            NAME = 'Iceberg-Table-Demo'\n            STORAGE_PROVIDER = 'S3'\n            STORAGE_BASE_URL = 's3://<Your dataBucket name>/'\n            STORAGE_AWS_ROLE_ARN = '<AWS ROLE ARN>'\n            STORAGE_AWS_EXTERNAL_ID = '<Any secret word you want>'\n         )\n      );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Save the External ID you choose - you'll need it for CloudFormation deployment.\u003C/p\u003E\n","\u003Ch3\u003EGet Snowflake Credentials\u003C/h3\u003E\n","\u003Cp\u003ERun Cell 3 to retrieve the Snowflake-generated IAM User ARN and External ID:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDESC EXTERNAL VOLUME extvol_iceberg_demo;\n\nSELECT b.KEY, b.VALUE \nFROM \n    TABLE(RESULT_SCAN(LAST_QUERY_ID())) a, \n    TABLE(FLATTEN(INPUT => PARSE_JSON(a."property_value"))) b \nWHERE \n    a."parent_property" = 'STORAGE_LOCATIONS'\n    AND a."property" = 'STORAGE_LOCATION_1'\n    AND (b.KEY='STORAGE_AWS_EXTERNAL_ID' OR b.KEY ='STORAGE_AWS_IAM_USER_ARN');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOutput:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EKEY                          | VALUE\n-----------------------------+----------------------------------------\nSTORAGE_AWS_IAM_USER_ARN     | arn:aws:iam::123456789012:user/abc...\nSTORAGE_AWS_EXTERNAL_ID      | XYZ12345_SFCRole=1_AbCdEfGh...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIMPORTANT: Copy and save these values!\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003ESTORAGE_AWS_IAM_USER_ARN\u003C/code\u003E → Use as \u003Cstrong\u003ESnowflakeUserArn\u003C/strong\u003E in CloudFormation\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTORAGE_AWS_EXTERNAL_ID\u003C/code\u003E → Use as \u003Cstrong\u003ESnowflakeExternalId\u003C/strong\u003E in CloudFormation\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploy AWS CloudFormation Stack\u003C/h2\u003E\n","\u003Cp\u003EIn this step, we will deploy the CloudFormation template that creates all AWS infrastructure including the Glue database, IAM role, and ETL jobs.\u003C/p\u003E\n","\u003Ch3\u003EPrepare CloudFormation Parameters\u003C/h3\u003E\n","\u003Cp\u003EGather these parameters before deploying:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EParameter Name\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample Value\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EGlueDatabaseName\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EName for Glue database\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowcldtest\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EGlueRoleName\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EName for IAM role\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowcldtest\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EDataBucketName\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ES3 bucket from Step 1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowcldtestbucket\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ES3ScriptLocation\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPath to any script\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Es3://snowcldtestbucket/scripts/iceberg_cld_demo_sales_table_script_1.py\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESnowflakeUserArn\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFrom Step 4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Earn:aws:iam::123456789012:user/abc...\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESnowflakeExternalId\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFrom Step 4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EXYZ12345_SFCRole=1_...\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EDeploy via AWS Console\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAWS CloudFormation Console\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate stack\u003C/strong\u003E → \u003Cstrong\u003EWith new resources\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EChoose \u003Cstrong\u003EUpload a template file\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EUpload: \u003Ca href=\"https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/cloudformation\"\u003E\u003Ccode\u003Ecloudformation/iceberg_glue_stack_cloudformation_template.yaml\u003C/code\u003E\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/cloudform.jpeg?v=f3d82d45\" alt=\"CloudFormation Template\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EEnter Stack Parameters\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EStack name\u003C/strong\u003E: \u003Ccode\u003Eiceberg-glue-snowflake-demo\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EFill in all parameters from the table above\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EConfigure Stack Options\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETags\u003C/strong\u003E (optional): Add tags for cost tracking\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPermissions\u003C/strong\u003E: Leave as default\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStack failure options\u003C/strong\u003E: Select \u003Cstrong\u003EDelete newly created resources during rollback\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EReview and Deploy\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EReview all parameters\u003C/li\u003E\u003Cli\u003ECheck the box: \u003Cstrong\u003E"I acknowledge that AWS CloudFormation might create IAM resources with custom names"\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ESubmit\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EMonitor Stack Creation\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWait for stack status to change to \u003Cstrong\u003ECREATE_COMPLETE\u003C/strong\u003E (typically 2-5 minutes)\u003C/li\u003E\u003Cli\u003EMonitor the \u003Cstrong\u003EEvents\u003C/strong\u003E tab for progress\u003C/li\u003E\u003Cli\u003EIf any errors occur, check the \u003Cstrong\u003EEvents\u003C/strong\u003E tab for details\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EDeploy via AWS CLI (Alternative)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Set variables\nexport GLUE_DB_NAME=iceberg_demo_db\nexport GLUE_ROLE_NAME=GlueIcebergRole\nexport DATA_BUCKET=my-iceberg-lakehouse-demo-123456789012\nexport SNOWFLAKE_USER_ARN="arn:aws:iam::123456789012:user/abc..."\nexport SNOWFLAKE_EXT_ID="XYZ12345_SFCRole=1_..."\n\n# Deploy stack using CloudFormation template from the cloned repository\n# (https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/cloudformation)\naws cloudformation create-stack \\\n  --stack-name iceberg-glue-snowflake-demo \\\n  --template-body file://cloudformation/iceberg_glue_stack_cloudformation_template.yaml \\\n  --capabilities CAPABILITY_NAMED_IAM \\\n  --on-failure DELETE \\\n  --parameters \\\n      ParameterKey=GlueDatabaseName,ParameterValue=$GLUE_DB_NAME \\\n      ParameterKey=GlueRoleName,ParameterValue=$GLUE_ROLE_NAME \\\n      ParameterKey=DataBucketName,ParameterValue=$DATA_BUCKET \\\n      ParameterKey=S3ScriptLocation,ParameterValue=s3://$DATA_BUCKET/scripts/iceberg_cld_demo_sales_table_script_1.py \\\n      ParameterKey=SnowflakeUserArn,ParameterValue="$SNOWFLAKE_USER_ARN" \\\n      ParameterKey=SnowflakeExternalId,ParameterValue="$SNOWFLAKE_EXT_ID"\n\n# Check stack status\naws cloudformation describe-stacks \\\n  --stack-name iceberg-glue-snowflake-demo \\\n  --query 'Stacks[0].StackStatus'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter CloudFormation completes, retrieve the IAM Role ARN and update the Snowflake notebook.\u003C/p\u003E\n","\u003Ch3\u003EGet the Glue IAM Role ARN\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing AWS Console:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EGo to CloudFormation → Stacks → \u003Ccode\u003Eiceberg-glue-snowflake-demo\u003C/code\u003E → \u003Cstrong\u003EOutputs\u003C/strong\u003E tab\u003C/li\u003E\u003Cli\u003ELook for "GlueRole" key → Copy the role name\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing AWS CLI:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eaws cloudformation describe-stacks \\\n  --stack-name iceberg-glue-snowflake-demo \\\n  --query 'Stacks[0].Outputs[?OutputKey==`GlueRole`].OutputValue' \\\n  --output text\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConstruct Full Role ARN\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003Earn:aws:iam::<YOUR-AWS-ACCOUNT-ID>:role/<GlueRoleName>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExample:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Earn:aws:iam::123456789012:role/GlueIcebergRole\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUpdate Snowflake Notebook Parameters\u003C/h3\u003E\n","\u003Cp\u003EGo back to your Snowflake notebook and update the AWS Role ARN in Cell 2:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE EXTERNAL VOLUME extvol_iceberg_demo\nSTORAGE_LOCATIONS = (\n    (\n        NAME = 'Iceberg-Table-Demo'\n        STORAGE_PROVIDER = 'S3'\n        STORAGE_BASE_URL = 's3://<Your dataBucket name>/'\n        STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::<account-id>:role/<role-name>'  -- UPDATE THIS\n        STORAGE_AWS_EXTERNAL_ID = '<your-external-id>'\n    )\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERe-run Cell 2 to recreate the external volume with the correct IAM role.\u003C/p\u003E\n","\u003Ch3\u003EVerify AWS Glue Resources\u003C/h3\u003E\n","\u003Cp\u003EBefore running the ETL jobs, verify that CloudFormation created all required resources.\u003C/p\u003E\n","\u003Ch3\u003ECheck Glue Database\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Using AWS CLI\naws glue get-database --name $GLUE_DB_NAME\n\n# Using AWS Console\n# Navigate to: AWS Glue → Data Catalog → Databases\n# Verify your database exists\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECheck Glue ETL Jobs\u003C/h3\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EAWS Glue Console\u003C/strong\u003E → \u003Cstrong\u003EETL Jobs\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EYou should see \u003Cstrong\u003E4 Glue jobs\u003C/strong\u003E created:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EJob Name\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eiceberg-sales-table-<db-name>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELoad 1M sales records to Iceberg\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eiceberg-customer-table-<db-name>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELoad customer data to Iceberg\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eiceberg-sales-report-<db-name>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGenerate Top 10 products report (Spark)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eiceberg-snow-report-<db-name>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate placeholder table for Snowflake\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/glue.jpeg?v=f3d82d45\" alt=\"Glue ETL Jobs\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EVerify Job Configuration\u003C/h3\u003E\n","\u003Cp\u003EEach job should have:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EScript location\u003C/strong\u003E: \u003Ccode\u003Es3://<your-bucket>/scripts/iceberg_cld_demo_*.py\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIAM role\u003C/strong\u003E: The role created by CloudFormation\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGlue version\u003C/strong\u003E: 4.0\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWorker type\u003C/strong\u003E: G.1X\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENumber of workers\u003C/strong\u003E: 2\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun Glue ETL Jobs\u003C/h2\u003E\n","\u003Cp\u003EIn this step, we will run the Glue ETL jobs to load data into Iceberg tables.\u003C/p\u003E\n","\u003Ch3\u003ERun Customer Table Load Job\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing AWS Console:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EGo to \u003Cstrong\u003EAWS Glue\u003C/strong\u003E → \u003Cstrong\u003EETL Jobs\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003Eiceberg-customer-table-<db-name>\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ERun\u003C/strong\u003E button\u003C/li\u003E\u003Cli\u003EMonitor the \u003Cstrong\u003ERuns\u003C/strong\u003E tab for status\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing AWS CLI:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eaws glue start-job-run --job-name iceberg-customer-table-$GLUE_DB_NAME\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Duration:\u003C/strong\u003E 2-3 minutes\u003C/p\u003E\n","\u003Ch3\u003ERun Sales Table Load Job\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing AWS Console:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect \u003Ccode\u003Eiceberg-sales-table-<db-name>\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ERun\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EMonitor progress (will take longer due to 1M records)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing AWS CLI:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eaws glue start-job-run --job-name iceberg-sales-table-$GLUE_DB_NAME\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Duration:\u003C/strong\u003E 3-5 minutes (processing 1M gzipped records)\u003C/p\u003E\n","\u003Ch3\u003EWait for Both Jobs to Complete\u003C/h3\u003E\n","\u003Cp\u003EBoth jobs must show \u003Cstrong\u003ESUCCEEDED\u003C/strong\u003E status before proceeding.\u003C/p\u003E\n","\u003Ch3\u003EVerify Iceberg Tables in Glue Catalog\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# List tables in the Glue database\naws glue get-tables --database-name $GLUE_DB_NAME \\\n  --query 'TableList[*].[Name,StorageDescriptor.Location]' \\\n  --output table\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E-----------------------------------------------------------\n|                       GetTables                         |\n+--------------------+-----------------------------------+\n|  customers_info    |  s3://my-bucket/iceberg-warehouse/...|\n|  raw_sales_data    |  s3://my-bucket/iceberg-warehouse/...|\n-----------------------------------------------------------\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGenerate Sales Report\u003C/h3\u003E\n","\u003Cp\u003EIn this step, we run the sales report job that performs the same analytics that we'll later replicate in Snowflake.\u003C/p\u003E\n","\u003Ch3\u003ERun Sales Report Job\u003C/h3\u003E\n","\u003Cp\u003EThis job reads from the \u003Ccode\u003Eraw_sales_data\u003C/code\u003E Iceberg table and generates the Top 10 products report.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing AWS Console:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect \u003Ccode\u003Eiceberg-sales-report-<db-name>\u003C/code\u003E job\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ERun\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EMonitor the run (check logs for detailed output)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing AWS CLI:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eaws glue start-job-run --job-name iceberg-sales-report-$GLUE_DB_NAME\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Duration:\u003C/strong\u003E 3-5 minutes\u003C/p\u003E\n","\u003Ch3\u003EWhat the Report Job Does\u003C/h3\u003E\n","\u003Cp\u003EThe sales report script performs these operations:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EReads from \u003Ccode\u003Eraw_sales_data\u003C/code\u003E Iceberg table\u003C/li\u003E\u003Cli\u003EFlattens the nested purchase structure\u003C/li\u003E\u003Cli\u003EAggregates by product_id calculating:\n\u003Cul\u003E\u003Cli\u003ETotal sales amount\u003C/li\u003E\u003Cli\u003ETotal quantity sold\u003C/li\u003E\u003Cli\u003EUnique customers\u003C/li\u003E\u003Cli\u003ETransaction counts\u003C/li\u003E\u003Cli\u003EAverage transaction metrics\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EGenerates Top 10 products by sales amount\u003C/li\u003E\u003Cli\u003ESaves to Iceberg table: \u003Ccode\u003Etop_10_products_report_spark\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ERun Snowflake Report Table Creation Job\u003C/h3\u003E\n","\u003Cp\u003ECreate the placeholder table for the Snowflake-generated report:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eaws glue start-job-run --job-name iceberg-snow-report-$GLUE_DB_NAME\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Duration:\u003C/strong\u003E 1-2 minutes\u003C/p\u003E\n","\u003Ch3\u003EVerify All Report Tables\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eaws glue get-tables --database-name $GLUE_DB_NAME \\\n  --query 'TableList[?starts_with(Name, `top_10`)].Name'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E[\n    "top_10_products_report_spark",\n    "top_10_products_report_snow"\n]\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Snowflake Catalog Integration\u003C/h2\u003E\n","\u003Cp\u003ENow we'll create the Snowflake Catalog Integration to connect to the AWS Glue Data Catalog.\u003C/p\u003E\n","\u003Ch3\u003EUpdate Catalog Integration Parameters\u003C/h3\u003E\n","\u003Cp\u003EIn Cell 4 of the Snowflake notebook, update these parameters:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPlaceholder\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhere to Find\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<Glue database Name>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYour CloudFormation parameter\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<AWS REGION>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYour AWS region (e.g., \u003Ccode\u003Eus-west-2\u003C/code\u003E)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<AWS ACCOUNT NUMBER>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYour AWS account ID\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<AWS ROLE ARN>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFrom CloudFormation outputs\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<External ID>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOptional — see note below\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote on \u003Ccode\u003ESIGV4_EXTERNAL_ID\u003C/code\u003E:\u003C/strong\u003E This is the external ID used to secure the AWS IAM trust relationship for the catalog integration — it is \u003Cstrong\u003Enot\u003C/strong\u003E the same as \u003Ccode\u003ESTORAGE_AWS_EXTERNAL_ID\u003C/code\u003E from your external volume. You can either provide your own string value here (which you then add to your IAM role's trust policy), or omit \u003Ccode\u003ESIGV4_EXTERNAL_ID\u003C/code\u003E entirely and let Snowflake auto-generate one. After creating the integration, run \u003Ccode\u003EDESC CATALOG INTEGRATION glue_rest_cat_int_demo\u003C/code\u003E and copy the \u003Ccode\u003EGLUE_AWS_EXTERNAL_ID\u003C/code\u003E value — that is what you add to the IAM trust policy condition.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CATALOG INTEGRATION glue_rest_cat_int_demo\n  CATALOG_SOURCE = ICEBERG_REST\n  TABLE_FORMAT = ICEBERG\n  CATALOG_NAMESPACE = '<Glue database Name>'\n  REST_CONFIG = (\n    CATALOG_URI = 'https://glue.<AWS REGION>.amazonaws.com/iceberg'\n    CATALOG_API_TYPE = AWS_GLUE\n    CATALOG_NAME = '<AWS ACCOUNT NUMBER>'\n  )\n  REST_AUTHENTICATION = (\n    TYPE = SIGV4\n    SIGV4_IAM_ROLE = '<AWS ROLE ARN>'\n    SIGV4_SIGNING_REGION = '<AWS REGION>'\n    SIGV4_EXTERNAL_ID = '<External ID>'\n  )\n  ENABLED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun Cell 4 to create the catalog integration.\u003C/p\u003E\n","\u003Ch3\u003ECreate Catalog-Linked Database\u003C/h3\u003E\n","\u003Cp\u003EUpdate Cell 6 with your Glue database name:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS glue_lake_int_db\n  LINKED_CATALOG = (\n    CATALOG = 'glue_rest_cat_int_demo',\n    ALLOWED_NAMESPACES = ('<Glue database name>'),\n    NAMESPACE_MODE = FLATTEN_NESTED_NAMESPACE,\n    NAMESPACE_FLATTEN_DELIMITER = '-',\n    SYNC_INTERVAL_SECONDS = 60\n  )\n  EXTERNAL_VOLUME = 'extvol_iceberg_demo';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun Cell 6 to create the catalog-linked database.\u003C/p\u003E\n","\u003Ch3\u003EVerify Catalog Link Status\u003C/h3\u003E\n","\u003Cp\u003ERun Cell 7 to check the sync status:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SYSTEM$CATALOG_LINK_STATUS('glue_lake_int_db');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExpected output should show successful sync status:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{"failureDetails":[],"executionState":"RUNNING","lastLinkAttemptStartTime":"2025-01-27T16:32:42.426Z"}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Tables Are Discovered\u003C/h3\u003E\n","\u003Cp\u003ERun Cell 8 to see the automatically discovered tables:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE glue_lake_int_db;\nUSE SCHEMA "<glue database name>";\nSHOW TABLES;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see all 4 Iceberg tables that were created by Glue:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Ecustomers_info\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eraw_sales_data\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Etop_10_products_report_spark\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Etop_10_products_report_snow\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun Apache Spark code in Snowflake\u003C/h2\u003E\n","\u003Cp\u003EThis is where the magic happens! We'll run the \u003Cstrong\u003Eexact same Spark code\u003C/strong\u003E in Snowflake that we ran in AWS Glue.\u003C/p\u003E\n","\u003Ch3\u003EInstall Snowpark Connect for Apache Spark\u003C/h3\u003E\n","\u003Cp\u003EFirst, create a Snowflake notebook and configure the required packages for Snowpark Connect.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAccess Snowflake Notebooks:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELog into your Snowflake account\u003C/li\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EProjects\u003C/strong\u003E → \u003Cstrong\u003ENotebooks\u003C/strong\u003E in the left sidebar\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Notebook\u003C/strong\u003E button to create a new notebook\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImport the Notebook:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect \u003Cstrong\u003EImport from file\u003C/strong\u003E option\u003C/li\u003E\u003Cli\u003EUpload the file: \u003Ca href=\"https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/snowflake_notebook\"\u003E\u003Ccode\u003Esnowflake_notebook/01_GLUE_LH_SNOWPARK_CONNECT_DEMO.ipynb\u003C/code\u003E\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EGive your notebook a meaningful name (e.g., "AWS Glue Iceberg Integration Demo")\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConfigure Notebook Runtime:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on \u003Cstrong\u003ENotebook settings\u003C/strong\u003E (gear icon)\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ESnowflake Runtime\u003C/strong\u003E: Choose \u003Cstrong\u003E2.0\u003C/strong\u003E or later\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EWarehouse\u003C/strong\u003E: Choose any warehouse for compute (e.g., \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E)\n\u003Cul\u003E\u003Cli\u003ERecommended: Small or Medium warehouse for this demo\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EApply\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAdd Snowpark Connect Package:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the \u003Cstrong\u003EPackages\u003C/strong\u003E tab in the notebook\u003C/li\u003E\u003Cli\u003ESearch for \u003Ccode\u003Esnowpark-connect\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003Elatest version\u003C/strong\u003E of Snowpark Connect\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EAdd\u003C/strong\u003E to install the package\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/packages.jpeg?v=f3d82d45\" alt=\"Snowflake Notebook Packages\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The notebook will initialize with Snowpark Connect capabilities after adding this package.\u003C/p\u003E\n","\u003Ch3\u003EInitialize Snowpark Connect\u003C/h3\u003E\n","\u003Cp\u003ERun Cell 13 to initialize Snowpark Connect:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake import snowpark_connect\nimport traceback\n\nspark = snowpark_connect.server.init_spark_session()\nspark = snowpark_connect.get_session()\n\n# Use lowercase table identifiers (Glue default)\nspark.conf.set("snowpark.connect.sql.identifiers.auto-uppercase", "none")\n\n# Catalog configuration\ncatalog = "GLUE_LAKE_INT_DB"\ndatabase = "<Glue database Name>"  # UPDATE THIS\nsource_sales_table = "raw_sales_data"  \nreport_table = "top_10_products_report_snow"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERead Sales Data (Same as Glue Script)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esales_iceberg_df = spark.table(f"{catalog}.{database}.{source_sales_table}")\nrecord_count = sales_iceberg_df.count()\nprint(f"Sales data loaded successfully from Iceberg table. Total records: {record_count:,}")\nsales_iceberg_df.show(3, truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFlatten and Clean Data (Same as Glue Script)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom pyspark.sql.functions import col\n\nflattened_sales = sales_iceberg_df.select(\n    col("customer_id").alias("customer_id"),\n    col("customer_name").alias("customer_name"),\n    col("purchases.prodid").alias("product_id"),\n    col("purchases.purchase_amount").alias("purchase_amount"),\n    col("purchases.quantity").alias("quantity"),\n    col("purchases.purchase_date").alias("purchase_date")\n)\n\nclean_sales = flattened_sales.filter(\n    col("product_id").isNotNull() & \n    (col("product_id") > 0) & \n    col("purchase_amount").isNotNull() & \n    (col("purchase_amount") > 0) &\n    col("quantity").isNotNull() & \n    (col("quantity") > 0)\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAggregate by Product (Same as Glue Script)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom pyspark.sql.functions import sum as spark_sum, count, avg, max as spark_max, min as spark_min, countDistinct, desc\n\nproduct_sales_summary = clean_sales.groupBy("product_id").agg(\n    spark_sum("purchase_amount").alias("total_sales_amount"),\n    spark_sum("quantity").alias("total_quantity_sold"),\n    count("*").alias("total_transactions"),\n    avg("purchase_amount").alias("avg_transaction_amount"),\n    spark_max("purchase_amount").alias("max_transaction_amount"),\n    spark_min("purchase_amount").alias("min_transaction_amount"),\n    avg("quantity").alias("avg_quantity_per_transaction"),\n    spark_max("quantity").alias("max_quantity_per_transaction"),\n    countDistinct("customer_id").alias("unique_customers"),\n    spark_min("purchase_date").alias("first_sale_date"),\n    spark_max("purchase_date").alias("last_sale_date")\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGenerate Top 10 Report (Same as Glue Script)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etop_10_products = product_sales_summary.orderBy(desc("total_sales_amount")).limit(10)\nprint("\\n=== TOP 10 HIGHEST SELLING PRODUCTS BY SALES AMOUNT ===")\ntop_10_products.show(10, truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESave Results to Iceberg Table\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom pyspark.sql.functions import current_date, lit\nfrom pyspark.sql.window import Window\nfrom pyspark.sql.functions import row_number\n\nsales_window = Window.partitionBy(lit(1)).orderBy(desc("total_sales_amount"))\n\ndetailed_report = product_sales_summary.withColumn(\n    "sales_amount_rank", row_number().over(sales_window)\n).filter(col("sales_amount_rank") <= 10).orderBy("sales_amount_rank")\n\nfinal_report = detailed_report.withColumn("report_date", current_date()) \\\n                             .withColumn("source_table", lit(source_sales_table)) \\\n                             .withColumn("job_name", lit("snowflake_notebook"))\n\n# Truncate and insert\nspark.sql(f"TRUNCATE TABLE {catalog}.{database}.{report_table}")\ntarget_df = spark.read.table(f"{catalog}.{database}.{report_table}")\nfinal_report.select(*target_df.columns).write.insertInto(f"{catalog}.{database}.{report_table}")\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EVerify Code Interoperability\u003C/h2\u003E\n","\u003Cp\u003EBoth AWS Glue and Snowflake have now generated the same Top 10 products report using \u003Cstrong\u003Eidentical Spark code\u003C/strong\u003E.\u003C/p\u003E\n","\u003Ch3\u003ECompare Results\u003C/h3\u003E\n","\u003Cp\u003EQuery both reports to verify they produce identical results:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Get Glue-generated report\nprint("=== GLUE-GENERATED REPORT ===")\nspark.sql(f"SELECT * FROM {catalog}.{database}.top_10_products_report_spark ORDER BY sales_amount_rank").show(10, truncate=False)\n\n# Get Snowflake-generated report\nprint("=== SNOWFLAKE-GENERATED REPORT ===")\nspark.sql(f"SELECT * FROM {catalog}.{database}.{report_table} ORDER BY sales_amount_rank").show(10, truncate=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify in AWS Glue Console\u003C/h3\u003E\n","\u003Cp\u003EGo to \u003Cstrong\u003EAWS Glue Console\u003C/strong\u003E → \u003Cstrong\u003EData Catalog\u003C/strong\u003E → \u003Cstrong\u003ETables\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ECheck the \u003Ccode\u003Etop_10_products_report_snow\u003C/code\u003E table:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIt should now have data (populated by Snowflake)\u003C/li\u003E\u003Cli\u003EMetadata automatically synced back to Glue catalog\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey Observations\u003C/h3\u003E\n","\u003Cp\u003E✅ \u003Cstrong\u003ESame Code\u003C/strong\u003E: 95%+ of the Spark code is identical between platforms\u003Cbr\u003E\n✅ \u003Cstrong\u003ESame Results\u003C/strong\u003E: Both reports show identical Top 10 products\u003Cbr\u003E\n✅ \u003Cstrong\u003ESame Data\u003C/strong\u003E: Both platforms query the same Iceberg files in S3\u003Cbr\u003E\n✅ \u003Cstrong\u003EBi-directional\u003C/strong\u003E: Both platforms can create and modify tables\u003Cbr\u003E\n✅ \u003Cstrong\u003EZero Data Movement\u003C/strong\u003E: No ETL between platforms required\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETroubleshooting\u003C/h2\u003E\n","\u003Ch3\u003ECloudFormation Stack Creation Failed\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EError:\u003C/strong\u003E IAM permissions denied\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESolution:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEnsure your AWS user has permissions to create IAM roles\u003C/li\u003E\u003Cli\u003EAdd \u003Ccode\u003Eiam:CreateRole\u003C/code\u003E, \u003Ccode\u003Eiam:PutRolePolicy\u003C/code\u003E, \u003Ccode\u003Eiam:AttachRolePolicy\u003C/code\u003E permissions\u003C/li\u003E\u003Cli\u003EOr use an admin user for initial deployment\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EError:\u003C/strong\u003E S3 bucket not found\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESolution:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EVerify the bucket exists: \u003Ccode\u003Eaws s3 ls s3://$DATA_BUCKET\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EEnsure bucket name in parameters matches exactly\u003C/li\u003E\u003Cli\u003ECheck bucket is in the same region as CloudFormation stack\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EGlue Job Fails\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EError:\u003C/strong\u003E "Access Denied" to S3 bucket\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESolution:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Check IAM role permissions\naws iam get-role-policy --role-name $GLUE_ROLE_NAME --policy-name GlueIcebergS3Access\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EError:\u003C/strong\u003E "No JSON object could be decoded"\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESolution:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EVerify data files uploaded correctly to S3\u003C/li\u003E\u003Cli\u003ECheck file paths match: \u003Ccode\u003Es3://<bucket>/raw_data/customers.json\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ETables Not Appearing in Snowflake\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EError:\u003C/strong\u003E \u003Ccode\u003ESHOW TABLES\u003C/code\u003E returns empty in catalog-linked database\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESolution:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECheck catalog link status:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SYSTEM$CATALOG_LINK_STATUS('glue_lake_int_db');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003EVerify ALLOWED_NAMESPACES includes your Glue database\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EVerify tables exist in Glue:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eaws glue get-tables --database-name $GLUE_DB_NAME\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EIf tables still don't appear, drop and recreate catalog-linked database\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ESnowpark Connect Not Working\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EError:\u003C/strong\u003E "Module 'snowpark_connect' not found"\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESolution:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEnsure Snowpark Connect package is added in Packages tab\u003C/li\u003E\u003Cli\u003ERestart notebook kernel\u003C/li\u003E\u003Cli\u003ECheck Snowflake Runtime is 2.0 or later\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EExternal ID Mismatch\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EError:\u003C/strong\u003E "External ID mismatch"\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESolution:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe external ID in Catalog Integration must match:\n\u003Cul\u003E\u003Cli\u003EThe External ID from DESC EXTERNAL VOLUME output\u003C/li\u003E\u003Cli\u003EThe SnowflakeExternalId parameter in CloudFormation\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EAll values must be identical\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EFollow these steps to remove all deployed resources.\u003C/p\u003E\n","\u003Ch3\u003EDelete Snowflake Resources\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Drop catalog-linked database\nDROP DATABASE IF EXISTS glue_lake_int_db;\n\n-- Drop catalog integration\nDROP CATALOG INTEGRATION IF EXISTS glue_rest_cat_int_demo;\n\n-- Drop external volume\nDROP EXTERNAL VOLUME IF EXISTS extvol_iceberg_demo;\n\n-- Drop demo database\nDROP DATABASE IF EXISTS ICEBERG_LAKE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDelete AWS CloudFormation Stack\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Delete stack (will remove all Glue jobs, database, IAM role)\naws cloudformation delete-stack --stack-name iceberg-glue-snowflake-demo\n\n# Wait for deletion to complete\naws cloudformation wait stack-delete-complete --stack-name iceberg-glue-snowflake-demo\n\n# Verify deletion\naws cloudformation describe-stacks --stack-name iceberg-glue-snowflake-demo\n# Should return error: "Stack does not exist"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDelete S3 Data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Delete all Iceberg data\naws s3 rm s3://$DATA_BUCKET/iceberg-warehouse/ --recursive\n\n# Delete scripts and raw data\naws s3 rm s3://$DATA_BUCKET/scripts/ --recursive\naws s3 rm s3://$DATA_BUCKET/raw_data/ --recursive\n\n# Optional: Delete the bucket itself\naws s3 rb s3://$DATA_BUCKET --force\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Cleanup\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Check Glue database is gone\naws glue get-database --name $GLUE_DB_NAME\n# Should return error\n\n# Check S3 bucket\naws s3 ls s3://$DATA_BUCKET\n# Should be empty or not exist\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EContinue Building with Snowflake CoCo\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. It can guide you through setting up, verifying, and troubleshooting your Glue catalog integration interactively, without switching between AWS console tabs and documentation.\u003C/p\u003E\n","\u003Ch3\u003ESet up or troubleshoot your Glue catalog integration\u003C/h3\u003E\n","\u003Cp\u003EUse these prompts to get started. Snowflake CoCo will ask for your AWS account ID, region, IAM role, and access delegation preference — then generate and execute the SQL, retrieve the Snowflake IAM user ARN, and walk you through the trust policy update.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate a new catalog integration:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EHelp me create a catalog integration for AWS Glue Iceberg REST\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVerify an existing integration is working:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EVerify my Glue catalog integration and list the namespaces and tables it can see\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETroubleshoot a broken integration:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EMy Glue catalog integration is failing — help me diagnose and fix it\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESet up a Catalog-Linked Database after the integration is ready:\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\u003Cstrong\u003ERun Spark code against your Glue-managed Iceberg tables using Snowpark Connect:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EHelp me set up Snowpark Connect for Apache Spark to read and write Iceberg tables managed by AWS Glue\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EYou've successfully built an interoperable lakehouse architecture that demonstrates true code portability between AWS Glue and Snowflake using Apache Iceberg.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create Iceberg tables using AWS Glue ETL jobs\u003C/li\u003E\u003Cli\u003EHow to configure Snowflake External Volume and Catalog Integration with AWS Glue\u003C/li\u003E\u003Cli\u003EHow Snowflake Catalog-Linked Databases automatically discover and sync Iceberg tables\u003C/li\u003E\u003Cli\u003EHow Snowpark Connect enables identical Spark code to run on both platforms\u003C/li\u003E\u003Cli\u003EHow to achieve zero data movement with bi-directional read/write capabilities\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main\"\u003EGitHub Repository for this Quickstart\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 Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-overview\"\u003ESnowpark Connect Overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://iceberg.apache.org/\"\u003EApache Iceberg Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.aws.amazon.com/glue/\"\u003EAWS Glue Developer Guide\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 Support\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 Catalog Integration for AWS Glue Iceberg REST\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build an interoperable lakehouse architecture using Apache Iceberg with AWS Glue, Catalog-Linked Databases, and Snowpark Connect for true code portability.","title":"Interoperable Lakehouse with AWS Glue, Snowflake Iceberg Tables, and Snowpark Connect for Apache Spark","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. With Apache Iceberg's broad adoption across major data platforms, it is designed for vendor-neutral interoperability. You can write data once and query it from any compatible engine—no proprietary formats, no data migration required. \n\nThis guide demonstrates how to build a modern, interoperable lakehouse architecture that seamlessly integrates **AWS Glue**, **Apache Iceberg**, and **Snowflake** to enable data processing workloads across multiple compute engines while maintaining a single source of truth.\n\nThe solution showcases how organizations can leverage best-of-breed tools without vendor lock-in, allowing Spark code written for AWS Glue to run unmodified on Snowflake using [Snowpark Connect](https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-overview).\n\n![Lakehouse Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/arch.jpeg?v=f3d82d45)\n\nYour organization's data lives everywhere -- across tools, platforms, and cloud providers. If you use both AWS and Snowflake, you can build a connected and interoperable architecture that focuses on getting value from your data without costly and complex data movement.\n\nFor this guide, we will use a Sales Analytics use case where 1M+ transaction records are processed. We'll load sales and customer data into Iceberg tables via AWS Glue, then demonstrate how the exact same Spark code can run on both AWS Glue and Snowflake to generate identical Top 10 product sales reports.\n\n### Prerequisites\n- Familiarity with Snowflake, basic SQL, Snowsight UI, and Snowflake Objects\n- Familiarity with AWS Services (S3, Glue, CloudFormation, IAM) and the Management Console\n- Basic knowledge of Python and PySpark\n\n### What You'll Learn \n- How to create Iceberg tables in the [AWS Glue Data Catalog](https://docs.aws.amazon.com/prescriptive-guidance/latest/serverless-etl-aws-glue/aws-glue-data-catalog.html) using Glue ETL jobs\n- How to configure a [Snowflake Catalog-Linked Database](https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database) with the Glue Data Catalog via Iceberg REST Catalog APIs\n- How to use [Snowpark Connect](https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-overview) to run identical Spark code on Snowflake\n- How to achieve true code portability between AWS Glue and Snowflake with zero data movement\n\n### What You'll Need \n- A [Snowflake Account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=interoperable_lakehouse_glue_iceberg) with `ACCOUNTADMIN` access (Enterprise Edition or higher recommended)\n- An [AWS Account](https://aws.amazon.com/free/) with administrative access\n- [AWS CLI](https://aws.amazon.com/cli/) installed and configured (optional, for command-line deployment)\n- Snowflake Notebook feature enabled (requires Snowflake Runtime 2.0+)\n- The [accompanying GitHub repository](https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main) cloned or downloaded to your local machine\n\n### What You'll Build \n- An AWS Glue Database with Iceberg tables stored on S3\n- CloudFormation-deployed infrastructure including IAM roles and Glue ETL jobs\n- Snowflake External Volume and Catalog Integration with AWS Glue\n- Snowflake Catalog-Linked Database for automatic table discovery\n- Sales analytics pipeline demonstrating 95%+ code reuse between platforms\n\n### Setup\n\nThis guide has an accompanying GitHub repository that contains all the files, scripts, data, and resources you need to complete this quickstart:\n\n\u003Cbutton\u003E\n\n  [GitHub Repository: glue_cld_snowflake](https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main)\n\u003C/button\u003E\n\n**Repository Contents:**\n```\nglue_cld_snowflake/\n├── cloudformation/\n│   └── iceberg_glue_stack_cloudformation_template.yaml  # AWS infrastructure template\n├── data/\n│   ├── customers.json                                    # Customer data\n│   └── sales_data_1m.json.gz                            # 1M sales records (compressed)\n├── scripts/\n│   ├── iceberg_cld_demo_sales_table_script_1.py         # Load sales data\n│   ├── iceberg_cld_demo_customer_table_script_2.py      # Load customer data\n│   ├── iceberg_cld_demo_sales_report_table_script_3.py  # Generate sales report\n│   └── iceberg_cld_demo_snow_report_table_script_4.py   # Create Snowflake report table\n├── snowflake_notebook/\n│   └── 01_GLUE_LH_SNOWPARK_CONNECT_DEMO.ipynb           # Snowflake notebook\n├── diagrams/                                             # Architecture diagrams\n└── trusted_policy/\n    └── IAM_trust_policy.json                             # IAM trust policy template\n```\n\nClone or download this repository before starting the quickstart to have all necessary files readily available.\n\n\u003C!-- ------------------------ --\u003E\n## Architecture and Data Flow\n\nThis solution implements an **open lakehouse architecture** that separates storage from compute and uses open standards (Apache Iceberg) to enable seamless interoperability.\n\n### Key Components\n\n**1. AWS Glue with Apache Iceberg**\n- Infrastructure as Code via CloudFormation template\n- Glue Database for Iceberg table metadata\n- 4 pre-configured Glue ETL jobs (Glue 4.0 with Spark 3.5)\n- IAM roles with appropriate S3 and Glue permissions\n\n**2. Snowflake Catalog-Linked Database**\n- Automatically syncs with AWS Glue Data Catalog\n- Discovers Iceberg tables without manual configuration\n- Supports both read and write operations to the remote catalog\n- Sync interval configurable (default 60 seconds)\n\n**3. Snowpark Connect**\n- Apache Spark DataFrame API implementation for Snowflake\n- Enables existing Spark applications to run unchanged\n- Provides Spark-compatible API with Snowflake's optimized query engine\n\n### Data Flow Diagram\n\n```\n┌─────────────────────────────────────────────────────────────────┐\n│                     S3 Data Bucket                              │\n│  • raw_data/sales_data_1m.json.gz (1M records)                 │\n│  • raw_data/customers.json                                      │\n│  • iceberg-warehouse/ (Iceberg table storage)                   │\n└────────────┬──────────────────────────────────┬─────────────────┘\n             │                                  │\n             ▼                                  ▼\n┌─────────────────────────┐        ┌──────────────────────────────┐\n│   AWS Glue ETL Jobs     │        │  AWS Glue Data Catalog       │\n│  (Spark on AWS)         │◄──────►│  (Iceberg REST Catalog)      │\n│                         │        │  • Metadata for all tables   │\n│  Script 1: Load Sales   │        │  • Schema definitions        │\n│  Script 2: Load Customers│       │  • Partition info            │\n│  Script 3: Sales Report │        └──────────┬───────────────────┘\n│  Script 4: Snow Report  │                   │\n└─────────────────────────┘                   │ Iceberg REST API\n                                              │ (Catalog Integration)\n                                              ▼\n                                   ┌──────────────────────────────┐\n                                   │ Snowflake Catalog-Linked DB  │\n                                   │  (Snowpark Connect)          │\n                                   │                              │\n                                   │  • Auto-discovers tables     │\n                                   │  • Notebook with Spark code  │\n                                   │  • Generates same report     │\n                                   │  • Zero data movement        │\n                                   └──────────────────────────────┘\n```\n\n\u003C!-- ------------------------ --\u003E\n## Upload Data into S3\n\nIn this step, we will create an S3 bucket and upload the data files and Glue scripts needed for the demo.\n\n### Create S3 Bucket\n\n- Navigate to AWS S3 Console and create a new bucket\n- Choose a unique name (e.g., `my-iceberg-lakehouse-demo-\u003Cyour-account-id\u003E`)\n- Select the same region as your Snowflake account (to minimize egress charges)\n- Keep default settings for encryption and versioning\n\n```bash\n# Option: Using AWS CLI\nexport DATA_BUCKET=my-iceberg-lakehouse-demo-\u003Cyour-account-id\u003E\nexport AWS_REGION=us-west-2  # Match with your Snowflake region\n\naws s3 mb s3://$DATA_BUCKET --region $AWS_REGION\n```\n\n### Create Folder Structure\n\nCreate the required folders in your bucket:\n- `raw_data/` - for source data files\n- `scripts/` - for Glue ETL scripts\n- `iceberg-warehouse/` - for Iceberg table storage\n\n```bash\n# Create folder structure\naws s3api put-object --bucket $DATA_BUCKET --key raw_data/\naws s3api put-object --bucket $DATA_BUCKET --key scripts/\naws s3api put-object --bucket $DATA_BUCKET --key iceberg-warehouse/\n```\n\n### Upload Data Files\n\nUpload the data files from the [`data/` folder](https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/data) in the repository:\n\n```bash\n# Navigate to the cloned repository directory\n# (https://github.com/sfc-gh-pjain/glue_cld_snowflake)\ncd glue_cld_snowflake\n\n# Upload data files from the cloned repository\n# (https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/data)\naws s3 cp data/customers.json s3://$DATA_BUCKET/raw_data/customers.json\naws s3 cp data/sales_data_1m.json.gz s3://$DATA_BUCKET/raw_data/sales_data_1m.json.gz\n\n# Verify upload\naws s3 ls s3://$DATA_BUCKET/raw_data/\n```\n\n![Raw Data Folder](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/raw.jpeg?v=f3d82d45)\n\n### Upload Glue Scripts\n\nUpload all Glue ETL scripts from the [`scripts/` folder](https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/scripts) in the repository:\n\n```bash\n# Upload all Glue scripts from the cloned repository\n# (https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/scripts)\naws s3 sync scripts/ s3://$DATA_BUCKET/scripts/ --exclude \"*\" --include \"*.py\"\n\n# Verify scripts upload\naws s3 ls s3://$DATA_BUCKET/scripts/\n```\n\n![Scripts Folder](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/scripts.jpeg?v=f3d82d45)\n\n**Expected Output:**\n```\niceberg_cld_demo_customer_table_script_2.py\niceberg_cld_demo_sales_table_script_1.py\niceberg_cld_demo_sales_report_table_script_3.py\niceberg_cld_demo_snow_report_table_script_4.py\n```\n\n\u003C!-- ------------------------ --\u003E\n## Create Snowflake External Volume\n\nIn this step, we will create the Snowflake database and External Volume to enable S3 access.\n\n### Run Initial Setup Cells\n\nRun the first cells of the Snowflake notebook to create the database and external volume.\n\n**Cell 0: Create Database and Schema**\n```sql\nCREATE OR REPLACE DATABASE ICEBERG_LAKE;\nCREATE OR REPLACE SCHEMA ICEBERG_LAKE.DEMO;\nUSE SCHEMA ICEBERG_LAKE.DEMO;\n```\n\n**Cell 2: Create External Volume**\n\nUpdate the following parameters in Cell 2 before running:\n\n| Placeholder | Replace With |\n|-------------|--------------|\n| `\u003CYour dataBucket name\u003E` | Your S3 bucket name from Step 1 |\n| `\u003CAWS ROLE ARN\u003E` | Leave as placeholder (update after CloudFormation) |\n| `\u003CAny secret word you want\u003E` | Choose a secret external ID (e.g., `my_iceberg_ext_id_2024`) |\n\n```sql\nCREATE OR REPLACE EXTERNAL VOLUME extvol_iceberg_demo\nSTORAGE_LOCATIONS =\n      (\n         (\n            NAME = 'Iceberg-Table-Demo'\n            STORAGE_PROVIDER = 'S3'\n            STORAGE_BASE_URL = 's3://\u003CYour dataBucket name\u003E/'\n            STORAGE_AWS_ROLE_ARN = '\u003CAWS ROLE ARN\u003E'\n            STORAGE_AWS_EXTERNAL_ID = '\u003CAny secret word you want\u003E'\n         )\n      );\n```\n\n**Important:** Save the External ID you choose - you'll need it for CloudFormation deployment.\n\n### Get Snowflake Credentials\n\nRun Cell 3 to retrieve the Snowflake-generated IAM User ARN and External ID:\n\n```sql\nDESC EXTERNAL VOLUME extvol_iceberg_demo;\n\nSELECT b.KEY, b.VALUE \nFROM \n    TABLE(RESULT_SCAN(LAST_QUERY_ID())) a, \n    TABLE(FLATTEN(INPUT =\u003E PARSE_JSON(a.\"property_value\"))) b \nWHERE \n    a.\"parent_property\" = 'STORAGE_LOCATIONS'\n    AND a.\"property\" = 'STORAGE_LOCATION_1'\n    AND (b.KEY='STORAGE_AWS_EXTERNAL_ID' OR b.KEY ='STORAGE_AWS_IAM_USER_ARN');\n```\n\n**Output:**\n```\nKEY                          | VALUE\n-----------------------------+----------------------------------------\nSTORAGE_AWS_IAM_USER_ARN     | arn:aws:iam::123456789012:user/abc...\nSTORAGE_AWS_EXTERNAL_ID      | XYZ12345_SFCRole=1_AbCdEfGh...\n```\n\n**IMPORTANT: Copy and save these values!**\n- `STORAGE_AWS_IAM_USER_ARN` → Use as **SnowflakeUserArn** in CloudFormation\n- `STORAGE_AWS_EXTERNAL_ID` → Use as **SnowflakeExternalId** in CloudFormation\n\n\u003C!-- ------------------------ --\u003E\n## Deploy AWS CloudFormation Stack\n\nIn this step, we will deploy the CloudFormation template that creates all AWS infrastructure including the Glue database, IAM role, and ETL jobs.\n\n### Prepare CloudFormation Parameters\n\nGather these parameters before deploying:\n\n| Parameter Name | Description | Example Value |\n|----------------|-------------|---------------|\n| **GlueDatabaseName** | Name for Glue database | `snowcldtest` |\n| **GlueRoleName** | Name for IAM role | `snowcldtest` |\n| **DataBucketName** | S3 bucket from Step 1 | `snowcldtestbucket` |\n| **S3ScriptLocation** | Path to any script | `s3://snowcldtestbucket/scripts/iceberg_cld_demo_sales_table_script_1.py` |\n| **SnowflakeUserArn** | From Step 4 | `arn:aws:iam::123456789012:user/abc...` |\n| **SnowflakeExternalId** | From Step 4 | `XYZ12345_SFCRole=1_...` |\n\n### Deploy via AWS Console\n\n- Navigate to **AWS CloudFormation Console**\n- Click **Create stack** → **With new resources**\n- Choose **Upload a template file**\n- Upload: [`cloudformation/iceberg_glue_stack_cloudformation_template.yaml`](https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/cloudformation)\n- Click **Next**\n\n![CloudFormation Template](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/cloudform.jpeg?v=f3d82d45)\n\n### Enter Stack Parameters\n\n- **Stack name**: `iceberg-glue-snowflake-demo`\n- Fill in all parameters from the table above\n- Click **Next**\n\n### Configure Stack Options\n\n- **Tags** (optional): Add tags for cost tracking\n- **Permissions**: Leave as default\n- **Stack failure options**: Select **Delete newly created resources during rollback**\n- Click **Next**\n\n### Review and Deploy\n\n- Review all parameters\n- Check the box: **\"I acknowledge that AWS CloudFormation might create IAM resources with custom names\"**\n- Click **Submit**\n\n### Monitor Stack Creation\n\n- Wait for stack status to change to **CREATE_COMPLETE** (typically 2-5 minutes)\n- Monitor the **Events** tab for progress\n- If any errors occur, check the **Events** tab for details\n\n### Deploy via AWS CLI (Alternative)\n\n```bash\n# Set variables\nexport GLUE_DB_NAME=iceberg_demo_db\nexport GLUE_ROLE_NAME=GlueIcebergRole\nexport DATA_BUCKET=my-iceberg-lakehouse-demo-123456789012\nexport SNOWFLAKE_USER_ARN=\"arn:aws:iam::123456789012:user/abc...\"\nexport SNOWFLAKE_EXT_ID=\"XYZ12345_SFCRole=1_...\"\n\n# Deploy stack using CloudFormation template from the cloned repository\n# (https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/cloudformation)\naws cloudformation create-stack \\\n  --stack-name iceberg-glue-snowflake-demo \\\n  --template-body file://cloudformation/iceberg_glue_stack_cloudformation_template.yaml \\\n  --capabilities CAPABILITY_NAMED_IAM \\\n  --on-failure DELETE \\\n  --parameters \\\n      ParameterKey=GlueDatabaseName,ParameterValue=$GLUE_DB_NAME \\\n      ParameterKey=GlueRoleName,ParameterValue=$GLUE_ROLE_NAME \\\n      ParameterKey=DataBucketName,ParameterValue=$DATA_BUCKET \\\n      ParameterKey=S3ScriptLocation,ParameterValue=s3://$DATA_BUCKET/scripts/iceberg_cld_demo_sales_table_script_1.py \\\n      ParameterKey=SnowflakeUserArn,ParameterValue=\"$SNOWFLAKE_USER_ARN\" \\\n      ParameterKey=SnowflakeExternalId,ParameterValue=\"$SNOWFLAKE_EXT_ID\"\n\n# Check stack status\naws cloudformation describe-stacks \\\n  --stack-name iceberg-glue-snowflake-demo \\\n  --query 'Stacks[0].StackStatus'\n```\n\nAfter CloudFormation completes, retrieve the IAM Role ARN and update the Snowflake notebook.\n\n### Get the Glue IAM Role ARN\n\n**Using AWS Console:**\n- Go to CloudFormation → Stacks → `iceberg-glue-snowflake-demo` → **Outputs** tab\n- Look for \"GlueRole\" key → Copy the role name\n\n**Using AWS CLI:**\n```bash\naws cloudformation describe-stacks \\\n  --stack-name iceberg-glue-snowflake-demo \\\n  --query 'Stacks[0].Outputs[?OutputKey==`GlueRole`].OutputValue' \\\n  --output text\n```\n\n### Construct Full Role ARN\n\n```\narn:aws:iam::\u003CYOUR-AWS-ACCOUNT-ID\u003E:role/\u003CGlueRoleName\u003E\n```\n\nExample:\n```\narn:aws:iam::123456789012:role/GlueIcebergRole\n```\n\n### Update Snowflake Notebook Parameters\n\nGo back to your Snowflake notebook and update the AWS Role ARN in Cell 2:\n\n```sql\nCREATE OR REPLACE EXTERNAL VOLUME extvol_iceberg_demo\nSTORAGE_LOCATIONS = (\n    (\n        NAME = 'Iceberg-Table-Demo'\n        STORAGE_PROVIDER = 'S3'\n        STORAGE_BASE_URL = 's3://\u003CYour dataBucket name\u003E/'\n        STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::\u003Caccount-id\u003E:role/\u003Crole-name\u003E'  -- UPDATE THIS\n        STORAGE_AWS_EXTERNAL_ID = '\u003Cyour-external-id\u003E'\n    )\n);\n```\n\nRe-run Cell 2 to recreate the external volume with the correct IAM role.\n\n### Verify AWS Glue Resources\n\nBefore running the ETL jobs, verify that CloudFormation created all required resources.\n\n### Check Glue Database\n\n```bash\n# Using AWS CLI\naws glue get-database --name $GLUE_DB_NAME\n\n# Using AWS Console\n# Navigate to: AWS Glue → Data Catalog → Databases\n# Verify your database exists\n```\n\n### Check Glue ETL Jobs\n\nNavigate to **AWS Glue Console** → **ETL Jobs**\n\nYou should see **4 Glue jobs** created:\n\n| Job Name | Purpose |\n|----------|---------|\n| `iceberg-sales-table-\u003Cdb-name\u003E` | Load 1M sales records to Iceberg |\n| `iceberg-customer-table-\u003Cdb-name\u003E` | Load customer data to Iceberg |\n| `iceberg-sales-report-\u003Cdb-name\u003E` | Generate Top 10 products report (Spark) |\n| `iceberg-snow-report-\u003Cdb-name\u003E` | Create placeholder table for Snowflake |\n\n![Glue ETL Jobs](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/glue.jpeg?v=f3d82d45)\n\n### Verify Job Configuration\n\nEach job should have:\n- **Script location**: `s3://\u003Cyour-bucket\u003E/scripts/iceberg_cld_demo_*.py`\n- **IAM role**: The role created by CloudFormation\n- **Glue version**: 4.0\n- **Worker type**: G.1X\n- **Number of workers**: 2\n\n\u003C!-- ------------------------ --\u003E\n## Run Glue ETL Jobs\n\nIn this step, we will run the Glue ETL jobs to load data into Iceberg tables.\n\n### Run Customer Table Load Job\n\n**Using AWS Console:**\n- Go to **AWS Glue** → **ETL Jobs**\n- Select `iceberg-customer-table-\u003Cdb-name\u003E`\n- Click **Run** button\n- Monitor the **Runs** tab for status\n\n**Using AWS CLI:**\n```bash\naws glue start-job-run --job-name iceberg-customer-table-$GLUE_DB_NAME\n```\n\n**Expected Duration:** 2-3 minutes\n\n### Run Sales Table Load Job\n\n**Using AWS Console:**\n- Select `iceberg-sales-table-\u003Cdb-name\u003E`\n- Click **Run**\n- Monitor progress (will take longer due to 1M records)\n\n**Using AWS CLI:**\n```bash\naws glue start-job-run --job-name iceberg-sales-table-$GLUE_DB_NAME\n```\n\n**Expected Duration:** 3-5 minutes (processing 1M gzipped records)\n\n### Wait for Both Jobs to Complete\n\nBoth jobs must show **SUCCEEDED** status before proceeding.\n\n### Verify Iceberg Tables in Glue Catalog\n\n```bash\n# List tables in the Glue database\naws glue get-tables --database-name $GLUE_DB_NAME \\\n  --query 'TableList[*].[Name,StorageDescriptor.Location]' \\\n  --output table\n```\n\n**Expected Output:**\n```\n-----------------------------------------------------------\n|                       GetTables                         |\n+--------------------+-----------------------------------+\n|  customers_info    |  s3://my-bucket/iceberg-warehouse/...|\n|  raw_sales_data    |  s3://my-bucket/iceberg-warehouse/...|\n-----------------------------------------------------------\n```\n\n### Generate Sales Report\n\nIn this step, we run the sales report job that performs the same analytics that we'll later replicate in Snowflake.\n\n### Run Sales Report Job\n\nThis job reads from the `raw_sales_data` Iceberg table and generates the Top 10 products report.\n\n**Using AWS Console:**\n- Select `iceberg-sales-report-\u003Cdb-name\u003E` job\n- Click **Run**\n- Monitor the run (check logs for detailed output)\n\n**Using AWS CLI:**\n```bash\naws glue start-job-run --job-name iceberg-sales-report-$GLUE_DB_NAME\n```\n\n**Expected Duration:** 3-5 minutes\n\n### What the Report Job Does\n\nThe sales report script performs these operations:\n1. Reads from `raw_sales_data` Iceberg table\n2. Flattens the nested purchase structure\n3. Aggregates by product_id calculating:\n   - Total sales amount\n   - Total quantity sold\n   - Unique customers\n   - Transaction counts\n   - Average transaction metrics\n4. Generates Top 10 products by sales amount\n5. Saves to Iceberg table: `top_10_products_report_spark`\n\n### Run Snowflake Report Table Creation Job\n\nCreate the placeholder table for the Snowflake-generated report:\n\n```bash\naws glue start-job-run --job-name iceberg-snow-report-$GLUE_DB_NAME\n```\n\n**Expected Duration:** 1-2 minutes\n\n### Verify All Report Tables\n\n```bash\naws glue get-tables --database-name $GLUE_DB_NAME \\\n  --query 'TableList[?starts_with(Name, `top_10`)].Name'\n```\n\n**Expected Output:**\n```json\n[\n    \"top_10_products_report_spark\",\n    \"top_10_products_report_snow\"\n]\n```\n\n\u003C!-- ------------------------ --\u003E\n## Create Snowflake Catalog Integration\n\nNow we'll create the Snowflake Catalog Integration to connect to the AWS Glue Data Catalog.\n\n### Update Catalog Integration Parameters\n\nIn Cell 4 of the Snowflake notebook, update these parameters:\n\n| Placeholder | Where to Find |\n|-------------|---------------|\n| `\u003CGlue database Name\u003E` | Your CloudFormation parameter |\n| `\u003CAWS REGION\u003E` | Your AWS region (e.g., `us-west-2`) |\n| `\u003CAWS ACCOUNT NUMBER\u003E` | Your AWS account ID |\n| `\u003CAWS ROLE ARN\u003E` | From CloudFormation outputs |\n| `\u003CExternal ID\u003E` | Optional — see note below |\n\n\u003E **Note on `SIGV4_EXTERNAL_ID`:** This is the external ID used to secure the AWS IAM trust relationship for the catalog integration — it is **not** the same as `STORAGE_AWS_EXTERNAL_ID` from your external volume. You can either provide your own string value here (which you then add to your IAM role's trust policy), or omit `SIGV4_EXTERNAL_ID` entirely and let Snowflake auto-generate one. After creating the integration, run `DESC CATALOG INTEGRATION glue_rest_cat_int_demo` and copy the `GLUE_AWS_EXTERNAL_ID` value — that is what you add to the IAM trust policy condition.\n\n```sql\nCREATE OR REPLACE CATALOG INTEGRATION glue_rest_cat_int_demo\n  CATALOG_SOURCE = ICEBERG_REST\n  TABLE_FORMAT = ICEBERG\n  CATALOG_NAMESPACE = '\u003CGlue database Name\u003E'\n  REST_CONFIG = (\n    CATALOG_URI = 'https://glue.\u003CAWS REGION\u003E.amazonaws.com/iceberg'\n    CATALOG_API_TYPE = AWS_GLUE\n    CATALOG_NAME = '\u003CAWS ACCOUNT NUMBER\u003E'\n  )\n  REST_AUTHENTICATION = (\n    TYPE = SIGV4\n    SIGV4_IAM_ROLE = '\u003CAWS ROLE ARN\u003E'\n    SIGV4_SIGNING_REGION = '\u003CAWS REGION\u003E'\n    SIGV4_EXTERNAL_ID = '\u003CExternal ID\u003E'\n  )\n  ENABLED = TRUE;\n```\n\nRun Cell 4 to create the catalog integration.\n\n### Create Catalog-Linked Database\n\nUpdate Cell 6 with your Glue database name:\n\n```sql\nCREATE DATABASE IF NOT EXISTS glue_lake_int_db\n  LINKED_CATALOG = (\n    CATALOG = 'glue_rest_cat_int_demo',\n    ALLOWED_NAMESPACES = ('\u003CGlue database name\u003E'),\n    NAMESPACE_MODE = FLATTEN_NESTED_NAMESPACE,\n    NAMESPACE_FLATTEN_DELIMITER = '-',\n    SYNC_INTERVAL_SECONDS = 60\n  )\n  EXTERNAL_VOLUME = 'extvol_iceberg_demo';\n```\n\nRun Cell 6 to create the catalog-linked database.\n\n### Verify Catalog Link Status\n\nRun Cell 7 to check the sync status:\n\n```sql\nSELECT SYSTEM$CATALOG_LINK_STATUS('glue_lake_int_db');\n```\n\nExpected output should show successful sync status:\n```json\n{\"failureDetails\":[],\"executionState\":\"RUNNING\",\"lastLinkAttemptStartTime\":\"2025-01-27T16:32:42.426Z\"}\n```\n\n### Verify Tables Are Discovered\n\nRun Cell 8 to see the automatically discovered tables:\n\n```sql\nUSE DATABASE glue_lake_int_db;\nUSE SCHEMA \"\u003Cglue database name\u003E\";\nSHOW TABLES;\n```\n\nYou should see all 4 Iceberg tables that were created by Glue:\n- `customers_info`\n- `raw_sales_data`\n- `top_10_products_report_spark`\n- `top_10_products_report_snow`\n\n\u003C!-- ------------------------ --\u003E\n## Run Apache Spark code in Snowflake\n\nThis is where the magic happens! We'll run the **exact same Spark code** in Snowflake that we ran in AWS Glue.\n\n### Install Snowpark Connect for Apache Spark\n\nFirst, create a Snowflake notebook and configure the required packages for Snowpark Connect.\n\n**Access Snowflake Notebooks:**\n- Log into your Snowflake account\n- Navigate to **Projects** → **Notebooks** in the left sidebar\n- Click **+ Notebook** button to create a new notebook\n\n**Import the Notebook:**\n- Select **Import from file** option\n- Upload the file: [`snowflake_notebook/01_GLUE_LH_SNOWPARK_CONNECT_DEMO.ipynb`](https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main/snowflake_notebook)\n- Give your notebook a meaningful name (e.g., \"AWS Glue Iceberg Integration Demo\")\n\n**Configure Notebook Runtime:**\n- Click on **Notebook settings** (gear icon)\n- Select **Snowflake Runtime**: Choose **2.0** or later\n- Select **Warehouse**: Choose any warehouse for compute (e.g., `COMPUTE_WH`)\n  - Recommended: Small or Medium warehouse for this demo\n- Click **Apply**\n\n**Add Snowpark Connect Package:**\n- Click on the **Packages** tab in the notebook\n- Search for `snowpark-connect`\n- Select the **latest version** of Snowpark Connect\n- Click **Add** to install the package\n\n![Snowflake Notebook Packages](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interoperable-lakehouse-with-aws-glue-snowflake-iceberg-tables-and-snowpark-connect-for-apache-spark/packages.jpeg?v=f3d82d45)\n\n**Note:** The notebook will initialize with Snowpark Connect capabilities after adding this package.\n\n### Initialize Snowpark Connect\n\nRun Cell 13 to initialize Snowpark Connect:\n\n```python\nfrom snowflake import snowpark_connect\nimport traceback\n\nspark = snowpark_connect.server.init_spark_session()\nspark = snowpark_connect.get_session()\n\n# Use lowercase table identifiers (Glue default)\nspark.conf.set(\"snowpark.connect.sql.identifiers.auto-uppercase\", \"none\")\n\n# Catalog configuration\ncatalog = \"GLUE_LAKE_INT_DB\"\ndatabase = \"\u003CGlue database Name\u003E\"  # UPDATE THIS\nsource_sales_table = \"raw_sales_data\"  \nreport_table = \"top_10_products_report_snow\"\n```\n\n### Read Sales Data (Same as Glue Script)\n\n```python\nsales_iceberg_df = spark.table(f\"{catalog}.{database}.{source_sales_table}\")\nrecord_count = sales_iceberg_df.count()\nprint(f\"Sales data loaded successfully from Iceberg table. Total records: {record_count:,}\")\nsales_iceberg_df.show(3, truncate=False)\n```\n\n### Flatten and Clean Data (Same as Glue Script)\n\n```python\nfrom pyspark.sql.functions import col\n\nflattened_sales = sales_iceberg_df.select(\n    col(\"customer_id\").alias(\"customer_id\"),\n    col(\"customer_name\").alias(\"customer_name\"),\n    col(\"purchases.prodid\").alias(\"product_id\"),\n    col(\"purchases.purchase_amount\").alias(\"purchase_amount\"),\n    col(\"purchases.quantity\").alias(\"quantity\"),\n    col(\"purchases.purchase_date\").alias(\"purchase_date\")\n)\n\nclean_sales = flattened_sales.filter(\n    col(\"product_id\").isNotNull() & \n    (col(\"product_id\") \u003E 0) & \n    col(\"purchase_amount\").isNotNull() & \n    (col(\"purchase_amount\") \u003E 0) &\n    col(\"quantity\").isNotNull() & \n    (col(\"quantity\") \u003E 0)\n)\n```\n\n### Aggregate by Product (Same as Glue Script)\n\n```python\nfrom pyspark.sql.functions import sum as spark_sum, count, avg, max as spark_max, min as spark_min, countDistinct, desc\n\nproduct_sales_summary = clean_sales.groupBy(\"product_id\").agg(\n    spark_sum(\"purchase_amount\").alias(\"total_sales_amount\"),\n    spark_sum(\"quantity\").alias(\"total_quantity_sold\"),\n    count(\"*\").alias(\"total_transactions\"),\n    avg(\"purchase_amount\").alias(\"avg_transaction_amount\"),\n    spark_max(\"purchase_amount\").alias(\"max_transaction_amount\"),\n    spark_min(\"purchase_amount\").alias(\"min_transaction_amount\"),\n    avg(\"quantity\").alias(\"avg_quantity_per_transaction\"),\n    spark_max(\"quantity\").alias(\"max_quantity_per_transaction\"),\n    countDistinct(\"customer_id\").alias(\"unique_customers\"),\n    spark_min(\"purchase_date\").alias(\"first_sale_date\"),\n    spark_max(\"purchase_date\").alias(\"last_sale_date\")\n)\n```\n\n### Generate Top 10 Report (Same as Glue Script)\n\n```python\ntop_10_products = product_sales_summary.orderBy(desc(\"total_sales_amount\")).limit(10)\nprint(\"\\n=== TOP 10 HIGHEST SELLING PRODUCTS BY SALES AMOUNT ===\")\ntop_10_products.show(10, truncate=False)\n```\n\n### Save Results to Iceberg Table\n\n```python\nfrom pyspark.sql.functions import current_date, lit\nfrom pyspark.sql.window import Window\nfrom pyspark.sql.functions import row_number\n\nsales_window = Window.partitionBy(lit(1)).orderBy(desc(\"total_sales_amount\"))\n\ndetailed_report = product_sales_summary.withColumn(\n    \"sales_amount_rank\", row_number().over(sales_window)\n).filter(col(\"sales_amount_rank\") \u003C= 10).orderBy(\"sales_amount_rank\")\n\nfinal_report = detailed_report.withColumn(\"report_date\", current_date()) \\\n                             .withColumn(\"source_table\", lit(source_sales_table)) \\\n                             .withColumn(\"job_name\", lit(\"snowflake_notebook\"))\n\n# Truncate and insert\nspark.sql(f\"TRUNCATE TABLE {catalog}.{database}.{report_table}\")\ntarget_df = spark.read.table(f\"{catalog}.{database}.{report_table}\")\nfinal_report.select(*target_df.columns).write.insertInto(f\"{catalog}.{database}.{report_table}\")\n```\n\n\u003C!-- ------------------------ --\u003E\n## Verify Code Interoperability\n\nBoth AWS Glue and Snowflake have now generated the same Top 10 products report using **identical Spark code**.\n\n### Compare Results\n\nQuery both reports to verify they produce identical results:\n\n```python\n# Get Glue-generated report\nprint(\"=== GLUE-GENERATED REPORT ===\")\nspark.sql(f\"SELECT * FROM {catalog}.{database}.top_10_products_report_spark ORDER BY sales_amount_rank\").show(10, truncate=False)\n\n# Get Snowflake-generated report\nprint(\"=== SNOWFLAKE-GENERATED REPORT ===\")\nspark.sql(f\"SELECT * FROM {catalog}.{database}.{report_table} ORDER BY sales_amount_rank\").show(10, truncate=False)\n```\n\n### Verify in AWS Glue Console\n\nGo to **AWS Glue Console** → **Data Catalog** → **Tables**\n\nCheck the `top_10_products_report_snow` table:\n- It should now have data (populated by Snowflake)\n- Metadata automatically synced back to Glue catalog\n\n### Key Observations\n\n✅ **Same Code**: 95%+ of the Spark code is identical between platforms  \n✅ **Same Results**: Both reports show identical Top 10 products  \n✅ **Same Data**: Both platforms query the same Iceberg files in S3  \n✅ **Bi-directional**: Both platforms can create and modify tables  \n✅ **Zero Data Movement**: No ETL between platforms required\n\n\u003C!-- ------------------------ --\u003E\n## Troubleshooting\n\n### CloudFormation Stack Creation Failed\n\n**Error:** IAM permissions denied\n\n**Solution:**\n- Ensure your AWS user has permissions to create IAM roles\n- Add `iam:CreateRole`, `iam:PutRolePolicy`, `iam:AttachRolePolicy` permissions\n- Or use an admin user for initial deployment\n\n**Error:** S3 bucket not found\n\n**Solution:**\n- Verify the bucket exists: `aws s3 ls s3://$DATA_BUCKET`\n- Ensure bucket name in parameters matches exactly\n- Check bucket is in the same region as CloudFormation stack\n\n### Glue Job Fails\n\n**Error:** \"Access Denied\" to S3 bucket\n\n**Solution:**\n```bash\n# Check IAM role permissions\naws iam get-role-policy --role-name $GLUE_ROLE_NAME --policy-name GlueIcebergS3Access\n```\n\n**Error:** \"No JSON object could be decoded\"\n\n**Solution:**\n- Verify data files uploaded correctly to S3\n- Check file paths match: `s3://\u003Cbucket\u003E/raw_data/customers.json`\n\n### Tables Not Appearing in Snowflake\n\n**Error:** `SHOW TABLES` returns empty in catalog-linked database\n\n**Solution:**\n1. Check catalog link status:\n```sql\nSELECT SYSTEM$CATALOG_LINK_STATUS('glue_lake_int_db');\n```\n\n2. Verify ALLOWED_NAMESPACES includes your Glue database\n\n3. Verify tables exist in Glue:\n```bash\naws glue get-tables --database-name $GLUE_DB_NAME\n```\n\n4. If tables still don't appear, drop and recreate catalog-linked database\n\n### Snowpark Connect Not Working\n\n**Error:** \"Module 'snowpark_connect' not found\"\n\n**Solution:**\n- Ensure Snowpark Connect package is added in Packages tab\n- Restart notebook kernel\n- Check Snowflake Runtime is 2.0 or later\n\n### External ID Mismatch\n\n**Error:** \"External ID mismatch\"\n\n**Solution:**\n- The external ID in Catalog Integration must match:\n  - The External ID from DESC EXTERNAL VOLUME output\n  - The SnowflakeExternalId parameter in CloudFormation\n- All values must be identical\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\nFollow these steps to remove all deployed resources.\n\n### Delete Snowflake Resources\n\n```sql\n-- Drop catalog-linked database\nDROP DATABASE IF EXISTS glue_lake_int_db;\n\n-- Drop catalog integration\nDROP CATALOG INTEGRATION IF EXISTS glue_rest_cat_int_demo;\n\n-- Drop external volume\nDROP EXTERNAL VOLUME IF EXISTS extvol_iceberg_demo;\n\n-- Drop demo database\nDROP DATABASE IF EXISTS ICEBERG_LAKE;\n```\n\n### Delete AWS CloudFormation Stack\n\n```bash\n# Delete stack (will remove all Glue jobs, database, IAM role)\naws cloudformation delete-stack --stack-name iceberg-glue-snowflake-demo\n\n# Wait for deletion to complete\naws cloudformation wait stack-delete-complete --stack-name iceberg-glue-snowflake-demo\n\n# Verify deletion\naws cloudformation describe-stacks --stack-name iceberg-glue-snowflake-demo\n# Should return error: \"Stack does not exist\"\n```\n\n### Delete S3 Data\n\n```bash\n# Delete all Iceberg data\naws s3 rm s3://$DATA_BUCKET/iceberg-warehouse/ --recursive\n\n# Delete scripts and raw data\naws s3 rm s3://$DATA_BUCKET/scripts/ --recursive\naws s3 rm s3://$DATA_BUCKET/raw_data/ --recursive\n\n# Optional: Delete the bucket itself\naws s3 rb s3://$DATA_BUCKET --force\n```\n\n### Verify Cleanup\n\n```bash\n# Check Glue database is gone\naws glue get-database --name $GLUE_DB_NAME\n# Should return error\n\n# Check S3 bucket\naws s3 ls s3://$DATA_BUCKET\n# Should be empty or not exist\n```\n\n\u003C!-- ------------------------ --\u003E\n## Continue Building with Snowflake CoCo\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. It can guide you through setting up, verifying, and troubleshooting your Glue catalog integration interactively, without switching between AWS console tabs and documentation.\n\n### Set up or troubleshoot your Glue catalog integration\n\nUse these prompts to get started. Snowflake CoCo will ask for your AWS account ID, region, IAM role, and access delegation preference — then generate and execute the SQL, retrieve the Snowflake IAM user ARN, and walk you through the trust policy update.\n\n**Create a new catalog integration:**\n```\nHelp me create a catalog integration for AWS Glue Iceberg REST\n```\n\n**Verify an existing integration is working:**\n```\nVerify my Glue catalog integration and list the namespaces and tables it can see\n```\n\n**Troubleshoot a broken integration:**\n```\nMy Glue catalog integration is failing — help me diagnose and fix it\n```\n\n**Set up a Catalog-Linked Database after the integration is ready:**\n```\nCreate a catalog-linked database from my Glue catalog integration\n```\n\n**Run Spark code against your Glue-managed Iceberg tables using Snowpark Connect:**\n```\nHelp me set up Snowpark Connect for Apache Spark to read and write Iceberg tables managed by AWS Glue\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nYou've successfully built an interoperable lakehouse architecture that demonstrates true code portability between AWS Glue and Snowflake using Apache Iceberg.\n\n### What You Learned\n- How to create Iceberg tables using AWS Glue ETL jobs\n- How to configure Snowflake External Volume and Catalog Integration with AWS Glue\n- How Snowflake Catalog-Linked Databases automatically discover and sync Iceberg tables\n- How Snowpark Connect enables identical Spark code to run on both platforms\n- How to achieve zero data movement with bi-directional read/write capabilities\n\n### Related Resources \n- [GitHub Repository for this Quickstart](https://github.com/sfc-gh-pjain/glue_cld_snowflake/tree/main)\n- [Snowflake Catalog-Linked Database Documentation](https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database)\n- [Snowpark Connect Overview](https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-overview)\n- [Apache Iceberg Documentation](https://iceberg.apache.org/)\n- [AWS Glue Developer Guide](https://docs.aws.amazon.com/glue/)\n- [AWS Glue Iceberg Support](https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-format-iceberg.html)\n- [Configure Catalog Integration for AWS Glue Iceberg REST](https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-catalog-integration-rest-glue)\n\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-d1b673b169","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-d130c770f2",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-1a7fd339e3","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-66f919a819","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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