Unlocking Data Lake Potential: Harnessing Apache Iceberg with AWS Glue and Snowflake

Modern Data Lake with AWS, Snowflake, Apache Iceberg

In today’s dynamic business landscape, companies are constantly adapting to changing market conditions, customer preferences, and technological advancements. This constant evolution creates a growing demand for data-driven insights and solutions. A rigid, one-size-fits-all approach often leads to inefficiencies, bottlenecks, and missed opportunities. A modern data architecture designed for scalability and flexibility enables organizations to adapt quickly to changing business needs and technological advancements.

By leverage AWS and Snowflake to craft tailored data architectures that deliver the speed and capability necessary for contemporary analytics and artificial intelligence (AI) applications. Building these architectures involves seamless data exchange among specialized data repositories. Hence, Snowflake and AWS are enhancing their backing for Apache Iceberg to streamline and promote data interoperability across various data services.

Advantages of purpose-built data architectures

Apache Iceberg

Apache Iceberg facilitates the construction of transactional data lakes by ensuring ACID compliance for write operations, supporting seamless schema evolution, and enabling efficient data partitioning and clustering for enhanced query performance. With concurrency control and snapshot isolation, Iceberg allows for simultaneous interaction with data while maintaining consistency and reliability. Its time travel capabilities enable historical data analysis, and optimized metadata management improves scalability. Additionally, Iceberg integrates smoothly with existing ecosystems, providing compatibility with various storage formats and query engines, making it a comprehensive solution for building robust and efficient transactional data lakes.

Apache Iceberg indeed offers integrations with a variety of popular data processing frameworks, including Apache Spark, Apache Flink, Apache Hive, Presto, and more. This compatibility allows users to seamlessly incorporate Iceberg into their existing data processing pipelines and leverage its features for building robust and efficient transactional data lakes.

AWS Glue

AWS Glue serves as a powerful tool for ingesting, cataloging, transforming, and managing data on Amazon S3, making it an ideal choice for building data lakes. With AWS Glue’s serverless architecture, users can create, run, and monitor ETL pipelines visually, streamlining the process of loading data into data lakes in Iceberg format.

AWS Glue Data Catalog adds another layer of convenience and efficiency to managing Iceberg tables within your data lake environment. By leveraging the Glue Data Catalog for tracking table metadata, you centralize the management of metadata for Iceberg tables alongside other data sources, streamlining cataloging processes and enhancing overall data governance.

Snowflake

Snowflake’s seamless integration with S3 coupled with its high-performance architecture, scalability, robust security features, schema flexibility, data sharing capabilities, built-in optimization, and cost-effectiveness make it an advantageous platform for building data lakes in Iceberg format on S3. With Snowflake, organizations can efficiently query and analyze data stored in Iceberg format on S3 without data movement, ensuring fast query execution, high concurrency support, and simplified data management.

Architectural pattern for sharing Iceberg tables between AWS and Snowflake

Workflow steps:

AWS Glue Data Integration:

AWS Glue extracts data from applications, databases, and streaming sources. AWS Glue then transforms it and loads it into the data lake in Amazon S3 in Iceberg table format.

1. Connectivity and Integration
   • AWS Glue supports connectivity to a variety of data sources and destinations, including Amazon S3, Amazon Redshift, Amazon RDS, Amazon DynamoDB, JDBC-compliant databases, and custom connectors via AWS Lambda functions.
   • Glue provides pre-built connectors for popular data sources, simplifying the process of ingesting data from external systems.
   • Users can define connection parameters, access credentials, and data ingestion settings through Glue’s console or API.
2. ETL Processes
   • AWS Glue ETL jobs enable users to define and execute data transformation tasks to clean, enrich, and normalize data before loading it into target destinations.
   • ETL jobs are defined using Apache Spark-based Python or Scala scripts, which are executed on managed Spark clusters provisioned by AWS Glue.
   • Glue ETL jobs support a wide range of data processing operations, including filtering, aggregation, join, pivot, and custom transformations.

Harnessing Iceberg Format with AWS Glue Catalog:

While inserting and updating metadata about the Iceberg table in the AWS Glue Data Catalog, the AWS Glue crawler automatically generates and updates Iceberg table metadata.

1. Integration with AWS Glue Catalog
   • AWS Glue Catalog serves as a centralized metadata repository for managing schema information, data lineage, and metadata for data assets.
   • Integration of Iceberg format with AWS Glue Catalog allows users to define Iceberg tables and store metadata in the Glue Catalog, enabling seamless management and querying of Iceberg tables.
   • Once Iceberg tables are defined, users can register them in the AWS Glue Catalog, providing metadata such as table name, schema definition, partitioning scheme, and location in Amazon S3.
2. Metadata Generation with AWS Glue Crawler
   • AWS Glue crawlers scan the specified S3 locations containing Iceberg tables and extract metadata such as table schema, partitioning information, and file locations.
   • The benefits of integrating Iceberg tables with AWS Glue, including centralized metadata management, data lineage tracking, and seamless querying.

Iceberg tables work Snowflake and integrates with AWS Glue Data Catalog

1. Data storage

   • Iceberg tables store their data and metadata files in an external cloud storage location (Amazon S3, Google Cloud Storage, or Azure Storage).
   • The external storage is not part of Snowflake. You are responsible for all management of the external cloud storage location, including the configuration of data protection and recovery.
   • Snowflake connects to your storage location using an external volume
2. External volume
   • An external volume is a named, account-level Snowflake object that stores an identity and access management (IAM) entity for your external cloud storage.
   • Snowflake securely connects to your cloud storage with an external volume to access table data, Iceberg metadata, and manifest files that store the table schema, partitions, and other metadata.
   • A single external volume can support one or more Iceberg tables.
3. Iceberg catalog
   • An Iceberg catalog enables a compute engine to manage and load Iceberg tables. The catalog forms the first architectural layer.
   • Storing the current metadata pointer for one or more Iceberg tables. A metadata pointer maps a table name to the location of that table’s current metadata file.
   • Performing atomic operations so that you can update the current metadata pointer for a table.
4. Catalog integration for AWS Glue
   • A catalog integration is a named, account-level Snowflake object that defines the source of metadata and schema for an Iceberg table when you don’t use Snowflake as the Iceberg catalog
   • Configure access permissions for the AWS Glue Data Catalog, create a new IAM policy for Snowflake to access the Glue Data Catalog.
   • Create a catalog integration for the Glue Data Catalog using the CREATE CATALOG INTEGRATION command.
   • Retrieve the AWS IAM user and external ID for your Snowflake account.
   • Grant the IAM user permissions to access the AWS glue data catalog.
   • Snowflake uses the snapshot location from AWS Glue Data Catalog to read Iceberg table data in Amazon S3.

Overall

AWS Glue Data Catalog with Snowflake Iceberg tables can streamline data management, improve productivity, and enhance the efficiency of your data analytics workflows. It combines the benefits of Snowflake’s powerful data warehouse capabilities with AWS’s managed metadata management and ETL processing services.