Interoperability with Snowflake Open Catalog & Iceberg Tables

Interoperability plays a crucial role in maximising the potential of data ecosystems. Snowflake’s Open Catalogue and Apache Iceberg tables are changing how organisations manage, share, and analyse data across multiple platforms. With open standards and flexible data architectures, these technologies help businesses eliminate silos, improve accessibility, and enhance collaboration.

 

This blog covers how Snowflake Open Catalog and Iceberg tables work together to provide a unified, scalable, cost-effective approach to modern data management. Understanding these innovations is essential for optimising analytics, ensuring data consistency, or future-proofing architecture. Let’s get started!

Overview of Snowflake Open Catalog 

Snowflake Open Catalog is a centralized repository for managing metadata in the Snowflake ecosystem. It allows users to catalog and manage their data tables and metadata, making accessing and sharing data across different platforms easier. By providing a secure, scalable, and easy-to-use interface, Snowflake Open Catalog ensures that users can maintain consistency and data governance while improving the discoverability and accessibility of data. 

 

In the context of Iceberg tables, Snowflake Open Catalog is crucial in helping users organize and manage the metadata of Iceberg tables, whether stored in Amazon S3, Google Cloud Storage, or Azure. It ensures that data from different sources is cataloged in one place, making it easier for users to query and manage their data. 

What are Iceberg Tables? 

Apache Iceberg is an open-source table format designed to handle large-scale datasets in data lakes. Iceberg provides features that address the challenges of managing big data, such as version control, schema evolution, and partitioning flexibility. It allows users to store, organize, and track changes in their data over time, providing a more manageable and scalable way to work with large datasets. 

 

Iceberg is cloud-native and designed for scalability, making it an excellent choice for data lakes. It allows users to manage large datasets efficiently and provides high performance for analytics and querying, making it a perfect complement to Snowflake's powerful data processing capabilities. 

The Importance of Interoperability in Modern Data Architecture 

In today’s complex data environments, it’s not uncommon for organizations to use various systems to store, process, and analyze their data. Data may reside in multiple cloud storage platforms, be stored in different formats, and be processed by various systems. For this reason, ensuring interoperability across platforms is crucial for seamless data workflows. 

 

Interoperability between platforms like Snowflake and Iceberg enables organizations to utilise each system's strengths. For example, Iceberg handles data storage and versioning in a way optimized for large datasets, while Snowflake’s cloud data platform is optimized for fast querying and analysis. By integrating these systems, businesses can create a unified data environment for efficient data management, governance, and analysis across platforms.

Exploring Snowflake and Iceberg Tables

Snowflake is a fully managed cloud data platform that enables organizations to store, process, and analyze their data at scale. It provides various tools for working with structured and semi-structured data and supports multi-cloud environments (AWS, Azure, and Google Cloud). Snowflake's architecture separates compute from storage, allowing organizations to scale each independently as needed. 

 

One of Snowflake's key features is its SQL-based interface, which allows users to write queries and analyze data with minimal setup and management. This ease of use, combined with Snowflake’s powerful computing engine, makes it an ideal platform for analyzing large datasets quickly. 

 

Snowflake also integrates with various data management and analytics tools, enabling users to build flexible data pipelines and workflows. It provides built-in data sharing and security features, making it a popular choice for businesses collaborating and ensuring compliance across teams and regions. 

Key Features of Iceberg Tables 

Apache Iceberg tables are designed to solve many challenges data engineers and analysts face when working with large datasets. Some of the key features of Iceberg tables include: 

• Version Control: Iceberg supports versioned tables, meaning users can easily track changes to their data over time and access older versions if needed. 

• Schema Evolution: Iceberg allows users to evolve the schema of their tables without needing to rewrite the data. This is crucial for maintaining flexibility as data requirements change. 

• ACID Transactions: Iceberg supports ACID transactions, which ensure that all operations on the table are consistent and reliable, even in the face of concurrent reads and writes. 

• Efficient Partitioning: Iceberg supports flexible partitioning strategies, which can improve query performance by optimizing how data is physically stored. 

• Time Travel: Iceberg enables users to perform time travel queries, allowing them to access historical versions of data at any point in time. 

These features make Iceberg a powerful and flexible table format for managing large-scale datasets in data lakes. 

How Snowflake and Iceberg Complement Each Other 

Snowflake and Iceberg complement each other by combining Snowflake’s high-performance query capabilities with Iceberg’s robust data management features. While Snowflake is known for its ability to perform fast, scalable analytics, Iceberg handles large datasets' storage, versioning, and metadata management. By integrating these two platforms, organizations can take advantage of both. 

 

For example, Iceberg stores data in a cloud storage system like Amazon S3, and Snowflake’s external tables can query this data without moving or copying it into Snowflake’s native storage. This integration allows businesses to use Snowflake’s advanced querying and analytics while keeping the data in a scalable, cost-effective format like Iceberg. 

Use Cases and Benefits of Snowflake and Iceberg Integration 

The integration of Snowflake and Iceberg brings numerous benefits to organizations, such as: 

• Efficient Data Management: Iceberg helps organize large datasets, track changes, and evolve schemas without impacting performance. Snowflake’s powerful query engine makes it easy to analyze this data at scale. 

• Faster Data Access: By leveraging Snowflake’s compute capabilities and Iceberg’s efficient data storage, users can query massive datasets with minimal delay. 

• Cost Efficiency: The ability to store data in Iceberg tables on cloud storage platforms like Amazon S3 allows organizations to reduce storage costs while maintaining fast, reliable access to their data. 

• Flexible Analytics: Businesses can perform complex analytics across data stored in Iceberg tables without needing to transform or migrate the data, making gaining insights from various data sources easier. 

Interoperability Between Snowflake and Iceberg

Connecting Snowflake Open Catalog with Iceberg Tables 

One key benefit of Snowflake and Iceberg interoperability is the ability to use the Snowflake Open Catalog to manage and access Iceberg tables. The catalog provides a centralized repository for Iceberg table metadata, essential for querying and managing large datasets across platforms. 

 

To connect Snowflake Open Catalog with Iceberg tables, you can follow these basic steps: 

Setup 

Step 1. Create Snowflake Open Catalog Account, Connections, Roles 

• Create a Snowflake Open Catalog account 

• Create a catalog 

From the Connections page: 

1. In the Principals tab, create three service connections named spark_analyst, spark_engineer, and snowflake_engineer.

2. In the Roles tab, create three principal roles named spark_analyst_role, spark_engineer_role and snowflake_engineer_role.

From the snowflake_catalog page, in the roles tab    

Create three catalog_role named table_all,table_reader_refined, and snowflake_catalog_role with the following privileges 

table_all: 

• Catalog: 

• NAMESPACE_LIST 

• TABLE_LIST 

• TABLE_READ_DATA 

• CATALOG_READ_PROPERTIES 

table_reader_refined: 

• Catalog: 

• NAMESPACE_LIST 

• Namespace BUILD_DB.REFINED 

• NAMESPACE_LIST 

• TABLE_LIST 

• TABLE_READ_DATA 

snowflake_catalog_role: 

• Catalog: 

• CATALOG_MANAGE_CONTENT 

Assign catalog roles to principal roles: 

• table_all: spark_engineer_role 

• table_reader_refined: spark_analyst_role 

• snowflake_catalog_role: snowflake_engineer_role 

Step 2. Create External Volume 

Create and configure an external volume for Snowflake Dynamic Iceberg tables to write data and metadata. 

 

Step 3. Create Tables 

Execute the statements in sampledata.sql (if having) to create tables and schemas in a dedicated database. If you choose to use different object names than the provided SQL, you may need to replace the following values with your own: 

• EXTERNAL_VOLUME 

• CATALOG 

• BASE_LOCATION 

• CATALOG_SYNC 

Step 4. Load Data 

Use the following .csv files and load data into respective tables using Snowsight 

Benefits of Data Interoperability 

The interoperability between Snowflake and Iceberg provides several benefits: 

• Seamless Data Access: Snowflake users can access data stored in Iceberg tables without needing to move or transform it, improving efficiency and reducing data duplication. 

• Improved Data Governance: By centralizing metadata in the Snowflake Open Catalog, organizations can better manage data access and track data lineage, ensuring compliance with regulatory requirements. 

• Enhanced Collaboration: Snowflake’s data-sharing capabilities, combined with Iceberg’s versioning and metadata tracking, enable teams to collaborate on data analysis while ensuring consistency and accuracy. 

Real-Time Data Access and Querying Across Platforms 

With Snowflake and Iceberg working together, organizations can query and analyze data in real-time across platforms. Whether the data is stored in Snowflake or Iceberg, Snowflake's powerful query engine ensures fast performance, enabling users to make data-driven decisions quickly.

Use Cases of Interoperability

1. Improved Data Sharing Across Multiple Platforms 

   Interoperability between Snowflake and Iceberg makes it easy to share data across different platforms. This allows teams to access and analyze data without replicating it across systems, leading to improved collaboration and faster decision-making. 

2. Optimizing Data Governance and Compliance 

   By using Snowflake Open Catalog to track metadata for Iceberg tables, organizations can ensure compliance with data governance and regulatory requirements. Centralized metadata helps maintain consistency and transparency across data systems.

3. Enhancing Performance in Data Lakes and Warehouses
   Combining Snowflake’s powerful query engine with Iceberg’s optimized data storage ensures high data lake and warehouse performance. This allows businesses to handle large-scale data operations easily, improving overall efficiency.

Technical Insights and Architecture

1. Integration Architecture for Snowflake and Iceberg 

   The integration between Snowflake and Iceberg relies on cloud storage systems like Amazon S3 or Google Cloud Storage. Snowflake queries data stored in Iceberg tables via external tables, and metadata is managed through Snowflake Open Catalog. This architecture ensures that users can access and analyze data stored in Iceberg without moving or transforming it. 
   

   Fig 3: Integration Architecture for Snowflake and Iceberg 

2. Data Flow and Processing Mechanisms 

   Data flows between Snowflake and Iceberg, allowing users to query Iceberg tables directly within Snowflake. Snowflake’s compute engine processes queries, while Iceberg manages data storage and metadata. This separation of concerns allows for optimal performance and scalability. 

3. Best Practices for Seamless Interoperability
   To ensure seamless interoperability, follow best practices such as: 

• Partition data effectively in Iceberg tables to optimize query performance. 

• Refresh external volumes regularly to ensure Snowflake has access to the most up-to-date data.

• Implement proper access controls to ensure data security and compliance.
  

Future of Data Interoperability

1. Innovations in Data Catalogs and Table Formats
   As data architectures evolve, new features and capabilities will emerge in data catalogs and table formats. Snowflake and Iceberg will continue to innovate, providing even more powerful tools for managing and analyzing data.

2. The Role of Cloud-Native Technologies
   Cloud-native technologies will continue to play a critical role in driving data interoperability. Platforms like Snowflake and Iceberg are designed to scale with the cloud, enabling businesses to efficiently manage vast amounts of data.

3. Predictions for Snowflake and Iceberg Ecosystem
   The future of the Snowflake and Iceberg ecosystem is bright, with ongoing developments in data management, querying capabilities, and interoperability. Expect further integration and performance optimization advancements, making data management even more seamless. 

Integrating Snowflake and Apache Iceberg allows organizations to leverage the best of both worlds—high-performance querying and scalable, efficient data storage. This interoperability enhances data management, collaboration, and analytics across platforms, empowering businesses to unlock the full potential of their data. By adopting Snowflake and Iceberg, organizations can ensure that their data architecture is robust and future-proof.