Apache Iceberg catalogs demystified: A methodical approach to data governance
Authors:
Samiksha Saraf, Director of Technology
Ashish Kumar Dahiya, Senior Technical Architect
Gurvinder Arora, Senior Lead Technical Writer
In this final blog of our “Future-proofing Data Architectures with Iceberg” series, we explore top data cataloging solutions—Apache Polaris, AWS Glue, Snowflake Horizon, and Databricks Unity Catalog—and how LeapLogic enables seamless migrations with features like schema evolution and query optimization. Did you miss our earlier posts? Catch up on Iceberg’s architecture, migration challenges, and LeapLogic’s approach to modernizing legacy platforms.
As data complexity grows, effective management and governance are critical. Apache Iceberg, supported by engines like Spark, Flink, and Snowflake, has become a cornerstone of modern data lakes. This blog focuses on key data cataloging solutions, spotlighting Apache Polaris for centralized governance and seamless interoperability.
Why is data catalog important for the organization?
Streamlined data discovery and democratization
Organizes and indexes datasets, enabling users to quickly find, understand, and use data without technical expertise, saving time and boosting efficiency.
Centralized data governance
Ensures data quality, privacy, and security with tools for classification, access control, and compliance with regulations like GDPR and HIPAA.
Enhanced collaboration
Provides a unified view of data, breaking down silos and encouraging interdepartmental collaboration and data sharing.
Unified view of distributed data
Integrates data from multiple platforms (on-premise, cloud, data lakes, and warehouses) for centralized management and accessibility.
Bridging legacy systems to modern catalogs
Modern data catalogs play a vital role in enabling organizations to manage and govern their data effectively. They offer robust tools for metadata management, data discovery, and compliance, catering to evolving data lakehouses and cloud-native environments.
Each data cataloging platform—Apache Polaris, AWS Glue Data Catalog, Snowflake Horizon Catalog, and Databricks Unity Catalog—caters to different organizational needs based on infrastructure, use cases, and integration requirements. Here’s a breakdown of how these platforms differ and their ideal use cases:
| Feature | Apache Polaris | AWS Glue Data Catalog | Snowflake Horizon Catalog | Databricks Unity Catalog |
|---|---|---|---|---|
| Advent and Introduction | Polaris is designed for the modern data lakehouse—a unified platform for structured and unstructured data supporting analytics and machine learning. Specifically tailored for managing Apache Iceberg tables within Lakehouse architectures, Polaris integrates popular data lakes, automates indexing, and optimizes queries to deliver faster and more efficient analytics. | The AWS Glue Data Catalog simplifies data integration within the AWS ecosystem by serving as a centralized metadata repository. As part of the fully-managed AWS Glue ETL service, it helps organizations efficiently manage, discover, organize, and govern data, streamlining transformation and utilization for analytics and machine learning. | The Snowflake Horizon Catalog is designed to provide centralized metadata management within the Snowflake platform. It enables efficient data discovery, governance, and collaboration across various data assets, offering robust integration with Snowflake-stored and external data sources. This ensures seamless management and utilization of data across the organization. | The Databricks Unity Catalog enables data scientists, analysts, and engineers to securely discover, access, and collaborate on trusted data and AI resources within Lakehouse architectures. It promotes a unified, open governance approach, enhancing interoperability, accelerating data and AI initiatives, and simplifying regulatory compliance, all while improving productivity across teams. |
| Primary use case | Iceberg metadata management | AWS ecosystem metadata and ETL | Snowflake metadata and governance | Unified governance for Databricks |
| Integration | Integration Multi-engine (Spark, Trino, Dremio) | AWS services (S3, Redshift, Athena) | Snowflake and external Iceberg tools | Databricks Lakehouse & ML workflows |
| Governance strength | Governance strength Column masking, tagging | IAM-based access controls | Dynamic data masking, lineage | Access control, AI/ML governance |
| Openness | Open-source flexibility | AWS proprietary | Snowflake-focused, partially flexible | Open-source flexibility |
| Best for | Open Iceberg-based Lakehouses | AWS-focused infrastructures | Snowflake environments | Databricks Lakehouse with AI/ML |
| Add-on features | Automated metadata updates | Data lineage tracking, column statistics | Dynamic data masking, data quality monitoring, data lineage visualization | Three-level namespace structure, monitoring, and observability |
How LeapLogic migrates legacy data catalogs to modern data lake architectures
LeapLogic migrates legacy metadata repositories and data catalogs to modern cloud-native data catalog services offered by modern data platforms. As highlighted in our last blog, we discussed an end-to-end customer use case where LeapLogic leveraged Apache Iceberg on the AWS stack. Here’s how LeapLogic helps in operationalizing the modern data catalog services, taking all the legacy workloads and data to the target native stack.
Key features
- Utilizes Apache Iceberg table format, Apache Polaris, or any other enterprise data catalog such as AWS Glue Data Catalog, Databricks Unity Catalog, Snowflake Horizon Catalog, etc.
- Conducts a comprehensive inventory of existing data assets to identify what is valuable and relevant
- Develops a clear migration strategy that outlines the scope, approach, and timelines
- Implements a robust data catalog during the migration process to facilitate the organization and prioritization of data assets
- Maps how legacy data will correspond to the new structure in the target system
- Depending upon table usage, configures Copy on Write or Merge on Read
- Utilizes data mesh techniques to establish seamless connectivity
- Enables Iceberg and catalog to leverage Spark’s parallel processing capabilities
- Optimizes traditional 3NF data models of relational databases as per the contained ETL logic
- Enables schema evolution on the table columns, including operations like adding, renaming, re-ordering, deleting, and changing type. This ensures that data overwrites are handled via metadata updates only
- Implements partitioning as per the use cases
- Optimizes queries to cloud-native platform equivalent, including partitioning, copy-on-write (COW) or merge-on-read (MOR) strategy, and compression techniques for tables that are frequently queried and archiving for archival tables
- Validates successful migration and certifies for production
- Provides adequate training for users on the new systems and maintains thorough documentation throughout the migration process to facilitate future reference and onboarding
Data cataloging solutions like Apache Polaris, AWS Glue, Snowflake Horizon, and Databricks Unity Catalog provide organizations with powerful tools for managing, governing, and discovering data across diverse platforms. Each platform offers unique strengths suited to different environments, enabling seamless integration and enhanced data governance.
LeapLogic plays a crucial role in modernizing data architectures by facilitating smooth migrations from legacy systems to cloud-native platforms. With its focus on efficient metadata management and query optimization, LeapLogic ensures businesses can fully leverage modern data catalogs for improved analytics and data governance.
