Iceberg rises. Catalogs compete. The architecture of AI-ready data is being rewritten in real time.
The significant pressures artificial intelligence (AI) has placed on data architectures to be more efficient has led to the rise of data lakehouse architectures, which decouple the storage and tracking of data from how it is accessed and processed. The essence of this approach lies in storing datasets within a data lake, utilizing open table formats such as Apache Iceberg, Delta Lake, or Apache Hudi, and enabling any compatible processing tool to work seamlessly with the data.
This architecture has become indispensable in the age of AI, where the ability to train and fine-tune models depends on rapid access to diverse datasets. Over the past few years, the industry witnessed the “Table Format War” between Apache Iceberg, Delta Lake, and Apache Hudi. However, this “war” was less about conflict and more about a race to solidify each format’s place in the marketplace through vendor support and feature innovation.
While all three formats have carved out distinct roles in the market, Apache Iceberg has emerged as a frontrunner and potential industry standard. This momentum is evident from the wave of Iceberg-related announcements in 2024 from major players like AWS, Google, Databricks, Snowflake, Upsolver, and Dremio. As the focus on table formats begins to settle, a new competitive frontier has emerged—the “lakehouse catalog war.” This phase isn’t a hostile battle but a race where lakehouse catalog platforms aim to expand vendor support and feature sets, vying to become the standard solution for tracking and governing lakehouse assets.
Components That Make Up a Lakehouse Catalog
A lakehouse catalog is a service that tracks and manages lakehouse assets such as tables, views, namespaces, functions, and models. Examples of lakehouse catalogs include Apache Polaris, Nessie, Gravitino, Unity Catalog, and Lakekeeper. These catalogs provide a central repository for discovering and managing assets, ensuring a unified approach to governance and access control.
One of the critical features of lakehouse catalogs is their ability to define access rules for assets, which tools that support these catalogs can enforce. This approach ensures that both governance and assets are portable across compute platforms. Moreover, managed catalog services offered by companies like Dremio, Snowflake, Databricks, and AWS reduce the operational burden by automating maintenance tasks such as optimizing table performance and cleaning up obsolete data.
As organizations embrace hybrid and multi-cloud ecosystems, the role of lakehouse catalogs in ensuring interoperability and governance portability becomes increasingly significant. These catalogs represent the next phase in making data lakehouses more robust and user-friendly.
The Iceberg REST Catalog Specification
An essential development in the lakehouse catalog space is the Iceberg REST Catalog Specification. This specification standardizes how compute tools interact with catalogs for reading and writing to Iceberg tables, making it easier for vendors to support new catalogs. By implementing the specification, catalogs like Polaris, Nessie, Gravitino, Lakekeeper, and Unity Catalog can ensure compatibility with tools and platforms that work with Iceberg.
However, not all catalogs fully implement the specification. For example, Unity Catalog primarily supports the read portions of the specification but not the write portions. Unlike other catalogs, Unity Catalog is designed to work with Delta Lake as its primary format, offering a feature called Uniform to mirror Iceberg versions of Delta tables. This functionality allows Unity Catalog users to read these tables with Iceberg-first tools like Dremio and Snowflake.
The Iceberg REST Catalog Specification is a crucial enabler for interoperability, simplifying vendor support and creating a foundation for innovation in the lakehouse ecosystem.
Key Trends to Watch
As the “catalog wars” continue to unfold, several developments are worth monitoring:
1. Enhancements to the REST Catalog Specification: The Iceberg REST Catalog Specification will likely evolve to include new features, such as the Scan Planning Endpoint, which could shift more query processing responsibilities to the catalog itself. This enhancement would allow catalogs to offer greater optimization and differentiation while maintaining a consistent open interface.
2. Growth of Managed Services: Managed catalog services are becoming a significant competitive differentiator. For instance, Apache Polaris is the open-source foundation for managed solutions like Snowflake’s Open Catalog and Dremio’s Hybrid Catalog. These services simplify the deployment and management of lakehouse catalogs while introducing features like automated performance tuning and governance. The more managed service that arises around any particular catalog will help that catalog build its market share.
3. Expansion of Supporting Services: The ecosystem of tools and platforms supporting various catalog solutions will play a pivotal role in shaping the market. The number of integrations, supported features, and user-centric innovations will determine which catalog solutions gain widespread adoption.
4. Feature Expansion: In the future, catalogs may expand their feature sets to include advanced tools for lineage tracking, observability, and managing user-defined functions (UDFs). These features can ensure seamless portability across diverse platforms and teams, enabling consistent use of metadata, AI model features, and other critical data assets. By centralizing and standardizing these elements, catalogs could streamline workflows for teams leveraging various compute tools, from data engineering pipelines to machine learning platforms.
Additionally, robust support for data observability and detailed tracking of dataset types, transformations, and dependencies could empower organizations to maintain transparency and ensure compliance across their operations while fostering collaboration across departments and ecosystems.
Final Thoughts
The shift toward data lakehouse architectures has redefined how organizations manage and access data. Just as the “Table Format War” is winding down, the “catalog wars” are heating up with platforms vying to become the standard solution for tracking and governing lakehouse assets.
Apache Iceberg has already solidified its position as a leading table format, but the spotlight now turns to lakehouse catalogs like Polaris, Nessie, Gravitino, and others. These solutions aim to provide seamless interoperability, advanced governance, and optimized performance in a hybrid and multi-cloud world.
The future of data architecture depends on the outcome of these developments. As the Iceberg REST Catalog Specification evolves and managed catalog services grow in sophistication, organizations will gain powerful tools to harness the potential of lakehouses for AI, analytics, and beyond. The “catalog wars” may just be beginning but, like the battles before it, their impact will shape the data industry for years.
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