The Chaos of Catalogs

It’s the chaos we’ve created and it’s the chaos we must now endure.

If you’ve spent time in data engineering, you’ve likely found yourself staring at a convoluted architecture diagram and thinking, OMG, the catalogs! What started as a relatively simple solution for managing metadata with Apache Hive has evolved into a fragmented ecosystem of competing solutions. The proliferation of data catalogs has brought innovation and chaos to the modern data landscape.

The Early Days: Hive Metastore

In the early days of Hadoop, Hive offered an SQL interface on top of MapReduce, allowing users to write queries instead of low-level code. Hive also introduced a catalog for managing metadata—storing table schemas, partition information, and data locations. The Hive metastore quickly became the backbone of Hadoop-based data systems, powering tools like HiveQL and later serving as a foundation for query engines like Presto and Spark SQL.

The ecosystem was relatively simple back then. The Hive Metastore was the default choice, and most tools operated on top of Hadoop-compatible storage systems. This setup allowed programmers to focus on choosing programming models rather than worrying about which catalog to choose.

However, as data volumes grew and use cases became more complex, Hive Metastore’s limitations became increasingly apparent.

The Limitations of Hive Metastore

Scalability Struggles: The reliance on relational databases like MySQL or PostgreSQL means the Hive Metastore often falters as the metadata volume scales, leading to slow query responses for growing data lakes.

Concurrency Limits: Large-scale deployments with numerous services frequently hit connection limits, disrupting metadata access.

Overhead in Metadata Retrieval: Retrieving metadata for highly partitioned tables introduces latency, particularly in complex schemas.

Schema Evolution Pain Points: Adjusting schemas, particularly for massive datasets, is cumbersome and slows down iterative development cycles.

Single Point of Failure: Downtime in the metastore can paralyze critical data processing pipelines.

High Maintenance Overhead: Managing schema migrations and version upgrades adds a considerable operational burden.

While foundational, the Hive Metastore’s centralized role presents challenges that can hinder the agility and scalability modern data ecosystems demand. Addressing these limitations is crucial for efficient data management and query performance.

Fragmentation: From One Catalog to Many

As the limitations of Hive Metastore became apparent, new systems emerged to fill the gaps. Lakehouse systems like Apache Hudi, Iceberg, and Delta Lake solved many challenges with Hadoop-centric data lakes. Many enterprise companies rapidly adopt the lakehouse formats, migrating from the proprietary data warehouses.

The success of lakehouse formats has driven a rapid proliferation of data catalogs. Each vendor started to build their proprietary implementation of the open(?) catalogs. What was once a unified ecosystem built around the Hive Metastore has splintered into a fragmented web of incompatible systems.

Today, in addition to the three open lakehouse formats, we have at least six or seven major catalog implementations to specific table formats or vendor ecosystems. Some vendors tightly couple their catalogs with commercial platforms, while others design them for open-source frameworks. This fragmentation poses significant challenges for companies adopting the lakehouse formats.

The vendor-specific implementation of the Iceberg catalog is understandable since every tool will try to provide the best-integrated experience for its customers. However, the integration will break the interoperability issues that break the very own promise of the “Open” in Open table formats.

The fragmentation of catalogs has implications that extend far beyond the technical realm. It increases the cost and complexity of managing data for businesses, making it harder to scale operations or adopt new technologies. It represents a missed opportunity for the industry to create a cohesive and interoperable ecosystem.

The Path Forward

At this point, two viable path forward options are available for companies adopting the lakehouse architecture.

An Uber Catalog - One Catalog to rule them all

I don’t think I need to write anything about this approach. We all know how it will end up.

Federated Catalog

Adopting a federated catalog model is a more realistic and scalable approach. This strategy acknowledges that multiple catalogs will coexist in the ecosystem and focuses on enabling seamless integration. Tools like Apache XTable exemplify this approach by providing bidirectional synchronization between table formats and various catalogs, including Hive Metastore, AWS Glue, and Unity Catalog. This model ensures that businesses can leverage the strengths of different catalog solutions while maintaining interoperability and reducing operational friction.

The federated model also aligns with the principles of open data ecosystems. Promoting synchronization rather than rigid centralization allows companies to adopt best-in-class tools without locking themselves into a single vendor’s proprietary ecosystem.

Conclusion: Living with the Chaos We Created

In our pursuit of better scalability, interoperability, and flexibility, we’ve built an ecosystem teeming with endless paths, none of which lead to a unified destination. It’s the chaos we’ve created and it’s the chaos we must now endure.

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