A deep dive into the concept and world of Apache Iceberg Catalogs
Tags: data, dataengineering, apache, iceberg
Published: 2024-03-01 15:17:06

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[Apache Iceberg is an open-source table format](https://www.dremio.com/blog/apache-iceberg-101-your-guide-to-learning-apache-iceberg-concepts-and-practices/) designed for data lakehouse architectures, enabling the organization of data on data lakes in a manner similar to tables found in traditional databases and data warehouses. This innovative table format provides a crucial abstraction layer, allowing users to leverage database-like features on their data lakes. Among its key features are ACID transactions, which ensure data integrity and consistency, time travel capabilities that allow users to access historical data snapshots, and robust table evolution mechanisms for managing partitions and schema changes. By integrating these functionalities, [Apache Iceberg](https://iceberg.apache.org/) transforms data lakes into more structured and manageable environments, facilitating advanced data analytics and management tasks.

## The Role of Catalogs in Apache Iceberg

The catalog mechanism is a cornerstone in the functionality of Apache Iceberg tables, providing a crucial layer of organization and accessibility, even though the specifics of its implementation are beyond the Iceberg specification. A catalog in Iceberg serves as a registry for tables, tracking Iceberg tables to ensure they are discoverable by compatible tools, thereby facilitating seamless integration and usage. Moreover, catalogs are instrumental in maintaining a consistent view of the data, which is essential for the integrity and reliability of ACID transactions. By acting as the source of truth, catalogs enable concurrent transactions to reference them and [ascertain the status of other ongoing transactions, effectively determining if another transaction was committed while theirs was in progress](https://www.dremio.com/subsurface/the-life-of-a-write-query-for-apache-iceberg-tables/). This mechanism is vital for maintaining data consistency and ensuring that the system provides ACID guarantees, thereby enhancing the robustness and reliability of data operations within the Iceberg ecosystem.

### Challenges with Catalogs and Choosing the Right Catalog For you

When selecting a catalog for Apache Iceberg, several key factors should guide your decision: compatibility with your current tools, the additional functionalities or integrations the catalog offers, and its maintainability. It's crucial to choose only one catalog for managing your tables because, upon the completion of a transaction, only the active catalog is updated. Utilizing multiple catalogs could result in them referencing outdated table states, leading to consistency issues. If there is an absolute need to employ multiple catalogs, a feasible approach is to designate a single catalog for write operations while others are used solely for reading. However, this setup demands the implementation of custom systems to synchronize the read-only catalogs with the primary to ensure they reflect the most current table state, maintaining consistency to the greatest extent possible.

### Service and File-System Catalogs

The distinction between a [service catalog and a file-system](https://www.youtube.com/watch?v=4hcfveg1t70) catalog in Apache Iceberg is fundamental to understanding their operational dynamics and use cases. Service catalogs, which constitute the majority, involve a running service that can be either self-managed or cloud-managed. These catalogs utilize a backing store to maintain all references to Iceberg tables, with locking mechanisms in place to enforce ACID guarantees. This setup ensures that when a table is modified, the catalog updates references accurately, preventing conflicting changes from being committed. 

On the other hand, the "Hadoop Catalog" represents a file-system catalog that is compatible with any storage system. Unlike service catalogs, the Hadoop Catalog does not rely on a backing store but instead uses a file named "version-hint.text" on the file system to track the latest version of the table. This file must be updated whenever the table changes. However, since not all storage systems provide the same level of atomicity and consistency in file replacement, this method can lead to potential inconsistencies, especially in environments with high concurrency. Therefore, while the Hadoop Catalog might be suitable for evaluating Iceberg's capabilities, it is generally not recommended for production use due to these potential consistency issues.

## A tour of Apache Iceberg Catalogs

In the following section, we will delve into the diverse range of currently existing catalogs available within the Apache Iceberg ecosystem. Each catalog offers unique features, integrations, and compatibility with different data storage systems and processing engines. Understanding the nuances of these catalogs is crucial for architects and developers to make informed decisions that align with their specific data infrastructure needs and operational goals. We'll explore a variety of catalogs, including those that are widely adopted in the industry as well as emerging options, highlighting their respective advantages, use cases, and

### Nessie

[Nessie is an innovative open-source catalog](https://projectnessie.org/) that extends beyond the traditional catalog capabilities in the Apache Iceberg ecosystem, introducing [git-like features to data management](https://www.dremio.com/blog/what-is-nessie-catalog-versioning-and-git-for-data/). This catalog not only tracks table metadata but also allows users to capture commits at a holistic level, enabling advanced operations such as multi-table transactions, rollbacks, branching, and tagging. These features provide a new layer of flexibility and control over data changes, resembling version control systems in software development. 

Nessie can be either self-managed or cloud-managed, with the latter option available through the Dremio Lakehouse Platform. Dremio integrates Nessie into its [Dremio Cloud product](https://www.dremio.com/blog/managing-data-as-code-with-dremio-arctic-easily-ensure-data-quality-in-your-data-lakehouse/), offering a seamless experience that includes automated table optimization alongside Nessie's robust cataloging capabilities. This integration underscores Nessie's versatility and its potential to enhance data governance and management in modern data architectures.

### Hive

The Hive catalog offers a seamless integration pathway for organizations already utilizing Hive in their data architectures, allowing them to leverage their existing Hive metastore as an Apache Iceberg catalog. This integration facilitates a smooth transition to Iceberg's advanced features while maintaining compatibility with the existing Hive ecosystem. By using the Hive catalog, users can avoid the redundancy of maintaining separate metadata stores, streamlining their data management processes. However, for those not currently using Hive, adopting the Hive catalog would necessitate setting up and running a Hive metastore service. This requirement introduces an additional layer of infrastructure that might be better served using an option with additional features.


### REST

The REST catalog represents a unique approach in the Apache Iceberg ecosystem, serving not as a standalone catalog but as a universal interface that can be adapted for any catalog type. It is based on an OPEN API specification that can be implemented in any programming language, provided that the specified endpoints are adhered to. This flexibility allows for the creation of custom catalogs tailored to specific use cases, enabling developers and organizations to contribute new catalog types to the community without the need to develop bespoke support for a myriad of engines and tools.

Among the catalogs utilizing this specification are Tabular, which offers a "headless warehouse" solution; Unity Catalog from Databricks, which primarily manages Delta Lake tables but also provides Iceberg table access through its UniFormat feature; and Gravitino, an emerging open-source catalog project. The community's vision is that all catalogs will eventually interface through this REST specification, simplifying tool integration by requiring only a single interface. However, it's important to note that the specification is still evolving, with version 3 under discussion and development, which is anticipated to introduce additional endpoints for greater extensibility and the incorporation of custom behaviors.


### AWS Glue

The AWS Glue catalog is an integral component of the AWS ecosystem, providing a fully managed catalog service that integrates seamlessly with other AWS tools such as Redshift and AWS Athena. As a native AWS service, it offers a streamlined experience for users deeply embedded in the AWS infrastructure, ensuring compatibility and optimized performance across AWS services. A notable feature of the AWS Glue catalog is its support for auto-compaction of tables through the AWS Lake Formation service, enhancing data management and optimization. While the AWS Glue catalog is an excellent choice for those committed to the AWS platform, organizations operating in on-premises environments or across multiple cloud providers might benefit from considering a cloud-agnostic catalog to ensure flexibility and avoid vendor lock-in.


### Snowflake Catalog

The Snowflake Iceberg catalog offers a unique integration, allowing Snowflake to manage Apache Iceberg tables that are stored externally on your storage system. This integration aligns with Snowflake's robust data management capabilities while offering the cost benefits of utilizing external storage. However, there are limitations to consider: all table creation, insertions, updates, and deletions must be conducted within Snowflake, as the Snowflake SDK currently only supports reading operations from Spark. While this setup allows users to leverage some cost savings by storing tables outside of Snowflake, it comes with a trade-off in terms of flexibility. Users do not have access to the full range of open-ecosystem tools for managing Snowflake-managed Iceberg tables, which could be a significant consideration for organizations that rely on a diverse set of data tools and platforms.


### LakeFS Catalog

Initially, there was a compatibility issue between LakeFS, a file-versioning solution, and Apache Iceberg due to a fundamental difference in their design: Iceberg relies on absolute paths in its metadata to reference files, whereas LakeFS uses relative paths to manage different file versions. To bridge this gap, LakeFS introduced its own custom catalog, allowing it to integrate with Apache Iceberg. 

However, as with any custom catalog, there's a dependency on engine support, which, at the time of writing, appears to be limited to Apache Spark. While versioning is a powerful feature for data management, users looking to leverage versioning for their Iceberg tables might find the built-in table versioning features of Iceberg or the catalog-level versioning offered by Nessie to be more universally compatible and supported options, especially when considering broader ecosystem integration beyond LakeFS's Iceberg catalog.

### Other

As the Apache Iceberg community evolves, there's a noticeable shift away from certain catalogs, primarily due to concerns like maintenance challenges or inconsistent implementations. Two such catalogs are the JDBC catalog and the DynamoDB catalog. The JDBC catalog, which enabled any JDBC-compatible database to function as an Iceberg catalog, is seeing reduced usage. This decline is likely due to the complexities and variances in how different databases implement JDBC, potentially leading to inconsistencies in catalog behavior. Similarly, the DynamoDB catalog, initially used in the early stages of AWS support for Iceberg, is also falling out of favor. The community's pivot away from these catalogs underscores a broader trend towards more robust, consistently supported, and feature-rich catalog options that align with the evolving needs and standards of Iceberg users and developers.

## Conclusion

Apache Iceberg stands as a transformative force in the data lakehouse landscape, offering a structured and efficient way to manage data on data lakes with features traditionally reserved for databases and data warehouses. The journey through the diverse world of Apache Iceberg catalogs highlights the importance of these components in ensuring data accessibility, consistency, and robust transactional support. From the integration-friendly Hive catalog to the innovative Nessie catalog that brings git-like versioning to data, each catalog serves a unique purpose and caters to different architectural needs and preferences.

As we've explored, choosing the right catalog is crucial, balancing factors like compatibility, functionality, and the specific context of your data ecosystem. Whether you're deeply embedded in the AWS infrastructure, leveraging the AWS Glue catalog, or exploring the versioning capabilities of LakeFS or Nessie, the decision should align with your strategic objectives and operational requirements.

The evolution away from certain catalogs, like the JDBC and DynamoDB catalogs, underscores the community's drive towards more reliable, feature-rich, and consistent catalog implementations. This shift is a testament to the ongoing maturation of the Iceberg ecosystem and its users' commitment to adopting practices and tools that enhance data reliability, scalability, and manageability.

As Apache Iceberg continues to evolve, so too will its ecosystem of catalogs, each adapting to the emerging needs of data professionals seeking to harness the full potential of their data lakehouses. Embracing these tools and understanding their nuances will empower organizations to build more resilient, flexible, and efficient data architectures, paving the way for advanced analytics and data-driven decision-making.