Preparing data for AI projects is about more than fast storage or shiny new table formats – it all starts with selecting the right data catalog to anchor your entire ecosystem.
The catalog you pick specifies how your tables are discovered, versioned, secured, and evolved, which, in turn, impacts the reliability and clarity of every single dataset that your models interact with. It acts as the quiet orchestrator behind consistent schemas, reproducible training inputs, and trustworthy lineage, ensuring that the data feeding your AI workflows is as reliable as the systems built on top of it.
Now you see why that choice plays such a huge role when building pipelines with AI-ready data. If you’re using Iceberg, an Iceberg REST Catalog sounds like the right fit. But is it really the best catalog for your project requirements?
Read this article to dive into the world of data catalogs and explore other Iceberg catalogs, including their advantages, weaknesses, and use cases where each excels.
What is an Iceberg REST Catalog?
Apache Iceberg REST Catalog is an API-driven solution to manage Apache Iceberg tables that doesn’t bind your data layout to any particular storage or metastore technology. Instead of connecting directly to Hive, Glue, or a custom catalog, you can use a REST interface that manages table metadata, versioning, and schema evolution.
The benefits for data practitioners are clear: better separation of responsibilities, simpler cross-environment portability, and a more straightforward approach to orchestrating table operations with whatever tools you already have. There’s no expensive infrastructure to manage, just an HTTP interface that keeps your Iceberg universe organized.
Understanding Apache Iceberg Tables and Catalogs
Apache Iceberg is a table format designed to transform large analytic datasets into well-structured, versioned, and query-friendly tables, rather than a collection of Parquet files spread across cloud storage.
An Iceberg table keeps track of everything – schemas, partitions, snapshots, and metadata – so engines like Spark, Trino, Flink, and Snowflake may read and write without interfering with one another.
In this sense, a catalog serves as a lookup service that tells query engines where each table’s metadata is stored and how to access it; like a directory for all your Iceberg tables. Whether powered by Hive, Glue, JDBC, or a REST API, the catalog ensures that table discovery, permissions, and actions are uniform across all technologies. Tables and catalogs work together to provide an engine-agnostic data layer designed for efficiency, atomicity, and easy evolution as your workloads grow.
REST Catalogs vs. Hive Metastore & JDBC Catalogs
How does the Iceberg REST catalog compare to alternative catalogs like Hive Metastore and JDBC? Here’s a comparison table that takes you across architecture, complexity, use cases, and more.
| Feature | REST Catalog | Hive Metastore Catalog | JDBC Catalog |
|---|---|---|---|
| Architecture | Stateless, API-driven service accessed over HTTP | Centralized metadata service originally built for Hive | Thin catalog layer backed by a relational database |
| Consistency Model | Strong consistency built into the Iceberg REST spec | Can have concurrency issues under heavy parallel writes | Depends on the underlying database’s isolation guarantees |
| Deployment | You can host it anywhere (Kubernetes, VM, serverless) | Requires running and maintaining the HMS service | Requires provisioning and tuning a relational database |
| Engine Interoperability | Engine-agnostic and aligned with Iceberg’s native APIs | Broad support but rooted in legacy Hive semantics | Supported by engines that implement Iceberg’s JDBC catalog API |
| Performance Characteristics | Minimal catalog overhead; HTTP calls scale horizontally | Metadata ops can bottleneck at a large scale | Scales well if the database is tuned; might hit connection limits |
| Security and Access Control | Modern auth patterns (tokens, OAuth, proxies) | Kerberos-heavy or legacy ACL models | Relies on database authentication and network controls |
| Cloud / Multi-environment portability | Catalog endpoint travels anywhere | Tightly coupled to Hadoop-era deployments | Good, but tied to the database instance’s lifecycle |
| Operational Complexity | Low; it’s a simple stateless service | Medium–high; HMS can be fragile without care | Medium; DB backup, tuning, and high availability required |
| Use Cases | Modern, multi-engine Iceberg deployments; clean decoupling | Legacy Hive ecosystems or mixed Hive/Iceberg environments | Teams looking for a simple catalog without running HMS |
Pros and Cons of Iceberg REST Catalogs
Now that you know how an Iceberg REST catalog compares to alternatives, let’s have a deeper look into its advantages and limitations. Evaluating these factors is essential when selecting a catalog solution for your organization.
Pros
- Lightweight Access Through REST APIs – You interact with the catalog through simple HTTP requests, eliminating the need for heavyweight services. This makes metadata activities easy to automate and incorporate into current workflows.
- Simplified Deployment Compared to Hive Metastore – Because it’s stateless and self-contained, a REST Catalog removes the operational burden of running an HMS cluster. You can deploy it on containers, serverless platforms, or a small virtual machine without any extra configuration.
- Engine-Agnostic Interoperability (Spark, Flink, Trino) – REST is Iceberg’s most future-proof catalog interface, built to work seamlessly across several engines. This ensures that metadata logic remains consistent even as your compute stack evolves.
- Scalable Support for Multi-Tenant Environments – Its stateless design grows horizontally, allowing you to segregate tenants using namespaces, authentication policies, and routing layers. This comes in handy for handling several teams or noisy production workloads.
- Flexible Integration Across Hybrid and Multi-Cloud Setups – A REST endpoint works well across cloud, on-premises, and hybrid environments without tying you to a single provider’s metastore. This opens the doors to designs where data and computing coexist in mixed environments.
Cons
- No Built-In Catalog-Level Version Control or Rollback Features – REST catalogs keep hold of table-level snapshots rather than whole catalog histories, so it doesn’t provide Iceberg versioning. If you need to undo structural changes across several tables, you must implement that logic elsewhere.
- Lack of Standardized Catalog-Level Branching and Environment Isolation – While individual tables can have branches, the specification does not include catalog-wide staging or environment isolation. This complicates development, testing, and production routines that rely on full-environment clones.
- Complex Multi-Client Configuration Management – Different engines may require slightly different catalog configurations (auth, endpoints, TLS). Keeping them aligned gets difficult as the number of clients increases.
- Authentication and Authorization Complexity Across Implementations – Because implementations vary, so do authentication models: tokens, OAuth, mTLS, and proxies. Due to this inconsistency, you must develop your own unified security strategy.
- Performance Overhead for Large-Scale Metadata Operations – Heavy metadata scans or large namespace listings may cause delays via HTTP. On a massive scale, you may need to implement caching, sharding, or a highly optimized backend.
- Limited Data Lineage and Governance Visibility – REST catalogs rely on table metadata rather than cross-table relationships or audit trails, resulting in limited visibility into data lineage and governance. Governance tools must be integrated independently to create a complete lineage view.
- Synchronization Issues with Frequently Changing Data Sources – High-churn workloads can result in frequent metadata updates, and when many engines write aggressively, consistency must be carefully controlled to avoid clashes.
- No Multi-Table Atomic Operations – REST catalogs operate at the table level, so atomic changes that span multiple tables, such as coordinated schema modifications, require bespoke orchestration outside the catalog.
Top Iceberg REST Catalog Alternatives
lakeFS Iceberg REST Catalog
lakeFS Iceberg REST Catalog adds a versioned backbone to your Iceberg universe, letting you branch entire catalogs – not just individual tables – for true environment isolation. It supports multimodal branching across both data and metadata, so experiments, migrations, and schema changes can unfold safely in parallel.
Teams can audit, validate, and publish changes using repeatable workflows that resemble Git, then integrate those same patterns into CI/CD pipelines for deterministic and reproducible releases. The result is full-stack version control for your lake: tables, metadata, and operations all captured in a unified history you can roll back, inspect, or promote with confidence.
Hive Metastore (HMS)
Hive Metastore is a long-standing Hadoop metadata service that stores table definitions, partitions, and schema information in a central relational database. Its strength is its broad ecosystem compatibility – Spark, Presto, Hive, and many older tools can communicate with it right out of the box.
While it might be slightly cumbersome to use, HMS remains a reliable option for aging platforms or hybrid setups that require consistent metadata across many engines.
AWS Glue Catalog
AWS Glue Catalog is a fully managed, cloud-native metastore that supports automated scaling, tight connection with S3, and simple metadata administration. It provides schema tracking, table discovery, and security via IAM, making it appealing to teams that already work within the AWS ecosystem.
Glue reduces the amount of work needed to manage operations while allowing for serverless data analysis, but it’s designed to work best with AWS processes – which might be just perfect if you run on this cloud provider.
Nessie / Project Nessie
Project Nessie enables Git-style versioning in data catalogs, allowing you to establish branches, make isolated changes, run validations, and merge when ready. It natively supports Iceberg, enabling repeatable analytics, development sandboxes, and safer production releases – all supported by a time-traveling catalog.
Nessie converts the metadata layer into a version-controlled system, ensuring that data operations are consistent with modern software standards.
Tabular Catalog
Tabular Catalog is a commercial, cloud-hosted Iceberg catalog developed by Iceberg’s designers, featuring a fully managed control plane, robust governance capabilities, and unified metadata management across engines.
It manages snapshots, schema evolution, and access control while optimizing metadata performance in the background. Tabular strives to completely remove the operational load while giving a polished, enterprise-ready Iceberg experience.
Polaris
Polaris is an open, vendor-neutral implementation of the Iceberg REST Catalog, prioritizing portability, consistency, and simplicity. It strictly adheres to the official Iceberg REST specification, providing a clean, engine-agnostic interface for managing tables and data.
Polaris was designed for teams looking for a standardized, open-source catalog with consistent behavior across Spark, Flink, Trino, and other platforms – without tying metadata to any cloud provider or proprietary service.
Iceberg REST Catalog Alternatives Comparison Table
| Category | lakeFS Iceberg REST Catalog | Hive Metastore (HMS) | AWS Glue Catalog | Nessie / Project Nessie | Tabular Catalog | Polaris |
|---|---|---|---|---|---|---|
| Performance and Scalability | High scalability via stateless architecture; leverages zero-copy branching for efficient, high-concurrency experimentation and testing without data duplication | Scales with tuning but can struggle under heavy concurrency; relies on DB + HMS service stability | Highly scalable and fully managed, with elastic performance tuned for AWS workloads | Scales well using a modern, service-based architecture; supports high-concurrency branching workflows | Enterprise-grade scalability with optimized metadata services and caching | Stateless REST design supports horizontal scaling; performance depends on backing storage and deployment |
| Versioning and Governance | Full Git-like versioning for the entire data lake; supports branching, committing, merging and reverting at the repository level | Minimal versioning; no catalog-level branching or time travel | Limited versioning; focuses on schema management rather than full history | Full Git-style branching, commits, merges, and catalog-level time travel for governance | Strong built-in governance, table-level versioning, and policy controls | No built-in multi-table versioning; governance depends on the underlying deployment |
| Metadata Management | Holistic state management that versions metadata alongside data files | Stores table definitions and partition info; lacks modern Iceberg semantics without extensions | Managed metadata with schema registry features; AWS-native integration | Metadata stored with version history, enabling reproducible environments | Advanced metadata optimization, snapshot management, and automated maintenance | Clear and spec-aligned Iceberg metadata handling via REST endpoints |
| Multi-Table Atomic Operations | Fully supported; atomic commits allow changes across multiple tables (and files) to be applied, validated, or rolled back simultaneously as a single unit | Not supported; operations are table-scoped | Not supported; Glue focuses on schema rather than atomicity | Supported through versioned commits that can bundle changes across tables | Partially supported via managed workflows, though often table-scoped | Not supported; operations remain table-level per Iceberg spec |
| Cloud vs. On-Premises Flexibility | Cloud-agnostic and portable; deployable on Kubernetes, Docker, or as a managed service | Flexible but often tied to Hadoop or on-prem clusters | AWS-only; not portable across clouds | Cloud-agnostic and deployable anywhere Kubernetes or containers run | Cloud-first SaaS; less suited for on-prem deployments | Highly portable and deployable on-prem, in containers, or across clouds |
| Holistic, multimodal data management | Yes. Unifies versioning for Iceberg tables, unstructured data, and code, enabling a single control plane for the entire data lifecycle | No. Stores table definitions and partition info only; strictly decoupled from underlying files or non-table assets. | No. Managed metastore focused on schema tracking and table discovery; distinct from data storage operations | Partial (metadata). Versions the catalog metadata layer but does not manage the underlying data files or non-Iceberg objects | No. Focuses on managed Iceberg table optimization and metadata governance | No. Strictly handles table metadata via the REST API spec; does not manage physical data assets |
How to Choose the Right Catalog for Your Use Case
Compatibility with Existing Iceberg Infrastructure
Choosing the best catalog for your case starts with determining how well it complements the Iceberg plumbing you already use – your compute engines, storage structure, and metadata patterns.
Some catalogs, such as HMS and Glue, fit well into older or cloud-specific ecosystems, whereas REST-based or versioned catalogs provide more current, engine-agnostic alignment. The closer the catalog matches your existing architecture, the more smoothly your activities will run.
Migration Path from Existing Catalogs
A smart choice relies on how smoothly you can transition from your present catalog without disrupting downstream jobs or rewriting half of your pipelines. HMS users frequently migrate to Glue or REST catalogs with minor table restructuring, whereas teams using Nessie or Tabular typically plan a gradual migration to implement versioning or managed governance. The best solution reduces friction while allowing you room to expand.
Tool Ecosystem Support
Your catalog should integrate with the engines that power your workloads. If you rely significantly on Spark or Flink, extensive ecosystem support is critical. If Trino or Dremio is important to your analytics stack, ensure that the catalog has native connectors or clean REST connections. The more engines you use, the more important it is to select a catalog that is neutral and consistent across them.
Authentication and Authorization Requirements
Different catalogs have varying security expectations – Glue relies on IAM, HMS frequently employs Kerberos-era principles, and REST catalogs may utilize tokens, OAuth, or mTLS. Your decision is based on which model best fits your platform’s security requirements and the level of administrative overhead you’re willing to accept. Aim for a catalog that supports your existing identity systems rather than requiring extensive rewiring.
Unique Catalog Features
Some catalogs stand out because they go beyond basic metadata searches. lakeFS, for example, functions as a versioned catalog, providing full-environment branching of both data and metadata – enabling multimodal data management that spans tables, files, and operational workflows. Others focus on governance, automation, or SaaS convenience.
Choosing the correct choice entails determining which superpowers – versioning, automation, portability, or managed reliability – are most important to your data platform’s future.
Understanding the lakeFS Iceberg REST Catalog (IRC)
How IRC Works with lakeFS Repositories and Branches
The lakeFS Iceberg REST Catalog (IRC) treats each lakeFS branch as a completely separate catalog state, allowing engines to query, write, and develop Iceberg tables against consistent snapshots of your repository.
Each branch provides its own view of tables and metadata, allowing development, experimentation, and production to run concurrently without interfering with one another. Because the catalog is merely another layer on top of lakeFS’ versioned repository, all changes to the tables – schemas, manifests, snapshots – travel with the underlying data.
Audit, Validate, and Publish Workflow
Table modifications in IRC follow the same controlled workflow that lakeFS employs for data: you establish a branch, perform transformations, test expectations, and validate outputs before merging.
This allows for safe “pre-production” analytics, in which pipelines execute full dry runs on real data, identify quality concerns early, and only publish once everything passes inspection. Merges become clean promotion phases, transforming your data operations into consistent, dependable release cycles.
Full-Catalog State Versioning for Reliable Pipelines
Because lakeFS versions data and metadata together, IRC allows for comprehensive catalog time travel. Every table definition, schema evolution, and snapshot is recorded at the time your branch was established. Pipelines can be replayed exactly as they were tested by using the specific catalog state at that time, which eliminates differences and risks associated with running them.
This full-catalog versioning facilitates debugging, makes experimentation safer, and significantly improves the predictability of production workloads, providing your data platform with a robust backbone, regardless of the number of moving parts involved.
How lakeFS Enhances Iceberg REST Catalog Workflows
If you’re looking to add data versioning capabilities to your Iceberg REST Catalog, with lakeFS you’ll get that and much more:
Complete Data and Metadata Version Control
lakeFS applies full versioning to both data files and Iceberg information, allowing you to capture entire table states at any given time. This ensures reproducibility, facilitates debugging, and provides a clear audit record for each modification.
Multimodal Repository Branching
You can branch whole repositories, including data, metadata, and table definitions, to conduct experiments, migrations, and feature development in completely isolated contexts. Each branch functions as its own catalog, with no duplication or risk to production.
Safe Audit, Validate & Publish Workflows
Before merging, teams can use dedicated branches to conduct transformations, apply schema changes, and validate quality. The method transforms “publish to production” into a planned, testable step rather than a leap of faith.
Integrate into Versioned CI/CD Pipelines
lakeFS introduces Git-like processes into data pipelines, allowing CI/CD systems to run jobs against consistent, branch-specific snapshots. Releases become predictable as each run refers to a deterministic version of your catalog and data.
Zero-Copy Operations for Efficient Testing
The use of copy-on-write semantics enables efficient testing of large datasets without duplication. This facilitates quick experimentation, even with large Iceberg tables.
Conclusion
Choosing the right Iceberg catalog affects the entire backbone of your AI-ready data – it determines how consistently your metadata evolves, how cleanly your engines discover tables, and how safely you scale experiments.
A strong catalog opens the doors to uniform schemas, predictable table states, and seamless interoperability across Spark, Flink, Trino, and the growing set of AI tooling options. It also impacts how easy it is to version, audit, and reproduce the datasets that feed models, which is critical when training systems that rely on accurate lineage and repeatable inputs.
The catalog serves as your trust control plane, with no need to untangle drift, failed pipelines, and opaque data histories.
