What is Apache Polaris?
Apache Polaris is an open-source catalog designed for Apache Iceberg. Originally created at Snowflake, it was donated to the Apache Software Foundation in 2024 with Dremio as a co-contributor, and it graduated from the Apache Incubator to a top-level project in February 2026. Polaris provides a vendor-neutral implementation of the Iceberg REST Catalog specification.
As organizations adopted the Open Data Lakehouse, they found that while storing data in open Apache Parquet files reduced storage lock-in, they could still be locked into proprietary catalogs. If an organization used a specific vendor’s proprietary catalog to manage their Iceberg tables, it was often difficult to let a competing query engine read that data. Polaris addresses this friction by providing an open implementation for catalog management, centralized security, and cross-engine interoperability.
The Architecture of Polaris
Polaris does not execute SQL queries, nor does it store physical data files. It functions as the central control plane, tracking the current metadata of Iceberg tables and enforcing who has access to them.
Implementing the REST Catalog API
Early Iceberg implementations relied on legacy catalogs (like the Hive Metastore or AWS Glue) which were originally designed for directory-based architectures. This caused inefficiencies and required engine-specific client code.
The Apache Iceberg community developed the Iceberg REST Catalog API, a standardized protocol defining how query engines interact with catalog metadata. Polaris is a native implementation of this REST API. Because it follows the open standard, engines that support the REST catalog (such as Dremio, Trino, Apache Spark, or Snowflake) can connect to Polaris without custom integration code.
Centralized Role-Based Access Control (RBAC)
Historically, enforcing security across a multi-engine lakehouse was difficult. If a data team defined a masking policy in Snowflake, that policy did not apply when a data scientist queried the exact same S3 bucket using Apache Spark.
Polaris centralizes governance. It introduces a hierarchical Role-Based Access Control (RBAC) model. Security administrators define permissions at the Catalog, Namespace, or Table level directly within Polaris. When Dremio or Spark connects to Polaris to request the location of an Iceberg table, Polaris evaluates the credentials of the requesting entity. If the entity lacks permission, Polaris denies the metadata request, and the engine does not receive the table location or storage credentials. This establishes an engine-agnostic security perimeter.
Avoiding Vendor Lock-In
The donation of Polaris to the Apache Software Foundation was a significant shift in the data ecosystem. It places a core control plane of the modern data lakehouse under open-source, community-driven governance.
Because Polaris is open-source, organizations can run it locally in Docker for testing, deploy it securely on their own internal Kubernetes clusters, or use managed services built on it (like Snowflake Open Catalog or Dremio’s catalog). Because it relies on standard Iceberg storage and open REST APIs, an organization can move its catalog between providers by migrating metadata pointers, without physically moving the underlying data.
Integration in the AI Era
In the context of the Agentic Lakehouse, a standardized catalog is important. AI agents work better with clearly defined data structures, which reduce the room for hallucinations.
When an AI agent (built on LangChain or DSPy) executes a workflow, it interacts directly with Polaris to discover the available datasets. Polaris provides the agent with schema definitions and enforces the agent’s specific security roles. This keeps an AI agent deployed for the HR department from querying sensitive financial Iceberg tables in a namespace it has no grants on.
Summary of Technical Value
Apache Polaris provides an open, community-governed implementation of Iceberg catalog management. By combining the Iceberg REST API with a centralized RBAC security model, it allows organizations to operate secure, multi-engine data lakehouses. It is a sign of the open data ecosystem maturing, and it helps enterprises keep ownership and interoperability across their architectural stack.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.