What is Apache Polaris? Unifying the Iceberg Ecosystem

Published: at 12:00 PM

Read the complete Open Source and the Lakehouse series:

Treating thousands of Parquet files as a unified database table requires a brain. Apache Iceberg provides the metadata structure to do this, but the Iceberg specification alone does not spin up a server, manage security roles, or handle network requests. You need a catalog service to orchestrate those root metadata pointers.

Until recently, that catalog layer threatened to fragment the entire lakehouse vision. Vendors began building their own proprietary catalogs to track Iceberg tables, trapping users in the exact data silos Iceberg promised to eliminate. Apache Polaris solves that fracture.

The Catalog Fragmentation War

The core promise of the modern data lakehouse is compute independence. In traditional database architectures, data storage and query processing are bundled within a single proprietary system. You cannot run queries against your data without using the query planner, query optimizer, and execution engine provided by that database vendor. The lakehouse architecture breaks this monopoly by storing data in open, vendor-neutral file formats (such as Apache Parquet) and table formats (such as Apache Iceberg) inside open cloud storage buckets.

With the files stored openly, you can query your data using any engine: Apache Spark for batch ETL, Apache Flink for real-time streaming, Trino for ad-hoc queries, and Dremio for high-performance interactive business intelligence.

However, table formats rely on a metadata catalog to track the current state of a table. A catalog acts as a centralized database pointer registry. When a write engine commits a transaction, it writes a new metadata file to object storage and updates the catalog pointer to reference this new file. If multiple engines attempt to write to a table without a shared coordinator catalog, they will write competing metadata files, leading to split-brain states, overwrites, and data corruption.

As Apache Iceberg gained mass adoption across the enterprise, the catalog layer became a critical strategic battleground. Legacy data platform vendors quickly realized that while they could no longer force customers to store data in proprietary file formats, they could still lock customers into their ecosystems by controlling the catalog.

Vendors began wrapping Iceberg tables in proprietary catalog managers. Under this setup, if a client engine wanted to query a table, it had to connect to the vendor’s proprietary catalog service. If you wanted to ingest data using Flink and query it using Dremio, you had to build complex sync processes to replicate metadata between the Flink catalog registry and the Dremio catalog registry. If the sync lagged, Trino and Dremio would query stale metadata, producing incorrect query results.

This metadata synchronization overhead created new data silos. Organizations found themselves managing multiple catalogs, with each compute engine maintaining a separate view of the lakehouse state. The open promise of the lakehouse was compromised. To restore compute-storage independence, the industry required a standardized, open catalog protocol. This standard emerged as the Apache Iceberg REST Catalog specification.

The Iceberg REST API Standard

The Iceberg community addressed the catalog fragmentation problem by defining a standard REST API specification. Instead of defining a specific backend database implementation, the REST Catalog specification defines the HTTP endpoints, request headers, query parameters, and JSON payloads that clients and servers must use to communicate table metadata.

This API-first approach changed how compute engines integrate with catalogs. Previously, support for a new catalog required writing custom Java connector classes for every query engine. If you wanted to use a custom database catalog, you had to write and maintain catalog integrations for Spark, Flink, Trino, and Presto.

Under the REST specification, query engines implement the REST client interface once. Any catalog server that implements the REST HTTP endpoints can serve metadata to any REST-compliant client engine instantly.

Apache Polaris is a fully featured, open-source backend implementation of this Iceberg REST Catalog specification. It provides a stateless, scalable catalog service that manages table metadata and access control policies while complying with the open API spec.

Because Polaris adheres strictly to the REST standard, it acts as a universal adapter for the lakehouse. A Python script using PyIceberg can resolve namespace paths, a Spark batch job can write data, and a Dremio query coordinator can perform query planning, all routing requests through a single Polaris catalog endpoint. By serving as a unified metadata registry, Polaris eliminates catalog duplication and ensures that all engines see a consistent, real-time snapshot of table states.

The Solution of Open Governance and the ASF

In the technology industry, open source is not always synonymous with open governance. A software project can be open source, allowing you to view and download its source code for free, while its roadmap, licensing terms, and release cycles remain controlled by a single commercial vendor. If that vendor decides to change the license of future releases or deprecate integrations that compete with its paid offerings, community users have little recourse.

To prevent commercial capture of the lakehouse brain, the co-creators of Polaris (Dremio and Snowflake) donated the project to the Apache Software Foundation (ASF) as an incubating project.

This donation was a critical milestone for the lakehouse ecosystem. The ASF is a non-profit corporation that provides organizational, legal, and financial support for open-source software projects. The foundation operates under a strict model of open governance known as “The Apache Way.” Under this model, project decisions are made by a diverse Project Management Committee (PMC) composed of individual contributors, rather than a single corporate entity. No single vendor can monopolize the project roadmap or restrict access to its integrations.

Open governance protects enterprise investments in several ways:

  1. Vendor Neutrality: The ASF legally owns the Polaris trademark, code repositories, and documentation. No commercial vendor can alter the licensing terms or lock key features behind proprietary tiers.
  2. Community-Driven Roadmap: Feature priorities are decided through open consensus, ensuring the catalog evolves in a direction that benefits the entire ecosystem rather than a single vendor’s commercial strategy.
  3. Engine-Agnostic Design: Because no single query engine vendor controls the project, Polaris maintains equal integration quality for all engines, preventing favoritism toward specific compute platforms.
  4. Long-Term Viability: If a commercial sponsor shifts its focus, the community and the PMC can continue maintaining and developing the project under the ASF umbrella, preventing project abandonment.

By placing Polaris under ASF governance, the community established a neutral foundation for lakehouse metadata management, guaranteeing that the catalog layer remains open and accessible to all.

Deep Dive into Polaris RBAC Hierarchy and Internals

Enterprise data platforms require granular security controls to prevent unauthorized access to sensitive datasets. Apache Polaris provides a robust, hierarchical Role-Based Access Control (RBAC) model designed specifically for metadata governance.

Unlike legacy access control models that secure data based on physical storage paths, Polaris defines privileges at the logical metadata level (catalogs, namespaces, tables, and views). This separation ensures that security policies remain consistent regardless of the compute engine or cloud storage region used to access the data.

The Polaris RBAC model consists of five key entities:

1. Principals

A principal is an identity that requests access to catalog resources. In Polaris, principals can represent human users, query engine connections, ETL pipelines, or automated scripts. Each principal is assigned a set of client credentials (a Client ID and Client Secret) used to authenticate via the OAuth2 token endpoint.

2. Principal Roles

A principal role is a logical grouping of permissions that can be assigned to one or more principals. For example, you can create a principal role named etl_developer for data engineers and a principal role named business_analyst for report creators. A principal can be assigned multiple principal roles.

3. Catalog Roles

A catalog role is a scope-restricted role defined within a specific catalog instance. Catalog roles represent functional access rights to metadata resources, such as sales_read_only or finance_administrator. Catalog roles are mapped to Principal Roles to grant actual access to principals.

4. Securable Objects

Securable objects are the logical resources managed by Polaris. The objects are organized in a strict hierarchical structure:

5. Privileges

Privileges are the specific actions allowed on securable objects. Polaris supports a detailed set of privileges, including:

The RBAC Mapping Flow

To grant a query engine access to a table, Polaris administrators construct a mapping chain:

[Principal: spark-executor]
       │ (belongs to)
       ▼
[Principal Role: IngestionEngine]
       │ (mapped to)
       ▼
[Catalog Role: SalesDataWriter]
       │ (granted privilege: TABLE_WRITE on)
       ▼
[Securable Object: catalog.sales.invoices]

This decoupled mapping structure provides significant administrative benefits. If you decide to migrate your data ingestion pipelines from Apache Spark to Apache Flink, you do not need to modify storage bucket policies or table-level permissions. You simply create a new principal for Flink, assign it to the existing IngestionEngine Principal Role, and the new engine instantly inherits the required write privileges.

Furthermore, Polaris enforces privilege inheritance down the logical hierarchy. If you grant the SalesDataReader catalog role the TABLE_READ privilege at the namespace level (e.g., catalog.sales), that role automatically inherits the read privilege for all tables and views created within that namespace, simplifying security management for large-scale data lakes.

Credential Vending vs. IAM Policy Sprawl

Securing a data lakehouse requires managing access to the physical cloud storage buckets (such as Amazon S3, Google Cloud Storage, or Azure Data Lake Storage) where the Parquet data files reside. Historically, organizations secured these buckets using one of two methods, both of which introduce severe security vulnerabilities.

The first method is distributing long-lived cloud access keys (such as AWS Access Keys and Secret Keys) to every query engine and compute cluster. In this model, the Spark configuration, Trino properties files, and developer notebooks are hardcoded with storage credentials. This approach creates a massive security risk. If a single developer notebook is compromised, the long-lived storage credentials are leaked, allowing unauthorized actors to bypass the catalog entirely and read or delete raw files directly from the cloud storage bucket.

The second method is creating complex, path-based IAM policies (such as AWS IAM Policies) for each compute engine. For instance, the marketing Spark cluster is assigned an IAM role that allows access to s3://my-bucket/marketing/*, while the finance cluster is restricted to s3://my-bucket/finance/*.

This approach leads to what security architects call “IAM Policy Sprawl.” As the number of tables, departments, and compute engines grows, the number of IAM policies multiplies, creating an administrative bottleneck. Cloud providers enforce strict limits on the size and number of IAM policies, forcing administrators to use overly broad wildcard policies (e.g., s3://my-bucket/*) to keep up with request volume. This violates the security principle of least privilege.

Furthermore, path-based IAM policies cannot enforce relational table security. An IAM policy can only restrict access to folder paths; it cannot enforce schema validation, detect snapshot modifications, or prevent a user from reading raw Parquet files directly while bypassing table-level access logs.

The Polaris Credential Vending Solution

Apache Polaris resolves these security challenges using a process called credential vending. Under this model, compute engines do not hold long-lived storage credentials. Instead, the Polaris catalog server acts as a secure credential broker between the query engines and the cloud storage provider.

The credential vending sequence proceeds as follows:

  1. Authentication: The query engine authenticates with Polaris using its OAuth2 client credentials and requests the metadata location for a specific table (e.g., sales.invoices).
  2. Authorization: Polaris validates the client’s RBAC mapping, verifying that the principal has the TABLE_READ privilege for the requested table.
  3. Token Request: Polaris contacts the cloud provider’s token service (such as AWS STS, Azure Active Directory, or Google Cloud IAM) using its own highly authorized identity.
  4. Token Scoping: Polaris requests a set of temporary security credentials, attaching a strict session policy that restricts read and write operations to the exact storage folder where the table’s Parquet files reside (e.g., s3://my-bucket/sales/invoices/*).
  5. Token Delivery: The cloud token service returns the temporary credentials (which typically expire in 15 minutes) to Polaris.
  6. Metadata and Credential Return: Polaris packages the table’s metadata location, schema definition, and the temporary storage credentials into a standard JSON response and returns it to the query engine.
  7. Direct Storage Access: The query engine reads the Parquet data files directly from the storage bucket using the temporary credentials and discards them when the query execution finishes.
┌──────────────┐         1. GET Table Metadata          ┌─────────────────┐
│              ├───────────────────────────────────────>│                 │
│              │                                        │                 │
│ Query Engine │         6. Return Metadata + Token     │ Apache Polaris  │
│   (Client)   │<───────────────────────────────────────┤ (REST Catalog)  │
│              │                                        │                 │
└──────┬───────┘                                        └────────┬────────┘
       │                                                         │
       │                                   2. Verify RBAC        │ 3. Request
       │                                   & Table Paths         │   Scoped Token
       │ 7. Direct Read/Write                                    │
       │    (Temporary Scoped Token)                             ▼
       │                                                ┌─────────────────┐
       ▼                                                │  Cloud Provider │
┌──────────────┐                                        │  Token Service  │
│ Cloud Object │                                        │    (AWS STS)    │
│   Storage    │<───────────────────────────────────────┤                 │
│  (S3/ADLS)   │          5. Vend Scoped Token          │                 │
└──────────────┘             (Expiry = 15m)             └─────────────────┘

This model provides major security improvements:

By centralizing access control at the metadata catalog layer, Polaris eliminates IAM policy sprawl and provides a secure, audited boundary for cloud data lakes.

Polaris in the Agentic Lakehouse

As organizations adopt artificial intelligence and automated decision-making workflows, query patterns are shifting. In addition to human analysts running dashboards, platforms are increasingly queried by autonomous AI agents.

An agentic lakehouse is an architecture where AI agents, powered by Large Language Models (LLMs) and advanced query planners, automatically explore metadata, generate SQL queries, execute analysis, and write results back to the lakehouse storage layer.

While agentic workflows promise major productivity gains, they introduce unique security and operational risks:

  1. Query Hallucinations: An AI agent might generate a malformed or destructive SQL query, such as attempting to write garbage data to a production table or executing drop commands.
  2. Access Exfiltration: If an AI agent has broad storage access, it can query sensitive namespaces, potentially exposing private customer information or proprietary financials.
  3. Lack of Auditing: Standard data lake setups struggle to track whether a query was executed by a human analyst or an automated AI agent, complicating compliance audits.

Dremio Semantic Integration and Polaris Routing

To secure agentic workflows, organizations integrate Polaris with Dremio’s semantic layer. Dremio acts as the intelligent gateway and query execution planner, while Polaris enforces governance and vends storage tokens.

The architecture operates as follows:

┌────────────────┐
│   AI Agent /   │
│   LLM Planner  │
└───────┬────────┘
        │ 1. Natural Language Query
        ▼
┌────────────────┐
│ Dremio Semantic│
│     Layer      │
└───────┬────────┘
        │ 2. Authenticate & Resolve Paths
        ▼
┌────────────────┐
│ Apache Polaris │
│ (REST Catalog) │
└───────┬────────┘
        │ 3. Validate RBAC & Vend Temporary Token
        ▼
┌────────────────┐
│ Cloud Storage  │
│  (S3 / ADLS)   │
└────────────────┘

When an AI agent needs to answer a business question, it sends a natural language query to Dremio. Natural language interface interactions are inherently unpredictable, making security verification crucial. Dremio’s semantic layer maps the request to logical business tables, resolving field-level aliases and translating the request into optimized, standard SQL queries.

Before executing the query, Dremio contacts Polaris to resolve the base table metadata and verify access permissions. This verification step is completed prior to initiating any compute or storage operations, preventing wasteful resource consumption on unauthorized queries. Polaris evaluates the RBAC policy mapped to the AI agent’s service principal. If the AI agent is not authorized to access the specific namespace or table, Polaris rejects the request at the metadata level, preventing the query from starting and avoiding data exposure.

If authorized, Polaris vends a temporary storage token scoped strictly to the Parquet files required for the query. Dremio’s distributed executors fetch the data files using the token, perform the query calculations, and return the aggregated results to the AI agent.

This integrated approach provides critical guardrails:

Architectural Comparison: Polaris vs. Nessie vs. Hive Metastore

When selecting a metadata registry for an Apache Iceberg lakehouse, organizations typically evaluate three primary open-source options: Apache Polaris, Project Nessie, and the legacy Apache Hive Metastore (HMS). Understanding the architectural design trade-offs of each system is critical for choosing the right catalog for your enterprise.

1. Apache Hive Metastore (HMS)

The Hive Metastore was designed in the early days of Apache Hadoop to map relational table schemas to directories of files in a distributed file system. HMS uses a Thrift-based RPC protocol and persists its catalog state in a relational database (such as PostgreSQL or MySQL).

2. Project Nessie

Project Nessie is a transaction catalog for Iceberg that brings Git-like version control to data lakes. Nessie tracks catalog state as a commit graph, allowing developers to create branches, merge changes, and roll back table updates.

3. Apache Polaris

Apache Polaris is built from the ground up as a stateless, highly scalable metadata catalog implementing the standard Iceberg REST Catalog specification.

Deep Dive: The OAuth2 Client Credentials Handshake

To understand how query engines secure their sessions when interacting with Apache Polaris, we can walk through the OAuth2 token exchange handshake. This protocol ensures that access keys are short-lived and tied to specific principal roles.

1. The Token Request

When a query engine starts up, it initiates the connection by executing an HTTP POST request to the token endpoint. The engine transmits its client ID and client secret, requesting a bearer token.

curl -X POST https://polaris.example.com/api/catalog/v1/oauth/tokens \
  -H "Content-Type: application/x-www-form-urlencoded" \
  -d "grant_type=client_credentials" \
  -d "client_id=principal_client_id_123" \
  -d "client_secret=principal_client_secret_abc" \
  -d "scope=PRINCIPAL_ROLE:data_engineer_role"

2. The Token Validation and Response

The Polaris catalog server intercepts this request, validates the client credentials against its database, and verifies that the requested principal role matches the configurations. If valid, Polaris generates a cryptographically signed JSON Web Token (JWT) representing the session.

{
  "access_token": "eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJwb2xhcmlzIiwic3ViIjoicHJpbmNpcGFsXzEyMyIsImV4cCI6MTcxNjM4OTkwMCwicm9sZXMiOlsiZGF0YV9lbmdpbmVlcl9yb2xlIl19.signature",
  "token_type": "bearer",
  "expires_in": 3600
}

3. Subsequent API Requests

The query engine extracts the returned access_token and caches it locally. For all subsequent metadata requests (such as listing tables or loading schema snapshots), the engine includes this token in the Authorization header of the HTTP requests:

curl -X GET https://polaris.example.com/api/catalog/v1/catalogs/sales_warehouse/namespaces/analytics/tables \
  -H "Authorization: Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9..."

If the token expires or is rejected by the server, the client engine automatically restarts the OAuth2 handshake, ensuring continuous catalog connectivity without requiring human administrative intervention.

Ecosystem Best Practices

To successfully deploy and manage Apache Polaris at scale, data engineering teams should adhere to the following architectural best practices:

1. Catalog Segmentation

Avoid registering all company tables in a single catalog instance. Instead, segment catalogs based on organizational boundaries (e.g., finance_catalog, marketing_catalog, sales_catalog) or environments (dev_catalog, staging_catalog, prod_catalog). This separation isolates metadata boundaries and reduces the blast radius of administrative errors.

2. Multi-Level Namespace Hierarchies

Structure your namespaces logically to take advantage of Polaris RBAC inheritance. A recommended hierarchy is:

catalog.environment.department.dataset_name

For example:

prod_catalog.analytics.finance.quarterly_invoices

By organizing namespaces this way, you can grant read access to the entire finance namespace to finance analysts, and they will automatically inherit read rights for new tables added to that namespace without manual intervention.

3. Tuning Token Time-To-Live (TTL)

Tuning token lifetimes is a balance between security and performance. Shorter TTLs (e.g., 5 to 15 minutes) minimize the window of exposure for vended storage credentials, but they force compute engines to contact Polaris more frequently to refresh tokens, increasing API load. For high-volume streaming ingest workloads, set token TTLs between 30 and 60 minutes to minimize handshake overhead. For ad-hoc analytics and developer notebooks, keep token TTLs short (15 minutes or less) to maximize security.

4. Back-End Database Replication and Backup

In production deployments, Polaris containers are stateless. The state of your catalogs, namespaces, roles, and table pointers is stored in the backing relational database (configured via EclipseLink JDBC). Treat this database as a tier-one production system. Implement regular backups, enable multi-region read replicas, and configure automated failover to prevent catalog outages from disabling your entire query engine infrastructure.

5. Client-Side Caching Configuration

Configure query engines (Spark, Trino, Dremio) to cache metadata locally during query planning phases. While the REST API is fast, making repeated HTTP calls to Polaris for every sub-task in a distributed query plan can saturate network interfaces. Client-side metadata caching reduces catalog latency and improves overall query compilation times.

Conclusion

Apache Polaris represents a major advancement in the maturity of the modern data lakehouse. By providing a production-grade, vendor-neutral implementation of the Iceberg REST Catalog specification, Polaris prevents catalog lock-in and guarantees true compute-storage independence.

Its robust role-based access control, secure credential vending mechanism, and seamless integration with high-performance query engines like Dremio ensure that organizations can govern their datasets without compromising query speeds. As data platforms transition to automated, agent-driven architectures, a centralized, open metadata brain like Polaris becomes an essential pillar for secure, scalable analytics.

Dremio’s built-in Open Catalog is built natively on Apache Polaris. When you sign up, you get a production-ready, vendor-neutral Polaris catalog deployed instantly. Try Dremio Cloud free for 30 days to query your data without creating proprietary metadata silos.