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Data security in the cloud has always been a game of trade-offs. You can lock everything down and watch your data engineering team struggle with access requests, or you can hand out broad credentials and hope nobody misuses them. Apache Polaris, the open-source catalog for Apache Iceberg, takes a fundamentally different approach: every single data access request goes through a complete security pipeline that authenticates the caller, checks their permissions, and issues temporary, scoped credentials that expire automatically.
In this post, I will walk through the entire Polaris security stack from the moment a query engine sends a request to the moment it receives cloud storage credentials. No shortcuts, no hand-waving. Just the exact path every request takes and why each step matters.
Why Traditional Approaches Fall Short
Before diving into Polaris, let us look at how most data platforms handle security today. A typical setup involves creating a service account in AWS IAM or Google Cloud IAM, attaching broad permissions to it, generating access keys, and distributing those keys to every compute engine that needs data access. Spark clusters get a key. Flink jobs get a key. Trino workers get a key. Every tool has direct, persistent access to your storage.
This pattern creates several problems:
- Credential sprawl: Keys exist in multiple places, making rotation painful
- Over-permissioning: Service accounts often have broader access than any single job needs
- Slow revocation: Removing access requires rotating keys and updating every consumer
- Audit gaps: It is hard to trace which specific job accessed which specific file
Polaris eliminates these problems by design. Compute engines never touch long-lived credentials. Instead, they request temporary access through Polaris's REST API, and Polaris decides what they get based on who they are and what they are allowed to do.
The Security Pipeline: A Request's Journey
When Spark, Flink, or any Iceberg-compatible engine needs to read a table, it sends a request to Polaris. That request travels through four distinct security layers before returning credentials. Let us trace the complete path.
Layer 1: Authentication - Who Are You?
Every request to Polaris must identify itself. Polaris uses principals - entities that represent users, services, or applications. Each principal has credentials (typically tokens) that prove its identity.
The authentication step answers a simple question: is this request coming from a known, valid principal? If the token is invalid, expired, or missing, the request stops here. No further processing happens.
Principals are managed through Polaris's management API. An administrator creates them, assigns initial credentials, and can rotate or revoke those credentials at any time. Unlike cloud IAM service accounts, Polaris principals are catalog-specific. They exist only within the Polaris ecosystem and have no inherent access to anything until explicitly granted.
Layer 2: Principal Roles - What Is Your Identity?
Once authenticated, Polaris looks up the principal's assigned principal roles. This is the first tier of Polaris's two-tier RBAC system.
Principal roles answer the question: what is this principal's organizational identity? A principal might have roles like "data-scientist", "etl-service", or "analytics-reader". These roles are assigned directly to principals and represent who the principal is in the organization.
The key insight here is separation of concerns. Principal roles handle identity. They say "this is a data scientist" or "this is an ETL job". They do not say what that identity can access. That decision happens at the next layer.
Layer 3: Catalog Roles and Privileges - What Can You Do?
Catalog roles are the second tier of Polaris's RBAC system. They define what operations are permitted on which catalog resources. A catalog role might grant TABLE_READ_DATA on the "analytics" catalog, or CATALOG_MANAGE_ACCESS on the "production" catalog.
Here is where Polaris's design gets interesting: catalog roles are not assigned directly to principals. Instead, they are granted to principal roles. A principal role "data-scientist" might be granted a catalog role "analytics-reader", which in turn has TABLE_READ_DATA on specific tables.
This two-tier design provides flexibility. You can change what a "data-scientist" can access by modifying catalog role grants, without touching individual principal assignments. You can also audit access patterns by principal role, making it easier to answer questions like "what can all data scientists access?"
The available privileges are granular:
- TABLE_READ_DATA - Read table data and metadata
- TABLE_WRITE_DATA - Write table data
- CATALOG_MANAGE_ACCESS - Manage catalog access control
- CATALOG_MANAGE_CONTENT - Create and modify catalog objects
- NAMESPACE_CREATE - Create namespaces
- And more
When a request arrives, Polaris resolves the principal to their principal roles, then resolves those principal roles to catalog roles, then checks whether any of those catalog roles have the required privilege on the requested resource. If yes, authorization succeeds. If no, the request is denied.
Layer 4: Credential Vending - Scoped, Temporary Access
This is where Polaris fundamentally differs from traditional approaches. Instead of returning a success message and letting the engine use its own credentials, Polaris mints fresh, temporary credentials specifically for this request.
The process works as follows:
Storage configuration lookup: Polaris retrieves the catalog's storage configuration (S3 bucket, GCS path, Azure container)
-
Cloud provider API call: Polaris calls the appropriate cloud API:
- AWS: STS AssumeRole with external ID
- GCS: Generate service account token
- Azure: Request tenant token
Scope restriction: The credentials are scoped to the specific table path requested. A read request for table "analytics.events" gets credentials that can only access that table's files, not the entire bucket.
Time bounding: Credentials are valid for approximately 15 minutes (configurable). After that, they expire automatically.
Permission mapping: The cloud credentials reflect the Polaris privilege. TABLE_READ_DATA yields read-only credentials. TABLE_WRITE_DATA yields read-write credentials.
The engine receives these temporary credentials and uses them to access cloud storage directly. From Polaris's perspective, the security contract is complete: the engine got exactly the access it needed, for exactly the time it needed, scoped to exactly the resource it requested.
The Numbers Behind Credential Vending
Credential vending is not free. Each minting operation requires a cloud provider API call, which adds latency to the data access path. In practice, Polaris achieves 100-200ms per credential minting operation. For interactive queries, this is acceptable. For high-throughput batch jobs, Polaris implements caching to reduce repeated cloud API calls.
The trade-off is clear: slightly higher latency for dramatically better security. And since Polaris caches credentials for repeated access patterns, the amortized cost drops significantly for typical workloads.
Version 1.3.0, released in January 2026, added federated credential vending. This means Polaris can now mint credentials for external catalogs (like Snowflake or Glue), not just its own managed storage. This extends the same security model to federated data access, which is a significant advancement for organizations with hybrid catalog deployments.
OPA Integration: Externalizing Authorization
Starting with v1.3.0, Polaris supports Open Policy Agent (OPA) integration. This allows organizations to externalize authorization decisions to a dedicated policy engine.
Instead of Polaris evaluating RBAC rules internally, it can send authorization queries to OPA. OPA evaluates policies written in Rego (OPA's policy language) and returns allow or deny decisions. This enables:
- Complex policies that go beyond Polaris's built-in privilege model
- Centralized policy management across multiple systems
- Dynamic policies that can consider context like time of day, request origin, or data classification
For organizations with existing OPA deployments, this integration means Polaris fits naturally into their security infrastructure without requiring parallel policy management.
Photo by Stephen Phillips on Unsplash
Production Security Hardening
Running Polaris securely in production requires attention beyond the default configuration. Here are key considerations:
TLS Everywhere
Enable TLS for all communication paths:
- REST API endpoints (Quarkus server configuration)
- JDBC connections to the persistence backend
- Internal service communication if running distributed
Persistence Security
The persistence layer stores all catalog metadata, including storage configurations and RBAC grants. Secure it as you would any database:
- Use encrypted connections (JDBC with SSL)
- Restrict network access to Polaris servers only
- Enable audit logging for metadata changes
- Consider separate persistence instances for different environments
Storage IAM Configuration
When configuring cloud storage, follow least-privilege principles:
- Create dedicated IAM roles for Polaris
- Use external IDs for cross-account AWS access
- Restrict allowed storage locations per catalog
- Regularly audit storage role permissions
Credential Cache Tuning
Polaris caches minted credentials to reduce cloud API calls. Tune the cache TTL based on your security requirements:
- Shorter TTL = better security, more cloud API calls
- Longer TTL = better performance, longer credential lifetime
For highly sensitive data, err on the side of shorter TTLs. For batch workloads with predictable access patterns, longer TTLs may be acceptable.
Monitoring and Alerting
Set up monitoring for:
- Failed authentication attempts (possible credential compromise)
- Unusual privilege escalation patterns
- Credential vending latency spikes
- Storage access errors (possible IAM misconfiguration)
Why This Matters for Data Engineering Teams
The Polaris security model changes how data engineering teams operate:
No more key management: Engineers do not need to generate, distribute, or rotate cloud storage credentials. Polaris handles it automatically.
Consistent access control: Whether data is accessed through Spark, Flink, Trino, or any other Iceberg-compatible engine, the same RBAC policies apply. No engine-specific IAM configurations.
Audit by design: Every data access leaves a trail through Polaris. You know who accessed what, when, and with what permissions. Compliance teams love this.
Instant revocation: Remove a principal's catalog role grant, and their access stops immediately. No waiting for key rotation to propagate.
Multi-tenancy without complexity: Different teams can share a Polaris instance while maintaining complete access isolation through catalog and namespace-level RBAC.
The Bottom Line
Apache Polaris does not treat security as an afterthought or a configuration option. It is woven into every layer of the architecture. From the moment a request arrives, through authentication, RBAC evaluation, and credential vending, Polaris maintains strict security boundaries.
The result is a data catalog where:
- Compute engines never possess long-lived credentials
- Every access is scoped to exactly what is needed
- Permissions expire automatically
- Audit trails are comprehensive
- Multi-engine environments have consistent security policies
For organizations building modern data platforms on Apache Iceberg, Polaris offers a security model that matches the sophistication of the data architecture itself. It is not just a catalog - it is a security boundary for your data lake.
I'm Prithvi S, Staff Software Engineer at Cloudera and Opensource Enthusiast. Follow my work on GitHub: https://github.com/iprithv
Tags: polaris, security, api, cloud
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