# Apache Polaris

Polaris is an open-source, cloud-native catalog service designed to manage Apache Iceberg™ catalogs efficiently. Integrated with e6data, Polaris enables users to query structured and semi-structured data across cloud data lakes using a unified, secure interface. It supports schema evolution, nested namespaces, and metadata access through the Apache Iceberg REST protocol—making it a robust choice for large-scale, production-grade lakehouse environments.

* [Connect to Apache Polaris](https://docs.e6data.com/product-documentation/~/revisions/W5MExJCuvHiG1ioEcgOy/catalogs/create-catalogs/apache-polaris/connect-to-apache-polaris)
* [Edit a Polaris catalog](https://app.gitbook.com/o/CLn7sDaP4M1F3t7m8X0L/s/eVBYKZm1xFKFFVzS0lRJ/~/changes/1062/catalogs/create-catalogs/apache-polaris/edit-polaris-catalog)
* [Delete a Polaris Catalog](https://app.gitbook.com/o/CLn7sDaP4M1F3t7m8X0L/s/eVBYKZm1xFKFFVzS0lRJ/~/changes/1062/catalogs/create-catalogs/apache-polaris/delete-polaris-catalog)

#### **Key Benefits:**

* **Unified Metadata Access**: Centrally view and query all registered Iceberg datasets, regardless of where they are stored (e.g., S3, Azure, or GCS).
* **Enterprise-Grade Security**: Implements fine-grained role-based access control (RBAC) for secure and compliant data access.
* **Scalable Architecture**: Designed to handle large-scale data catalogs, partitions, and workloads across enterprise-grade deployments.
* **Multi-Cloud Compatibility**: Supports storage backends across AWS, Azure, and Google Cloud.
* **Interoperability with Compute Engines**: Seamlessly integrates with engines like Apache Spark, Flink, Dremio, and Snowflake for read operations.
* **Support for Views**: Along with tables, Polaris supports virtualized views to simplify querying and data abstraction.
* **Rich Namespace Support**: Allows nested namespaces up to 16 levels deep for granular organization of data assets.

#### **Use Cases:**

* Centralized cataloging of data across departments or business units
* Managing logical namespaces for better data organization
* Secure access to structured data across cloud platforms
* Seamless integration with data processing and analytics engines.

#### **What Is Supported:**

* Catalog and schema discovery
* Multi-level namespaces
* Role-based access mapping
* Cloud-native compatibility (AWS, Azure, GCP)
* Access to schema, table, column metadata, statistics, and partitions

#### **Future Improvements:**

* Currently supports **read-only operations**
* Only **catalog-level access control** is available
* Fine-grained access (e.g., per-table or per-column) is planned for future releases
* Validating catalogs with different principal roles 

#### **Sample Queries:**

```
-- List all tables in a Polaris namespace
SHOW TABLES FROM polaris_catalog.sales.q3;

-- Query a table through Polaris catalog
SELECT customer_id, total_amount
FROM polaris_catalog.sales.q3.orders
WHERE total_amount > 1000;
```

**Troubleshooting:**

| Issue                     | Resolution                                                  |
| ------------------------- | ----------------------------------------------------------- |
| Connection fails          | Ensure Polaris URL and Client ID are correct                |
| Tables or schemas missing | Verify role privileges and catalog configurations           |
| Access errors (401/403)   | Check with your Polaris admin to confirm access permissions |