# 23rd July 2025

**Enhancements and Improvements**

#### **Improved Integrations with Polaris, Iceberg, Hudi, and Delta**

We’ve significantly enhanced our support for modern technologies, including:

* [**Table Formats**](https://docs.e6data.com/product-documentation/table-formats)**:** Apache Iceberg, Apache Hudi, and Delta Lake
* [**Catalog Systems**](https://docs.e6data.com/product-documentation/catalogs)**:** Apache Polaris

These updates improve both **connectivity** and the **querying experience** for users working with external catalogs and lakehouse storage systems.

[**Apache Polaris**](https://docs.e6data.com/product-documentation/catalogs/create-catalogs/apache-polaris)

* You can now create, edit, and delete Polaris catalog connections directly in the UI.
* Full support for exploring Iceberg tables registered under Polaris, including schema discovery and partitioned queries.
* Improved metadata synchronization and catalog validation during connection setup.

[**Apache Iceberg**](https://docs.e6data.com/product-documentation/table-formats/apache-iceberg)

* Iceberg tables can now be accessed seamlessly through supported external catalogs such as AWS Glue, Polaris, and Snowflake Open Catalog.
* Support for both partitioned and non-partitioned Iceberg tables.
* Enhanced handling of schema evolution and metadata-aware querying.
* Improved catalog management UI for editing and managing Iceberg integrations.

[**Delta Lake**](https://docs.e6data.com/product-documentation/table-formats/delta-lake)

* Added support for reading Delta Lake tables through catalogs like AWS Glue and Unity Catalog.
* Metadata visibility improvements for schema and file stats.
* Cleaner user interface for catalog selection and validation.
* Table browsing and querying experience aligned with other table formats.

[**Apache Hudi**](https://docs.e6data.com/product-documentation/table-formats/apache-hudi)

* Access Hudi tables via supported external catalogs: AWS Glue and Apache Hive
* Support for  Copy-on-Write (CoW)  table types
* Query modes: Read-Optimized, Incremental, and Real-Time
* Supports time travel, schema evolution, and upserts/inserts/deletes
* Access to commit metadata for incremental processing
* Improved integration with external metastores for smoother table discovery and querying.

With these improvements, users can work with modern data lakehouse architectures more efficiently, without friction in connecting or querying across different engines.


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