All the Greenplum utilities you mentioned here are also open-sourced and available for Cloudberry, but some of them are not in the main repo of Apache Cloudberry (This is more a matter of adhering to the Apache Software Foundation's regulations than a technical limitation).
Here is the unofficial roadmap of Cloudberry:
1. Continuously upgrading the PostgreSQL core version, maintaining compatibility with Greenplum Database, and strengthening the product's stability.
2. End-to-end performance optimization to support near real-time analytics, including streaming ingestion, vectorized batch processing, JIT compilation, incremental materialized views, PAX storage format, etc.
3. Supporting lakehouse applications by fully integrating open data lake table formats represented by Apache Iceberg, Hudi, and Delta Lake.
4. Gradually transforming Cloudberry Database into a data foundation supporting AI/ML applications, based on Directory Table, pgvector, and PostgresML.
Delighted to see Greenplum mentioned in this article, also equally pleased to see Apache Cloudberry mentioned in the comments. Greenplum has been open-source for nearly a decade, forming a fairly mature global open-source ecosystem, with many core developers distributed around the world ( they were not necessarily hired by Pivotal/VMware/Broadcom). Greenplum forked as Cloudberry wasn't to outdo Greenplum Database, but to foster a more neutral and open community around an MPP database with a substantial global following. To that end, the project was donated to the Apache Software Foundation following Greenplum's decision to close source. Since the project is in its early stages within the Apache incubator, our immediate goal is to build a solid foundation that adheres to Apache standards. Instead of introducing extensive new features, we are concentrating on developing a stable and compatible open-source alternative to Greenplum.
Here is the unofficial roadmap of Cloudberry:
1. Continuously upgrading the PostgreSQL core version, maintaining compatibility with Greenplum Database, and strengthening the product's stability. 2. End-to-end performance optimization to support near real-time analytics, including streaming ingestion, vectorized batch processing, JIT compilation, incremental materialized views, PAX storage format, etc. 3. Supporting lakehouse applications by fully integrating open data lake table formats represented by Apache Iceberg, Hudi, and Delta Lake. 4. Gradually transforming Cloudberry Database into a data foundation supporting AI/ML applications, based on Directory Table, pgvector, and PostgresML.