In a class by itself, only Apache HAWQ combines exceptional MPP-based analytics performance, robust ANSI SQL compliance, Hadoop ecosystem integration and manageability, and flexible data-store format support. All natively in Apache Hadoop. No connectors required.

Built from a decade鈥檚 worth of massively parallel processing (MPP) expertise developed through the creation of the Pivotal Greenplum庐 enterprise database and open source PostgreSQL, HAWQ® enables to you to swiftly and interactively query Hadoop data, natively via HDFS.

On Aug 15, 2018, the ASF Board established Apache HAWQ as a Top Level Project, which was approved by unanimous vote of the directors present. Please see the associated press release from the ASF. HAWQ entered incubation in September of 2015 and made four releases as an incubating project. Along the way, the HAWQ community has worked hard to ensure that the project is being developed according to the principles of the The Apache Way. We will continue to do so in the future as a TLP, to the best of our ability.

Exceptional performance

HAWQ®鈥檚 parallel processing architecture delivers high performance throughput and low latency - potentially near real time - query responses that can scale to petabyte-sized datasets. Operate natively in Apache Hadoop.

Robust ANSI SQL compliance

Leverage familiar skills. Achieve higher levels of compatibility for SQL-based applications and BI/data visualization tools. Execute complex queries and joins, including roll-ups and nested queries.

Apache Hadoop ecosystem manageability and integration

Integrate and manage with Apache YARN. Provision with Apache Ambari. Interface with Apache HCatalog. HAWQ® supports Apache Parquet, Apache AVRO, Apache HBase, and others. Easily scale nodes up or down to meet performance or capacity requirements.

Plus, HAWQ® works with Apache MADlib machine learning libraries to execute advanced analytics for data-driven digital transformation, modern application development, data science purposes, and more.