Apache HAWQ has moved to be a top-level project. HAWQ is described as an advanced enterprise SQL-on-Hadoop query engine and analytic database that  combines the key technological advantages of MPP database with the scalability and convenience of Apache Hadoop.

HAWQ reads data from and writes data to HDFS natively, is fast and scalable, and provides users with a complete, standards compliant SQL interface. HAWQ operates natively in Apache Hadoop.

The developers say that HAWQ's parallel processing means it combines high performance throughput and low latency (potentially near real time) query responses that can scale to petabyte-sized datasets. The developers used the expertise in massively parallel processing (MPP) gained through the creation of the Pivotal Greenplum enterprise database and open source PostgreSQL.

HAWQ started life as Pivotal HAWQ before being transferred to an Apache project. Pivotal HAWQ itself owed much to technology that Pivotal acquired when it bought Greenplum and its MPP Shared-Nothing data warehouse product.  Pivotal developed HAWQ as a SQL query database that combined the merits of Pivotal Greenplum with Hadoop distributed storage. Pivotal contributed the Pivotal HAWQ core to the Apache in September 2015. The original team of developers continued work on the Apache HAWQ product to make the product better at running on public cloud, private cloud, or shared physical cluster environments.

The SQL support means HAWQ works with SQL-based applications and BI/data visualization tools. You can use it to execute complex queries and joins, including roll-ups and nested queries. HAWQ now works with the rest of the Apache Hadoop ecosystem products, so you can integrate and manage with Apache YARN. provision with Apache Ambari. and interface with Apache HCatalog. It also supports Apache Parquet, Apache HBase, and works with the Apache MADlib machine learning libraries for AI-based analytics.

More Information

HAWQ website

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