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’s worth of massively parallel processing (MPP) expertise developed through the creation of the Pivotal Greenplum® 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.
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Exceptional HAWQ®’s 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.
HAWQ® is breaking new ground for advanced analytics and machine learning in Apache Hadoop. All contributors welcome! Get involved with the next wave in Hadoop analytic database technology. HAWQ® is fully open source with Apache. Everything from this community, website, and the code itself has been developed by a community of people who want to support and propel HAWQ® technology.
We especially welcome additions and corrections to the documentation, wiki, and website to improve user experiences. Bug reports, and fixes and additions to the HAWQ® code are welcome. Helping users learn best practices also earns good karma in our community.
Got an idea for a feature or fix for HAWQ? We welcome contributors. Please discuss in the dev mail list, post an issue on Jira, or make pull requests on Github.
Apache MADlib is a SQL-based advanced analytics and machine learning library that works with Apache HAWQ®.
Are you a HAWQ expert? Want to share your knowledge with others? We are collaborative community that shares best practices.
Even this website is a work of our community. Got a suggestion, fix, or even a redesign idea? Please contribute.
Documentation and how-to tips can always be made clearer, better organized, more complete, and translated to more languages.
Looking for an answer to a specific question? Are you an expert who likes to answer questions? Engage here:
Note: When downloading from a mirror, please be sure to verify that checksums and signatures are correct. To do so, use the checksum and signature files from the main Apache site at https://www.apache.org/dist/hawq/. Check here to get all the verification required files including OpenPGP keys that we used to sign releases: KEYS
The PGP signatures can be verified using GPG. First download the KEYS as well as the asc signature file for the particular distribution. Then verify the signatures using:
% gpg --import KEYS
% gpg --verify ${filename}.tar.gz.asc ${filename}.tar.gz
Alternatively, you can verify the SHA signature on the files.
For example, to check a SHA256 sum of a file:
On Linux:
% sha256sum filename.sha
On Mac:
% shasum -a 256 filename.sha
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