Pivotal ported their massively parallel processing (MPP) database Greenplum to Hadoop and made it open source as an incubating project at Apache, called Apache HAWQ. This bring together full ANSI SQL with MPP capabilities and Hadoop integration.
The integration in an existing Hadoop installation is easy, as you can integrate all existing data via external tables. This is done using the pxf API to query external data. This API is customizable, but already brings the most used formats ready made. These include:
To access and store small amounts of data Apache HAWQ has an interface called gpfdist. This enables you to store data outside of your HDFS and still access it within HAWQ to join with the data stored in HDFS. This is especially handy, when you need small tables for dimension or mapping data in Apache HAWQ. This data will then not use a whole block of your HDFS, that is mostly empty.
Apache HAWQ even come integrated with MADlib, also an Apache incubating product, developed by Pivotal. MADlib is a Machine Learning framework, based on SQL. So moving data between different tools for analysing it, is not need anymore. If you have stored your data in Apache HAWQ, you can mine it in the database directly and don’t have to export it, e.g. to a Spark client or tools like Knime or RapidMiner.
MADLib comes with algorithms in the following categories:
- Topic Modelling
- Assocition Rule Mining
- Descriptive Statistics
By using HAWQ you even can leverage tools like Tableau with real time database connections, which was not satisfactory so far when you used Hive.
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