Welcome to the Big Data space in the Pentaho Community wiki. This space is the community home and collection point for all things Big Data within the Pentaho ecosystem. It is the place to find documentation, how-to's, best practices, use-cases and other information about employing Pentaho technology as part of your overall Big Data Strategy. It is also where you can share your own information and experiences. We look forward to your participation and contribution!
Pentaho's Big Data story revolves around Pentaho Data Integration AKA Kettle. Kettle is a powerful Extraction, Transformation and Loading (ETL) engine that uses a metadata-driven approach. The kettle engine provides data services for, and is embedded in, most of the applications within the Pentaho BI suite from Spoon, the Kettle designer, to the Pentaho report Designer. Check out About Kettle and Big Data for more details of the Pentaho Big Data Story.
- Pentaho Big Data components are now open source - In order to play well within the Hadoop open source ecosystem and make Kettle be the best and most pervasive ETL engine in the Big Data space, Pentaho has put all of the Hadoop and NoSQL components into open source starting with the 4.3 release. [READ MORE HERE Press Release (TODO)]
- Kettle license moves to Apache - To further Kettle adoption within the Hadoop community, Pentaho had decided to move the Kettle open source license from LGPL to the more permissive Apache license. This will remove the issue of what restrictions are applied to a derivative work based on combining Kettle with Hadoop. [READ MORE HERE Press Release (TODO)]
- 4.3 Pre-Release of Kettle with the new Big Data components is now available for download:
- First set of Big Data How-To's Published - Check out the How-To's for MapR Hadoop and Cassandra NoSQL Database here.
This is a closed wiki space
The only people with access are Pentaho Employees and BAD team
This is a first shot at getting an open source collaboration space for Big Data. It will eventually be open but is currently a work in progress and a place to put the use cases, demo's etc. I completely pulled the structure and initial content from my arse and am not in love with any of it. It is a round lump of clay, waiting to be molded by the brilliant minds of the Big Ass Data Team.