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.
News and Information
- 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.
- 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.
- 4.3 Pre-Release of Kettle with the new Big Data components will be available for download on Jan 30, 2012 download:
- First set of Big Data How-To's Published - Check out the How-To's for MapR Hadoop and Cassandra NoSQL Database here.
It's easy to get started with Pentaho for Big Data.
- Watch the intro videos below.
- Read about Kettle and Big Data.
- Download and configure the software here.
- Try the How To's for yourself.
- Join the Pentaho Big Data forum and let us know how you are using Big Data, ask questions and give feedback.
A quick introduction to executing Kettle transforms as a Mapper and Reducer within the cluster.
What would the same task as "1) Pentaho MapReduce" look like if you coded it in Java? At a half hour long, you may not want to watch the entire video...
This is a quick summary of the previous two videos, "1) Pentaho MapReduce" and "2) Straight Java", and why Pentaho Kettle boosts productivity and maintainability.
A quick example of loading into the Hadoop Distributed File System (HDFS) using Pentaho Kettle.
A quick example of extracting data from the Hadoop Distributed File System (HDFS) using Pentaho Kettle.