The following guest post is provided by Aaron Kimball, CTO of WibiData.
The Kiji ecosystem has grown with the addition of a new module, KijiMR. The Kiji framework is a collection of components that offer developers a handle on building Big Data Applications. In addition to the first release,KijiSchema, we are now proud to announce the availability of a second component: KijiMR. KijiMR allows KijiSchema users to use MapReduce techniques including machine-learning algorithms and complex analytics to develop many kinds of applications using data in KijiSchema. Read on to learn more about the major features included in KijiMR and how you can use them.
KijiMR offers developers a set of new processing primitives explicitly designed for interacting with complex table-oriented data. The low-level batch interfaces available in MapReduce include basic InputFormat and OutputFormat implementations. The raw APIs are designed for processing key-value pairs stored in flat files in HDFS. Integrating MapReduce with HBase via InputFormat and OutputFormat APIs is hard to do from scratch in every algorithm. In KijiMR, we have extended the available MapReduce APIs to include