Built on Apache HBase and Apache Hadoop, the Kiji Project provides a framework to build scalable big data applications. Architecture

The Kiji Project is modularized into separate components to support a wide range of usage and encourage clean separation of functionality.

KijiSchema simplifies real-time storage and retrieval of diverse data from primitive types to objects, time-series and event streams. KijiSchema handles challenges with serialization, schema design & evolution, and meta data management common in NoSQL storage solutions.

KijiMR provides a powerful paradigm to apply MapReduce in both batch and real-time workloads. KijiMR introduces producers to perform record-wise analytics and gatherers, which build predictive models by analyzing aggregate behaviors.

Kiji BentoBox easily installs and configures a local development environment with Hadoop, HBase and all Kiji components in under the time it takes for a coffee break.

KijiHive provides HiveQL access to Kiji datasets.

KijiExpress is Kiji’s Scala-based scripting language for analyzing Kiji data using Scalding and for building machine learning models.

KijiREST will provide an HTTP REST API for front-end developers to access data and trigger model scoring.

KijiScoring will provide the real-time scoring of predictive models within your application.

Ready to try Kiji? Head over to our Downloads page to grab a BentoBox or download the components independently.