WibiData releases Kiji Chirashi framework for Big Data Applications

Kiji is an open source framework for building big data apps with Apache HBase, launched by WibiData to fill the gap between a key-value store functionality and the needs of a predictive modeling application.

SAN FRANCISCO, October 3, 2013 - WibiData announces the latest release of the Kiji ProjectKiji Project, an open source framework for building Big Data Applications. The latest Kiji BentoBox brings powerful languages and libraries to the Hadoop and HBase stack. This marks the first release of BentoBox 1.2, called "Chirashi."

"The latest release of Kiji brings new features to our SDK for building Big Data Applications. With "Chirashi," developers, data scientists and analysts can build more powerful applications using machine learning models and integrate with business intelligence tools," said Aaron Kimball, WibData's Co-founder and Chief Architect.

Kiji was created to fill the gap between what a key-value store provides and what an application needs to run predictive models and read and write complex data. WibiData open sourced Kiji to help developers fill the gap between application development and data science.

Hive users gain support for all primitive and complex types in Apache Hive DDL generation as well as the ability to write back to Kiji tables. With greater Hive support, analysts can access all the data in Kiji using HiveQL, JDBC/ODBC, or other Hive compatible business intelligence tools. For example, ad hoc queries for aggregating statistics of users are useful for exploratory analysis of a data set. Combined with Hive's existing data warehousing functionality, Kiji integrates as a significant component in a big data ecosystem.

KijiScoring lets developers create real-time predictive models and scoring functions. Developers can now pass per-request settings to producer functions, greatly expanding the flexibility of real-time predictive model scoring. For example, a user's current geolocation from mobile application can be factored in when re-computing which offers or recommendations to serve a user.

KijiExpress, a Scala based analytic language, provides a library for analyzing and modeling data stored in Kiji tables. With this latest release, users gain an improved interface for interacting with Avro data stored in Kiji. Now users can use a paging facility to read large series of values from columns in Kiji. This release also includes WibiData's latest developments on the KijiExpress Model Lifecycle, allowing users to define how models are trained on and applied to users.

The Kiji BentoBox "Chirashi" can be downloaded here.

Learn more at www.Kiji.org .