What’s Next for HBase? Big Data Applications Using Frameworks Like Kiji by Michael Stack.
From the post:
Apache Hadoop and HBase have quickly become industry standards for storage and analysis of Big Data in the enterprise, yet as adoption spreads, new challenges and opportunities have emerged. Today, there is a large gap — a chasm, a gorge — between the nice application model your Big Data Application builder designed and the raw, byte-based APIs provided by HBase and Hadoop. Many Big Data players have invested a lot of time and energy in bridging this gap. Cloudera, where I work, is developing the Cloudera Development Kit (CDK). Kiji, an open source framework for building Big Data Applications, is another such thriving option. A lot of thought has gone into its design. More importantly, long experience building Big Data Applications on top of Hadoop and HBase has been baked into how it all works.
Kiji provides a model and set of libraries that you to get up and running quickly.
Kiji provides a model and a set of libraries that allow developers to get up and running quickly. Intuitive Java APIs and Kiji’s rich data model allow developers to build business logic and machine learning algorithms without having to worry about bytes, serialization, schema evolution, and lower-level aspects of the system. The Kiji framework is modularized into separate components to support a wide range of usage and encourage clean separation of functionality. Kiji’s main components include KijiSchema, KijiMR, KijiHive, KijiExpress, KijiREST, and KijiScoring. KijiSchema, for example, helps team members collaborate on long-lived Big Data management projects, and does away with common incompatibility issues, and helps developers build more integrated systems across the board. All of these components are available in a single download called a BentoBox.
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Be sure to visit the Kiji Project website.
Turn-key tutorials to get you started.