Kiji Project: An Open Source Framework for Building Big Data Applications with Apache HBase by Aaron Kimball.
From the post:
Our team at WibiData has been developing applications on Hadoop since 2010 and we’ve helped many organizations transform how they use data by deploying Hadoop. HBase in particular has allowed companies of all types to drive their business using scalable, high performance storage. Organizations have started to leverage these capabilities for various big data applications, including targeted content, personalized recommendations, enhanced customer experience and social network analysis.
While building many of these applications, we have seen emerging tools, design patterns and best practices repeated across projects. One of the clear lessons learned is that Hadoop and HBase provide very low-level interfaces. Each large-scale application we have built on top of Hadoop has required a great deal of scaffolding and data management code. This repetitive programming is tedious, error-prone, and makes application interoperability more challenging in the long run.
Today, we are proud to announce the launch of the Kiji project (www.kiji.org), as well as the first Kiji component: KijiSchema. The Kiji project was developed to host a suite of open source components built on top of Apache HBase and Apache Hadoop, that makes it easier for developers to:
- Use HBase as a real-time data storage and serving layer for applications
- Maximize HBase performance using data management best practices
- Get started building data applications quickly with easy startup and configuration
Kiji is open source and licensed under the Apache 2.0 license. The Kiji project is modularized into separate components to simplify adoption and encourage clean separation of functionality. Our approach emphasizes interoperability with other systems, leveraging the open source HBase, Avro and MapReduce projects, enabling you to easily fit Kiji into your development process and applications.
KijiSchema: Schema Management for HBase
The first component within the Kiji project is KijiSchema, which provides layout and schema management on top of HBase. KijiSchema gives developers the ability to easily store both structured and unstructured data within HBase using Avro serialization. It supports a variety of rich schema features, including complex, compound data types, HBase column key and time-series indexing, as well cell-level evolving schemas that dynamically encode version information.
KijiSchema promotes the use of entity-centric data modeling, where all information about a given entity (user, mobile device, ad, product, etc.), including dimensional and transaction data, is encoded within the same row. This approach is particularly valuable for user-based analytics such as targeting, recommendations, and personalization.
This looks important!
Reading further about their “entiity-centric” approach:
Entity-Centric Data Model
KijiSchema’s data model is entity-centric. Each row typically holds information about a single entity in your information scheme. As an example, a consumer e-commerce web site may have a row representing each user of their site. The entity-centric data model enables easier analysis of individual entities. For example, to recommend products to a user, information such as the user’s past purchases, previously viewed items, search queries, etc. all need to be brought together. The entity-centric model stores all of these attributes of the user in the same row, allowing for efficient access to relevant information.
The entity-centric data model stands in comparison to a more typical log-based approach to data collection. Many MapReduce systems import log files for analysis. Logs are action-centric; each action performed by a user (adding an item to a shopping cart, checking out, performing a search, viewing a product) generates a new log entry. Collecting all the data required for a per-user analysis thus requires a scan of many logs. The entity-centric model is a “pivoted” form of this same information. By pivoting the information as the data is loaded into KijiSchema, later analysis can be run more efficiently, either in a MapReduce job operating over all users, or in a more narrowly-targeted fashion if individual rows require further computation.
I’m already convinced about a single representative for an entity. 
Need to work through the documentation on capturing diverse information about a single entity in one row.
I suspect that the structures that capture data aren’t entities for purposes of this model.
Still, will be an interesting exploration.