Cascading 3.0 Adds Support For Wide Range Of Computational Frameworks And Data Fabrics

Today, Concurrent, Inc. announces the release of Cascading 3.0, the latest version of the popular open source framework for developing and managing Big Data applications. Widely recognized as the de facto framework for the development of Big Data applications on platforms such as Apache Hadoop, Cascading simplifies application development by means of an abstraction framework that facilitates the execution and orchestration of jobs and processes. Compatible with all major Hadoop distributions, Cascading sits squarely at the heart of the Big Data revolution by streamlining the operationalization of Big Data applications in conjunction with Driven, a commercial product from Concurrent that provides visibility regarding application performance within a Hadoop cluster.

Today’s announcement extends Cascading to platforms and computational frameworks such as local in-memory, Apache MapReduce and Apache Tez. Going forward, Concurrent plans for Cascading 3.0 to ship with support for Apache Spark, Apache Storm and other computational frameworks by means of its customizable query planner, which allows customers to extend the operation of Cascading to compatible computational fabrics as illustrated below:

The breakthrough represented by today’s announcement is that it renders Cascading extensible to a variety of computational frameworks and data fabrics and thereby expands the range of use cases and environments in which Cascading can be optimally used. Moreover, the customizable query planner featured in today’s release allows customers to configure their Cascading deployment to operate in conjunction with emerging technologies and data fabrics that can now be integrated into a Cascading deployment by means of the functionality represented in Cascading 3.0.

Used by companies such as Twitter, eBay, FourSquare, Etsy and The Climate Corporation, Cascading boasts over 150,000 applications a month, more than 7,000 deployments and 10% month over month growth in downloads. The release of Cascading 3.0 builds on Concurrent’s recent partnership with Hortonworks whereby Cascading will be integrated into the Hortonworks Data Platform and Hortonworks will certify and support the delivery of Cascading in conjunction with its Hadoop distribution. Concurrent, Inc. also recently revealed details of a strategic partnership with Databricks, the principal steward behind the Apache Spark project, that allows it to “operate over Spark…[the] next generation Big Data processing engine that supports batch, interactive and streaming workloads at scale.” In an interview with Cloud Computing Today, Concurrent CEO Gary Nakamura confirmed that Concurrent plans to negotiate partnerships analogous to the agreement with Hortonworks with other Hadoop distribution vendors in order to ensure that Cascading consolidates its positioning as the framework of choice for the development of Big Data applications. Overall, the release of Cascading 3.0 represents a critical product enhancement that positions Cascading to operate over a broader pasture of computational frameworks and consequently assert its relevance for Big Data application development in a variety of data and computational frameworks. More importantly, however, the product enhancement in Cascading 3.0, in conjunction with the partnership with Databricks regarding Apache Spark, suggests that Cascading is well on its way to becoming the universal framework of choice for developing and managing applications in a Big Data environment, particularly given its compatibility with a wide range of Hadoop distributions and data and computational frameworks.

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