Concurrent Inc. has recently announced that enterprise customers such as Airbnb, Etsy and The Climate Corporation are using Concurrent’s Big Data management application Cascading in combination with Amazon Elastic MapReduce to manage Big Data processing in Hadoop. Cascading is a Big Data processing application that allows developers to use an API to construct data processing and analytic operations on Apache Hadoop clusters without leveraging advanced programming languages such as Pig and Hive. In comparison to Pig and Hive, Cascading enables programmers to write Hadoop-related code with comparable granularity and superior job orchestration and management capabilities. A Java application, Cascading can be used within both a private data center environment as well as a cloud based development ecosystem. Airbnb uses Cascading to “determine factors driving room bookings as well as user drop-off” whereas Etsy’s Cascading deployment “powers all A/B analysis, a variety of analytics and dashboards, behavioral inputs to our search index.”
Cascading’s use across of a number of industry verticals for Apache Hadoop programming and analytics points to a quiet revolution in the Big Data world marked by the increasing currency of programming frameworks that simplify and streamline the construction of data processing tasks within a Hadoop cluster. Speaking of the milestone constituted by Cascading’s usage by customers such as Etsy and Airbnb, Concurrent CEO Chris Wensel noted that Cascading “has been battle tested in rigorous production environments for many years. Developers rely on Cascading and the growing ecosystem of community sponsored projects to build complex data intensive applications that drive their business.” Expect more and more enterprises to leverage Cascading to simplify Hadoop-programming both within cloud environments and traditional data center infrastructures as the demand for big data analytics intensifies both in scope and business urgency.