From time to time the folks at Concurrent, Inc. let me know about improvements to its technology or about new products. This time, Cascading has been enhanced to support Mapreduce and Tez.
Cascading 3.0 Sets the Standard for Enterprise Application Development
With more than 150,000 user downloads a month, Cascading is the de facto standard in open source application infrastructure technology. Supported by key strategic partnerships with Hortonworks and Databricks, and broad support with all major Hadoop distributions, Cascading is the enterprise development framework of choice for data-centric applications. Cascading accelerates and simplifies enterprise application development, and meets a variety of enterprise use cases from simple to complex.
Cascading 3.0 is a major leap forward in enterprise data-centric application development. Features and benefits include:
- Cascading 3.0 provides the most comprehensive data application framework to meet business challenges and solve a variety of business problems ranging from simple to complex, regardless of latency or scale.
- Cascading 3.0 allows enterprises to build their data applications once, while providing the flexibility to run applications on the fabric that best meets their business needs.
- Cascading 3.0 will ship with support for: local in-memory, Apache MapReduce, and Apache Tez.
- Soon thereafter, with community support, Apache Spark, Apache Storm and others will be supported through its new pluggable and customizable query planner.
- Third-party products, data applications, frameworks and dynamic programming languages built on Cascading will immediately benefit from this portability.
- Cascading offers compatibility with all major Hadoop vendors and service providers: Altiscale, Amazon EMR, Cloudera, Hortonworks, Intel, MapR and Qubole, among others.
I've written about Concurrent before (see Concurrent Driven: Big data application performance management for more information). This announcement focuses on allowing Cascading users to develop data-focused applications for both the Apache Mapreduce and Apache Tez environments as well as other Big Data platforms the company currently supports (all major Hadoop vendors and service providers: Altiscale, Amazon EMR, Cloudera, Hortonworks, Intel, MapR and Qubole, among others).
The company's goal clearly is to make its application framework a necessary part of big data application development. Concurrent has promised to integrate its application framework with Apache Spark and Storm in the near future.
If your company is developing big data applications, Concurrent should be on your watch list.