February 2018
Intermediate to advanced
606 pages
14h 54m
English
Content preview from Spark: The Definitive Guide
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Chapter 33. Ecosystem and Community
One of Spark’s biggest selling points is the sheer volume of resources, tools, and contributors. At the time of this writing, there are over 1,000 contributors to the Spark codebase. This is orders of magnitude more than most other projects dream of achieving and a testament to Spark’s amazing community—both in terms of contributors and stewards. The Spark project shows no sign of slowing down, as companies large and small seek to join the community. This environment has stimulated a large number of projects that complement and extend Spark’s features, including formal Spark packages and informal extensions that users can use in Spark.
Spark Packages
Spark has a package repository for packages specific to Spark: Spark Packages. These packages were discussed in Chapters 9 and 24. Spark packages are libraries for Spark applications that can easily be shared with the community. GraphFrames is a perfect example; it makes graph analysis available on Spark’s structured APIs in ways much easier to use than the lower-level (GraphX) API built into Spark. There are numerous other packages, including many machine learning and deep learning ones, that leverage Spark as the core and extend its functionality.
Beyond these advanced analytics packages, others exist to solve problems in particular verticals. Healthcare and genomics have seen a surge in opportunity for big data applications. For example, the ADAM Project leverages unique, internal optimizations ...
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