This article was originally posted by Edd Dumbill of Silicon Valley Data Science and re-posted here with Edd’s permission. We thought it would be a great complement to these other StampedeCon presentations:
- Using Multiple Persistence Layers in Spark to Build a Scalable Prediction Engine by Richard Williamson (Silicon Valley Data Science)
- Lifting the hood on Spark Streaming by Andrew Psaltis (Shutterstock)
- How Cisco Migrated from MapReduce Jobs to Spark Jobs by Ken Owens (Cisco)
- Workshop: Deep Dive into Apache Cassandra & Apache Spark by Jon Haddad (Datastax)
The Apache Spark big data processing platform has been making waves in the data world, and for good reason. Building on the progress made by Hadoop, Spark brings interactive performance, streaming analytics, and machine learning capabilities to a wide audience. Spark also offers a more developer-friendly and integrated platform to a field in which self-assembly of components has been the norm.
As an upcoming contender for big data computation, Spark has many advantages, notably speed and developer convenience. But how do you know whether Spark can help you? Here are some common use cases that we’ve seen in the field at Silicon Valley Data Science, working hands-on with Spark. Continue reading “What is Apache Spark Used For?”