Getting Started with Spark and Redis


After Spark was introduced, it caught developer attention as a fast and general engine for large-scale data processing, easily surpassing alternate big data frameworks in the types of analytics that could be executed on a single platform.

Spark supports cyclic data flow and in-memory computing, allowing programs to run faster than Hadoop MapReduce. With its ease of use and support for SQL, streaming, and machine learning libraries, it has ignited early interest in a wide developer community. 

Redis Labs recently published a Redis connector for Apache Spark that provides read and write access to all of Redis’ core data structures as RDDs (Resilient Distributed Datasets, in Spark terminology).

The combination of Spark and Redis fast tracks your analytics, allowing unprecedented real-time processing of really large datasets. This paper outlines the initial steps needed to start using Apache Spark and Redis. 

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