In this article, author Srini Penchikala discusses Apache Spark GraphX library used for graph data processing and analytics. The article includes sample code for graph algorithms like PageRank ...

Apache Spark speeds up big data processing by a factor of 10 to 100 and simplifies app development to such a degree that ... “Like if you wanted to do graph processing there’s one ...

Apache Spark turns the user’s data processing commands into a Directed Acyclic Graph, or DAG. The DAG is Apache Spark’s scheduling layer; it determines what tasks are executed on what nodes ...

Apache Spark is used by a large number of companies for big data processing. As an open source platform, Apache Spark is developed by a large number of developers from more than 200 companies.

Apache Spark got its start in 2009 at UC Berkeley’s AMPLab as a way to perform in-memory analytics on large data sets. ... stream processing, and graph processing in the same program.

The Hadoop processing engine Spark has risen to become one of the hottest big data technologies in a short amount of time. And while Spark has been a Top-Level Project at the Apache Software ...

Apache Spark's powerful open-source platform enables high-speed data processing for large and complex datasets. The joint benchmarking used the k-core decomposition algorithm of Spark's GraphX ...

The software received a major update with last year’s release of Spark 2.3, which brought support for Kubernetes and true real-time processing in Spark Streaming. And soon – perhaps even later this ...

Spark has moved beyond pure experimentation—with imminent availability of a stable 1.0 release and inclusion in all major Hadoop distributions.