In this article, author discusses Apache Spark GraphX used for graph data processing and analytics, with sample code for graph algorithms like PageRank, Connected Components and Triangle Counting.

Apache Spark speeds up big data processing by a factor of 10 to 100 and simplifies app development to such a degree that developers call it a "game changer." ...

Fast, flexible, and developer-friendly, Apache Spark is the leading platform for large-scale SQL, batch processing, stream processing, and machine learning.

The advantage of Spark over Hadoop for businesses is that it enables the development of complex multi-step data processing pipelines using acyclic oriented graphs ( AGDs ).

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 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 brings high-speed, in-memory analytics to Hadoop clusters, crunching large-scale data sets in minutes instead of hours ...

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

Google Cloud Compute (GCP) supports Spark too, and Spark is one of a handful of “runners” in Google’s high-level Apache Beam construct. In addition to running just about anywhere, Spark offers a ...