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'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 ...

A Facebook team has recently published a comparison of the performance of their existing Giraph-based graph processing system with the newer GraphX which is part of the popular Spark framework ...

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." ...

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

Writing Spark applications Spark, written in Scala, provides a unified abstraction layer for data processing, making it a great environment for developing data applications.

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 ...

Impetus Technologies, has announced StreamAnalytix 3.0, which adds support for Apache Spark-based batch processing and enriched online and offline machine learning features. The new capabilities are ...

A little-known startup called Hazelcast Inc. is hoping to steal some of the limelight from popular open-source projects Apache Spark and Apache Flink, launching what it claims is a faster and ...

This is a comprehensive Apache Hadoop and Spark comparison, covering their differences, features, benefits, and use cases.