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." ...

Spark SQL, part of Apache Spark, is used for structured data processing by running SQL queries on Spark data. Srini Penchikala discusses Spark SQL module & how it simplifies data analytics using SQL.

Spark utilizes memory for its data processing, making it much faster (100x) than disk-based Hadoop. But Spark can run even faster with a little help.

Reactive programming company Typesafe today released a survey that confirms the high adoption rate of Apache Spark, an open source Big Data processing framework that improves traditional Hadoop-based ...

“Spark is well known for its in-memory performance, but Databricks and the open source community have also invested a great deal in optimizing on-disk performance, scalability, and stability,” said ...

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

Spark then utilizes Resilient Distributed Datasets (RDDs) and Data Frames for simplified, yet advanced data processing. Write programs that access, transform, and store the data.

Spark’s functionality for handling advanced data processing tasks such as real time stream processing and machine learning is way ahead of what is possible with Hadoop alone. This, along with ...