Apache Spark is an open source cluster computing system that aims to make data analytics fast — both fast to run and fast to write, originally developed in the AMPLab at UC Berkeley.
Apache Spark is an open source cluster computing system that aims to make data analytics fast — both fast to run and fast to write.
To run programs faster, Spark offers a general execution model that can optimize arbitrary operator graphs, and supports in-memory computing, which lets it query data faster than disk-based engines like hadoop.
Spark is not tied to the two-stage mapreduce paradigm, and promises performance up to 100 times faster than Hadoop MapReduce
Spark provides primitives for in-memory cluster computing that allows user programs to load data into a cluster's memory and query it repeatedly, making it well suited to machine learning algorithms. To make programming faster, Spark provides clean, concise APIs in scala, java and python. You can also use Spark interactively from the scala and python shells to rapidly query big datasets.
Recommended reference sources:
Learning Spark - Lightning-Fast Big Data Analysis
AMP Camp 5 (Berkeley, CA, November 20-21, 2014)
AMP Camp 4 (Strata Santa Clara, Feb 2014) — focus on BlinkDB, MLlib, GraphX, Tachyon
AMP Camp 3 (Berkeley, CA, Aug 2013)
AMP Camp 2 (Strata Santa Clara, Feb 2013)
AMP Camp 1 (Berkeley, CA, Aug 2012)