Apache Spark™ is a fast and general engine for large-scale data processing.
Speed
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.
Spark has an advanced DAG execution engine that supports cyclic data flow and in-memory computing.
Logistic regression in Hadoop and Spark
Ease of Use
Write applications quickly in Java, Scala, Python, R.
Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python and R shells.
text_file = spark.textFile("hdfs://...")
text_file.flatMap(lambdaline:line.split()) .map(lambda word: (word, 1)) .reduceByKey(lambda a, b: a+b)
Word count in Spark's Python API
Generality
Combine SQL, streaming, and complex analytics.
Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.
Runs Everywhere
Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access perse data sources including HDFS, Cassandra, HBase, and S3.
Spark is used at a wide range of organizations to process large datasets. You can find example use cases at the Spark Summit conference, or on the Powered By page.
