What is ?
Apache® Spark™ is a powerful open source processing engine built around speed, ease of use, and sophisticated analytics. It was originally developed at UC Berkeley in 2009. Since its release, Spark has seen rapid adoption by enterprises across a wide range of industries. It has quickly become the largest open source community in big data, with over 1000 contributors from 250+ organizations.
Spark comes packaged with support for ETL, interactive queries (SQL), advanced analytics (e.g. machine learning) and streaming over large datasets. For loading and storing data, Spark integrates with many storage systems (e.g. HDFS, Cassandra, HBase, S3). Spark is also pluggable, with dozens of third party libraries and storage integrations.
Apache® Spark™ Ecosystem
Structured Data: Spark SQL
Many data scientists, analysts, and general business intelligence users rely on interactive SQL queries for exploring data. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as distributed SQL query engine. It enables unmodified Hadoop Hive queries to run up to 100x faster on existing deployments and data. It also provides powerful integration with the rest of the Spark ecosystem (e.g., integrating SQL query processing with machine learning).
Streaming Analytics: Spark Streaming
Many applications need the ability to process and analyze not only batch data, but also streams of new data in real-time. Running on top of Spark, Spark Streaming enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics. It readily integrates with a wide variety of popular data sources, including HDFS, Flume, Kafka, and Twitter.
Machine Learning: MLlib
Machine learning has quickly emerged as a critical piece in mining Big Data for actionable insights. Built on top of Spark, MLlib is a scalable machine learning library that delivers both high-quality algorithms (e.g., multiple iterations to increase accuracy) and blazing speed (up to 100x faster than MapReduce). The library is usable in Java, Scala, and Python as part of Spark applications, so that you can include it in complete workflows.
Graph Computation: GraphX
GraphX is a graph computation engine built on top of Spark that enables users to interactively build, transform and reason about graph structured data at scale. It comes complete with a library of common algorithms.
General Execution: Spark Core
Spark Core is the underlying general execution engine for the Spark platform that all other functionality is built on top of. It provides in-memory computing capabilities to deliver speed, a generalized execution model to support a wide variety of applications, and Java, Scala, and Python APIs for ease of development.
What are the benefits of Apache Spark?
Ease of Use
Spark has easy-to-use APIs for operating on large datasets. This includes a collection of over 100 operators for transforming data and familiar data frame APIs for manipulating semi-structured data.
Engineered from the bottom-up for performance, Spark can be 100x faster than Hadoop for large scale data processing, by exploiting in memory computing and other optimizations. Spark is also fast when data is stored on disk, and currently holds the world record for large-scale on-disk sorting.
A Unified Engine
Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning and graph processing. These standard libraries increase developer productivity and can be seamlessly combined to create complex workflows.
For more information:
- Apache Spark Quick Start Guide
- Apache Spark Documentation
- Learning Spark, by Holden Karau, Andy Konwinski, Patrick Wendell and Matei Zaharia (O’Reilly Media)
- Spark in Action, by Marko Bonaci and Petar Zecevic (Manning)
- Advanced Analytics with Spark, by Sandy Ryza, Uri Laserson, Sean Owen and Josh Wills (O’Reilly Media)
Download Apache Spark
The latest release of Apache Spark 2.2.0, released today, July 11, 2017 . It can be downloaded from Apache.