1, spark official website: http://spark.apache.org/
Apache Spark™ is a unified analytics engine for large-scale data processing.
2, four features of spark:
Speed
Run workloads 100x faster.
Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine.
Ease of Use
Write applications quickly in Java, Scala, Python, R, and SQL.
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, R, and SQL shells.
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, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources.
You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.
All package addresses related to spark and packages under apache:
http://archive.apache.org/dist/
Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). It’s easy to run locally on one machine — all you need is to have javainstalled on your system PATH, or the JAVA_HOME environment variable pointing to a Java installation.
Spark runs on Java 8+, Python 2.7+/3.4+ and R 3.1+. For the Scala API, Spark 2.3.0 uses Scala 2.11. You will need to use a compatible Scala version (2.11.x).
Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0. Support for Scala 2.10 was removed as of 2.3.0.