• • Linear and modular scalability.
  • Strictly consistent reads and writes.
  • Automatic and configurable sharding of tables.
  • Automatic failover support between RegionServers.

• • Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables.
  • Easy to use Java API for client access.
  • Block cache and Bloom Filters for real-time queries.
  • Query predicate push down via server side Filters.
  • Thrift gateway and an REST-ful Web service that supports XML, Protobuf, and binary data encoding options.

How to use Apache HBase REST Interface

• Extensible JRuby-based (JIRB) shell.
• Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX.

• Apache Spark is an open-source distributed cluster-computing framework.
• Spark is a data processing engine developed to provide faster and ease-of-use analytics than Hadoop MapReduce.
• Before Apache Software Foundation took possession of Spark, it was under the control of University of California, Berkeley’s AMP Lab.

• Apache Hadoop is an open-source framework written in Java that allows us to store and process Big Data in a distributed environment, across various clusters of computers using simple programming constructs.
• To do this, Hadoop uses an algorithm called MapReduce, which divides the task into small parts and assigns them to a set of computers.
• Hadoop also has its own file system, Hadoop Distributed File System (HDFS), which is based on the Google File System (GFS).
• HDFS is designed to run on low-cost hardware.