* 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 a REST-ful Web service that supports XML, Protobuf, and binary data encoding options * Extensible jruby-based (JIRB) shell * Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX

* Fast processing of OLAP workloads. * Integration with MapReduce, Spark and other Hadoop ecosystem components. * Tight integration with Cloudera Impala, making it a good, mutable alternative to using HDFS with Parquet. * Strong but flexible consistency model, allowing you to choose consistency requirements on a per-request basis, including the option for strict-serializable consistency. * Strong performance for running sequential and random workloads simultaneously. * Easy to administer and manage with Cloudera Manager. * High availability. Tablet Servers and Masters use the Raft Consensus Algorithm, which ensures that as long as more than half the total number of replicas is available, the tablet is available for reads and writes. For instance, if 2 out of 3 replicas or 3 out of 5 replicas are available, the tablet is available. * Reads can be serviced by read-only follower tablets, even in the event of a leader tablet failure. * Structured data model.