Automatic failover and load balancing: HBase runs on top of HDFS, which is internally distributed and automatically recovered using multiple block allocation and replications. It works with multiple HMasters and region servers. This failover is also facilitated using HBase and RegionServer replication.

Automatic sharding: An HBase table is made up of regions that are hosted by RegionServers and these regions are distributed throughout the RegionServers on different DataNodes. HBase provides automatic and manual splitting of these regions to smaller subregions, once it reaches a threshold size to reduce I/O time and overhead.

Hadoop/HDFS integration: It's important to note that HBase can run on top of other file systems as well. While HDFS is the most common choice as it supports data distribution and high availability using distributed Hadoop, for which we just need to set some configuration parameters and enable HBase to communicate to Hadoop, an out-of-the-box underlying distribution is provided by HDFS.

Real-time, random big data access: HBase uses log-structured merge-tree (LSM-tree) as data storage architecture internally, which merges smaller files to larger files periodically to reduce disk seeks.

MapReduce: HBase has a built-in support of Hadoop MapReduce framework for fast and parallel processing of data stored in HBase.

Java API for client access: HBase has a solid Java API support (client/server) for easy development and programming.

Thrift and a RESTtful web service: HBase not only provides a thrift and RESTful gateway but also web service gateways for integrating and accessing HBase besides Java code (HBase Java APIs) for accessing and working with HBase.

Support for exporting metrics via the Hadoop metrics subsystem: HBase provides Java Management Extensions (JMX) and exporting matrix for monitoring purposes with tools such as Ganglia and Nagios.

Distributed: HBase works when used with HDFS. It provides coordination with Hadoop so that distribution of tables, high availability, and consistency is supported by it.

Linear scalability (scale out): Scaling of HBase is not scale up but scale out, which means that we don't need to make servers more powerful but we add more machines to its cluster. We can add more nodes to the cluster on the fly. As soon as a new RegionServer node is up, the cluster can begin rebalancing, start the RegionServer on the new node, and it is scaled up, it is as simple as that.

Column oriented: HBase stores each column separately in contrast with most of the relational databases, which uses stores or are row-based storage. So in HBase, columns are stored contiguously and not the rows. More about row- and column-oriented databases will follow.

HBase shell support: HBase provides a command-line tool to interact with HBase and perform simple operations such as creating tables, adding data, and scanning data. This also provides full-fledged command-line tool using which we can interact with HBase and perform operations such as creating table, adding data, removing data, and a few other administrative commands.

Sparse, multidimensional, sorted map database: HBase is a sparse, multidimensional, sorted map-based database, which supports multiple versions of the same record.

Snapshot support: HBase supports taking snapshots of metadata for getting the previous or correct state form of data.