Hadoop is the de facto standard for Big Data and HBase is the Hadoop database.
Introduction of HBase
Traditional databases confine users to define a strict data structure in order to reduce disk usage, data tables would be joined again when users need bigger logical views, this would cause a real bottleneck in Big Data world if data tables are too big to store and to join again, even using sharded databases they still have scalability limits.
On the other hand, HBase allows open data structure, there is no type nor length boundary on any HBase column value, there is also no limit on the number of columns, a column family could have millions of columns, that means: You can store anything in HBase no matter what size with random access, auto-failover, automatic versioning, automatic sharding, extreme scalability, and strict data consistency!
You may not have petabytes of data that you need to analyze today, nevertheless, you can deploy HBase and Hadoop with confidence and can store anything in Hadoop and HBase, it is proven at scale because the user community of Hadoop and HBase is global, active and diverse, the success of the biggest Web companies in the world demonstrates that Hadoop can grow as your business does, companies across many industries participate, including financial services, social networking, media, telecommunications, retail, health care and others. For more information, please read: Who uses HBase and Hadoop.
The way to start your HBase Project
Data-driven decisions and applications creates immense value from Big Data. There are 3 key steps to start a HBase project successfully:
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