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Store Big Data with HBase

By Judith Hurwitz, Alan Nugent, Fern Halper, Marcia Kaufman

HBase is a distributed, nonrelational (columnar) database that utilizes HDFS as its persistence store for big data projects. It is modeled after Google BigTable and is capable of hosting very large tables (billions of columns/rows) because it is layered on Hadoop clusters of commodity hardware.

HBase provides random, real-time read/write access to big data. HBase is highly configurable, providing a great deal of flexibility to address huge amounts of data efficiently. Now take a look at how HBase can help address your big data challenges.

HBase is a columnar database, so all data is stored into tables with rows and columns similar to relational database management systems (RDBMSs). The intersection of a row and a column is called a cell. One important difference between HBase tables and RDBMS tables is versioning.

Each cell value includes a “version” attribute, which is nothing more than a timestamp uniquely identifying the cell. Versioning tracks changes in the cell and makes it possible to retrieve any version of the contents should it become necessary. HBase stores the data in cells in decreasing order (using the timestamp), so a read will always find the most recent values first.

Columns in HBase belong to a column family. The column family name is used as a prefix to identify members of its family. For example, fruits:apple and fruits:banana are members of the fruits column family. HBase implementations are tuned at the column family level, so it is important to be mindful of how you are going to access the data and how big you expect the columns to be.

The rows in HBase tables also have a key associated with them. The structure of the key is very flexible. It can be a computed value, a string, or even another data structure. The key is used to control access to the cells in the row, and they are stored in order from low value to high value.

All of these features together make up the schema. The schema is defined and created before any data can be stored. Even so, tables can be altered and new column families can be added after the database is up and running. This extensibility is extremely useful when dealing with big data because you don’t always know about the variety of your data streams.