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HBase Development Company

HBase is a column-oriented non-relational database management system and runs on top of the Hadoop Distributed File System (HDFS). HBase provides the best way of storing data sets, which are common in many big data use cases. It is well suited for real-time data processing or random read or write access to large volumes of data. HBase can store huge amounts of data from terabytes to petabytes. The tables present in Apache HBase consists of billions of rows having millions of columns. It is built for low operations, which is having some specific features compared to traditional relational models.

The first HBase prototype was created as a Hadoop contribution in Feb 2007. HBase features like working with sparse data in an extremely resilient and fault-tolerant way and it can work on multiple types of data also making it useful for varied business scenarios.

HBase Development Features

Atomic Read and Write

During one read or write process, all other processes are prevented from performing any read or write operations and HBase offers atomic read and write, on a low level.

Consistency

We can use this Apache HBase database feature for high-speed requirements because it offers consistent reads and writes.

High Availability

This database offers LAN and WAN which supports failover and recovery. Basically, there is a master server, at the core, which handles the region servers as well as all metadata for the cluster.

Hadoop/HDFS integration

HBase can run on top-level of other file systems as well as like Hadoop/HDFS integration.

Backup Support

In the “Backup support” means it supports the backup of all the tables of Hadoop in HBase.

Schema-less

No topic of fixed column’s schema in HBase because it is schema-less and it defines only column families.

Benefits of using Apache HBase for
Database Development

This database can handle very large volumes of data.
Great for analytics in association with Hadoop MapReduce Index.
Much flexible on schema design.
Row-level atomicity, that is the PUT operation will either fail or write.
Can be integrated with Hive for SQL queries, which is better for DBAs who are more familiar with SQL queries.
Operations such as data processing and reading will take a small amount of time as compared to traditional relational models.

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