Cassandra vs HBase: What are the differences?
Developers describe Cassandra as "A partitioned row store. Rows are organized into tables with a required primary key". Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL. On the other hand, HBase is detailed as "The Hadoop database, a distributed, scalable, big data store". Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
Cassandra and HBase belong to "Databases" category of the tech stack.
"Distributed" is the top reason why over 96 developers like Cassandra, while over 7 developers mention "Performance" as the leading cause for choosing HBase.
Cassandra and HBase are both open source tools. Cassandra with 5.27K GitHub stars and 2.35K forks on GitHub appears to be more popular than HBase with 2.91K GitHub stars and 2.01K GitHub forks.
Uber Technologies, Facebook, and Spotify are some of the popular companies that use Cassandra, whereas HBase is used by Pinterest, HubSpot, and Yammer. Cassandra has a broader approval, being mentioned in 342 company stacks & 239 developers stacks; compared to HBase, which is listed in 54 company stacks and 18 developer stacks.
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