Over the past decade, NoSQL databases were developed to address the needs of modern Big Data applications in ways that traditional relational databases fall short. Developers want
databases that are well-suited to handle the variety, volume and velocity of Big Data applications. They also want a database that works well with today’s iterative development sprints and with cloud computing and commodity hardware.
NoSQL technologies answered these requirements but tend to vary greatly in their response. There are essentially four types:
- Key-value stores. The simplest of the NoSQL databases, key-value stores have each item in the database stored as an attribute name together with its value. Riak, Voldemort, and Redis are examples.
- Wide-column stores. Cassandra and HBase are examples of this type of database where data is stored together in columns.
- Document databases. MongoDB is the most well-known document database. These types of databases store data in documents.
- Graph databases. Neo4J and HyperGraphDB are popular examples of graph database which are useful for data about networks.
MongoDB leads the pack of NoSQL databases according to the latest DB-Engines rankings. In fact, over a third of the Fortune 100 companies choose MongoDB. MongoDB combines the best of relational databases with the innovations that make NoSQL so popular today.
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