Demystifying NoSQL: Revolutionizing the Database Landscape Beyond Relational Models!

NoSQL (Not only SQL) refers to databases with a non-relational approach, which are also referred to as structured data storage. Examples of NoSQL include Amazon Dynamo and Google BigTable, as well as open-source Apache Cassandra, CouchDB, OrientDB, and MongoDB. The term NoSQL in this special sense of “Not only” has been used since the beginning of 2009 (introduced by Johan Oskarsson) and describes a new concept that tries to overcome the limitations of relational databases, especially in distributed data storage systems. In order to deal efficiently with very large amounts of data, frequent read and write requests, complex data structures and interlinked data, the relational model, in particular fixed table schemata and joins, is dispensed with. This relational model is only efficient for clustered small transactions and large batch transactions with almost exclusively read access. A relational database is scaled up by ever faster hardware and additional memory. NoSQL, on the other hand, uses a process known as “scaling out”. The load is distributed over any number of systems. Currently NoSQL can be divided into 4 categories:

Key-Value databases use a hash table in which keys are entered with a respective pointer to a data set. Such images are usually provided with caching mechanisms.

Column-oriented databases are used to store very large amounts of data that are distributed across many computers. A row key addresses an entire row of a column family.

Document-oriented databases use documents that are collections of other key-value collections. The semi-structured documents are stored in file formats such as JSON.

Object databases are made up of graphs consisting of nodes, edges or relations and node properties. This graph modeling scales across many computers.

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