Why is there NoSQL database?

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Why is there NoSQL database?

NoSQL databases break the traditional mindset of storing data at a single location. Instead, NoSQL distributes and stores data over a set of multiple servers. This distribution of data helps the NoSQL database server to distribute the load on the database tier.

What language is used in NoSQL?

Traditional SQL developers looking warily at the rapid growth of NoSQL databases need not worry about transferring their skills to a new programming paradigm, as UnQL, the query language specification for NoSQL, features many of the same constructs as SQL itself.

Can NoSQL be relational?

SQL databases are known as relational databases, and have a table-based data structure, with a strict, predefined schema required. NoSQL databases, or non-relational databases, can be document based, graph databases, key-value pairs, or wide-column stores.

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What are the major problems with NoSQL databases?

Most NoSQL databases offer a concept of eventual consistency in which database changes are propagated to all nodes so queries for data might not return updated data immediately or might result in reading data that is not accurate which is a problem known as stale reads. Also some NoSQL systems may exhibit lost writes and other forms of data loss.

What are the four data architecture patterns of NoSQL?

The data is stored in NoSQL in any of the following four data architecture patterns. 1. Key-Value Store Database 2. Column Store Database 3. Document Database 4. Graph Database These are explained as following below. 1. Key-Value Store Database:

What is the acid of a NoSQL database?

Most NoSQL stores lack true ACID (Atomicity, Consistency, Isolation, Durability) transactions but a few databases, such as MarkLogic, Aerospike, FairCom c-treeACE, Google Spanner (though technically a NewSQL database), Symas LMDB, and OrientDB have made them central to their designs.

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What is sharding in NoSQL database?

NoSQL database use sharding for horizontal scaling. Partitioning of data and placing it on multiple machines in such a way that the order of the data is preserved is sharding. Vertical scaling means adding more resources to the existing machine whereas horizontal scaling means adding more machines to handle the data.