NoSQL Databases

NoSQL Databases: 

Introduction to NoSQL

A term for any type of database that does not use SQL for the primary retrieval of data from the database. NoSQL databases have limited traditional functionality and are designed for scalability and high performance retrieve and append. Typically, NoSQL databases store data as key-value pairs, which works well for data that is unrelated. 

NoSQL databases can store relationship data—they just store it differently than relational databases do. When compared with SQL databases, many find modelling relationship data in NoSQL databases to be easier than in SQL databases, because related data doesn’t have to be split between tables.

NoSQL data models allow related data to be nested within a single data structure.

Here are the four main types of NoSQL databases:

• Document databases

A document database stores data in JSON, BSON, or XML documents (not Word documents or Google docs, of course). In a document database, documents can be nested. Particular elements can be indexed for faster querying.

Documents can be stored and retrieved in a form that is much closer to the data objects used in applications, which means less translation is required to use the data in an application. SQL data must often be assembled and disassembled when moving back and forth between applications and storage.

• Key-value stores

Key-value databases are a simpler type of database where each item contains keys and values. A value can typically only be retrieved by referencing its key, so learning how to query for a specific key-value pair is typically simple. Key-value databases are great for use cases where you need to store large amounts of data but you don’t need to perform complex queries to retrieve it. Common use cases include storing user preferences or caching. Redis and DynamoDB are popular key-value databases.

• Column-oriented databases

Wide-column stores store data in tables, rows, and dynamic columns. Wide-column stores provide a lot of flexibility over relational databases because each row is not required to have the same columns. Many consider wide-column stores to be two-dimensional key-value databases. Wide-column stores are great for when you need to store large amounts of data and you can predict what your query patterns will be. Wide-column stores are commonly used for storing Internet of Things data and user profile data. Cassandra and HBase are two of the most popular wide-column stores.

• Graph databases

Graph databases store data in nodes and edges. Nodes typically store information about people, places, and things while edges store information about the relationships between the nodes. Graph databases excel in use cases where you need to traverse relationships to look for patterns such as social networks, fraud detection, and recommendation engines. Neo4j and JanusGraph are examples of graph databases.