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    NoSQL (Not Only SQL) is a type of database for web applications and big data which allows users to store and retrieve information in a format that is intuitive and easy to understand. Instead of complex tables or data, NoSQL stores information as ASCII text and manipulates it with built-in operators.  Leveraging cloud computing to eliminate downtime, NoSQL provides mechanisms different from the fixed tables, columns, and rows used in relational databases. 

    The name “NoSQL” was first used in 1998 to identify Carl Strozzi’s Strozzi NoSQL open-source relational database. NoSQL databases are flexible, scalable, non-tabular, and non-relational, embracing simplicity in design to provide developers with flexibility in scaling big data. They feature agile schemas that offer efficient functionality and high performance and availability.

    A common misconception of NoSQL is that it does not support SQL-like queries. To the contrary, NoSQL can actually stand alongside SQL databases, as they stores relationship data differently from traditional databases. Users can stock related data in a single structure without the need to split them between tables. 

    With their simplicity and scalability, NoSQL databases are developer-friendly. As cloud computing offers cost-efficient hosting of data and applications, the volume of data and queries also grows. Software engineers can quickly adapt to fast-changing data requirements. It allows developers to store large amounts of unstructured data and enable data distribution across multiple servers.

    Types of NoSQL Databases

    There are four types of databases to choose to make managing large volumes of data and user loads easier:  

    • Key-value databases stand out for their simplicity, where every item has keys and values. Each value is retrievable by its reference key. This type of database lets you store large volumes of data and retrieve information without performing complex queries. 
    • Document databases allow you to store data in documents that contain fields and values in pairs. The values can be numbers, arrays, strings, objects, or booleans with structures aligning with objects in code. A document database offers a variety of uses, and it can store large volumes of data.
    • Graph databases employ nodes and edges as data storage. The node can host data about people, places, and things, and the edges keep the relationships between the nodes. 
    • Wide-column stores use tables, rows, and dynamic columns in storing data. With wide-column stores, it is easier to predict query patterns in multi-volume data, which is great for managing IoT information and user profiles. 

    What makes NoSQL different from MySQL?

    Structured query language (SQL) databases — like Oracle SQL Developer, IBM Db2, Microsoft SQL Server, or the open-source MySQL — are traditional and popular relational databases that store data in tables with fixed rows and columns. Storage and retrieval is accomplished by the application negotiating the tabular relationships between data. 

    SQL databases demand more processing resources and time. Before starting the development, database and software engineers must plan their projects to the minute details, creating complex E-R (entity-relationship) diagrams to reduce data duplication. 

    SQL databases feature standardized interfaces, while NoSQL databases prefer accessibility, tolerance to partition, and speed over standardization. 

    One advantage of NoSQL over MySQL is the flexibility of its design schema. SQL relational databases, requires a detailed database model. With NoSQL, which is naturally non-relational, it is easier to scale to meet the demands of big data by adding using documents. MySQL has a standard query language that NoSQL might lack. 

    Examples of NoSQL databases include MongoDB and CouchDB. 

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