Reasons to Use a Nosql Database

Introduction

NoSQL is an approach to database design that can accommodate a wide variety of data models, including key-value, document, columnar and graph formats. NoSQL, which stand for "not only SQL," is an alternative to traditional relational databases in which data is placed in tables and data schema is carefully designed before the database is built. NoSQL databases are designed for working with huge distributed data.

Reasons to use a nosql database

NoSQL encompasses a wide variety of different database technologies that were developed to meet the demands presented in building modern applications: Structured, semi-structured, unstructured and polymorphic data helps developers working with applications that create massive volumes of new, rapidly changing data types Help small team as it can handle Agile sprints, quick iteration, and frequent code pushes every week or two, some even multiple times every day.

It also handles Object-oriented programming that is easy to use and flexible. Applications that once served a finite audience are now delivered as services that must be always-on, accessible from many different devices and scaled globally to millions of users. Organizations are now turning to scale-out architectures using open source software, commodity servers and cloud computing instead of large monolithic servers and storage infrastructure.

Popular nosql databases

MongoDB—the most popular NoSQL system, especially among startups. A document-oriented database with JSON-like documents in dynamic schemas instead of relational tables that’s used on the back end of sites like Craigslist, eBay, Foursquare. It’s open-source, so it’s free, with good customer service. Apache’s CouchDB—a true DB for the web, it uses the JSON data exchange format to store its documents; JavaScript for indexing, combining and transforming documents; and, HTTP for its API. HBase—another Apache project, developed as a part of Hadoop, this open-source, non-relational “column store” NoSQL DB is written in Java, and provides BigTable-like capabilities. Oracle NoSQL—Oracle’s entry into the NoSQL category.

Apache’s Cassandra DB—born at Facebook, Cassandra is a distributed database that’s great at handling massive amounts of structured data. Anticipate a growing application? Cassandra is excellent at scaling up. Examples: Instagram, Comcast, Apple, and Spotify. Riak—an open-source key-value store database written in Erlang. It has fault-tolerance replication and automatic data distribution built in for excellent performance.

NoSQL Database Types:

• Key-value stores Key-value stores, or key-value databases, implement a simple data model that pairs a unique key with an associated value. Because this model is simple, it can lead to the development of key-value databases, which are extremely performant and highly scalable for session management and caching in web applications.
• Document databases Document databases, also called document stores, store semi-structured data and descriptions of that data in document format. They allow developers to create and update programs without the of need of master schema. Use of document databases has increased along with use of JavaScript and the JavaScript Object Notation (JSON), a data interchange format that has gained wide currency among web application developers, although XML and other data formats can be used as well. Document databases are used for content management and mobile application data handling. Couchbase Server, CouchDB, DocumentDB, MarkLogic and MongoDB are examples of document databases.
• Wide-column stores Wide-column stores organize data tables as columns instead of as rows. Wide-column stores can be seen in both in SQL and NoSQL databases. Wide-column stores can query large data volumes faster than conventional relational databases. A wide-column data store can be used for recommendation engines, catalogs, fraud detection and other types of data processing. Google BigTable, Cassandra and HBase are examples of wide-column stores.
• Graph stores Graph data stores organize data as nodes, which are like records in a relational database, and edges, which represent connections between nodes. Because the graph system stores the relationship between nodes, it can support richer representations of data relationships. Also, unlike relational models reliant on strict schemas, the graph data model can evolve over time and use. Graph databases are applied in systems that must map relationships, such as reservation systems or customer relationship management. Examples of graph databases include AllegroGraph, IBM Graph, Neo4j and Titan.