Impact of MongoDB in Data science

We are seeing a growing interest in alternate database management systems that are different from the traditional relational database like Oracle and SAP.With all the excitement surrounding the relatively recent wave of non-relational – otherwise known as “NoSQL” – databases, it can be hard to separate the hype from the reality. The term NoSQL is generally used to describe the databases that doesn’t use SQL to connect.Being non-relational, distributed, open-source and horizontally scalable there is a lot to talk about NoSQL in action.

Editor’s Note: MongoDB’s rise is being fueled by the immense demand for Big Data Analytics. Learn what big data is, what are its related tools and how it is affecting the data science sector. Enroll for our Big Data Analyticsnow.

MongoDB is a general purpose database commercially developed and supported by a company called 10gen. There are a quite lot of projects now using MongoDB. Its dynamic schema and object-oriented structure make it a great fit for real-time analytics and dashboard along with e-commerce, mobile, archiving and more. Popular MongoDB use case examples include big data, content management and delivery, mobile and social infrastructure, user data management, data hubs and more. Operational Intelligence, Real Time Analytical use, Storing Log data areas have got concrete examples of use.

Shutterfly is a popular, Internet-based, photo sharing and personal publishing company that manages a persistent store of more than 6 billion images with a transaction rate of up to 10,000 operations per second. Data Architect Kenny Gorman accepted the task of helping Shutterfly select and implement a replacement for its existing relational database management system the Oracle’s RDBMS to MongoDB. MongoDB has been adopted as backend software by a number of major websites and services, including Craigslist, eBay, Foursquare, SourceForge, and the New York Times

Important features of MongoDB

  • Document-Oriented Storage: JSON-style documents with dynamic schema offers simplicity and power.
  • Full Index Support: Index on any attribute, just like we are used to.
  • Replication and High Availability: Mirror across LANs and WANs for scale.
  • Auto-Sharding: Scale horizontally without compromising functionality. Sharding distributes a single logical database system across a cluster of machines.
  • Querying: Rich, document-based queries.
  • Fast In-Place Updates: Atomic modifiers for contention-free performance.
  • Map/Reduce: Flexible aggregation and data processing.
  • GridFS: Store files of any size without complicating your stack. Instead of storing a file in a single document, GridFS divides a file into parts, or chunks, and stores each of those chunks as a separate document.
  • Online shell allows the facility of trying out MongoDB without installing.

The rise of big data is helping fuel MongoDB’s growth. Companies are seeking new ways to manage a flood of information on their customers and the markets they serve, aiming to use the data to make decisions in real time. The market for big-data technology and services will grow at 32 percent annually, as predicted by research firm IDC.

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