When it comes for storage of BigData there is no better option than a NoSQL database and in particular to mention, MongoDB is one of the best.

Large-scale web organizations such as Facebook, Google and Amazon uses NoSQL databases to focus on narrowing their operational goals and employ relational databases as adjuncts where high-grade data consistency is necessary.

A NoSQL ( “non SQL” or “non relational” ) database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases.  NoSQL database design implements a key-value store, document store, column store or graph format for data.

Why MongoDB ?

MongoDB stores data in JSON-like documents that can vary in structure. Related information is stored together for fast query access through the MongoDB query language. MongoDB uses dynamic schemas, meaning that you can create records without first defining the structure, such as the fields or the types of their values.

• The document model maps to the objects in application code, making data easy to work with
• Ad hoc queries, indexing, and real-time aggregation provide powerful ways to access and analyze data
• MongoDB is a distributed database at its core, so high availability, horizontal scaling, and geographic distribution are built in and easy to use

With MongoDB, we can build applications that were never possible with traditional relational databases. Here’s how.

• Fast, Iterative Development :-  A flexible data model coupled with dynamic schema and idiomatic drivers make it fast for developers to build and evolve applications. Automated provisioning and management enable continuous integration and highly productive operations. Contrast this against static relational schemas and complex operations that have hindered in the past.
• Flexible Data Model :- MongoDB’s document data model makes it easy for storing and combining data of any structure, without giving up sophisticated validation rules, data access and rich indexing functionality. We can dynamically modify the schema without downtime. We spend less time prepping the data for the database, and more time putting the data to work.
• Multi-Datacenter Scalability :- MongoDB can be scaled within and across geographically distributed data centers, providing new levels of availability and scalability. As our deployments grow in terms of data volume and throughput, MongoDB scales easily with no downtime, and without changing our application. And as our availability and recovery goals evolve, MongoDB lets us adapt flexibly, across data centers, with tunable consistency.
• Integrated Feature Set :- Analytics and data visualization, text search, graph processing, geospatial, in-memory performance and global replication allows us to deliver a wide variety of real-time applications on the technology, reliably and securely. RDBMS systems require additional, complex technologies demanding separate integration overhead and expense to do this well.
• Lower TCO :- Application development teams are more productive when they use MongoDB. Single click management means operations teams are as well. MongoDB runs on commodity hardware, dramatically lowering costs.

With the recent development of MongoDB, now we have a lot of options from different software to use drivers/connectors for connecting to MongoDB database and use the data for various processing/analysis/analytics purposes.

We have implemented the best in industry for giving better performance for both storage as well as processing of BigData with help of Hadoop and MongoDB. With help of multiple parallel processing of BigData using the storage & scalability from MongoDB, we intend to provide best in industry for our performance & customers.