The future belongs to enterprises that go beyond analyzing the growing data and extracting business value out of it. From IBM’s navigational database in the 1960s, we have come a long way to the widespread use of RDBMS. Its edge over the flat file system resulted in extensive use. But when data is as big as it is today, RDBMS faces some serious analysis problems. This is where NoSQL databases comes in to play. MongoDB is one of the most popular NoSQL database with features that makes it a natural choice when it comes to implementing NoSQL database system. Let’s look at some reasons that makes it imperative to be updated with MongoDB.
The Age of NoSQL Database:
RDBMS falls short when it comes to handling Big data challenges like Count Distinct Queries. It addresses the issues that arise when dealing with data entities with multiple dimensions. The current fixed-table schemas and join-operations framework in RDBMS are not capable enough to manage unstructured data. However, NoSQL database seeks to address that issue. Instead of following the traditional table-based relational database model, NoSQL employs a dynamic JSON document-oriented model, facilitating easy and quick data integration. Data professionals need to update themselves about the new technology.
Rapid changes in applications and user & infrastructure characteristics are molding the data world to fit the NoSQL database model. The most popular NoSQL database system is MongoDB. It is flaunted as the future of database systems and is already enjoying a prominent position in modern application development.Giants like CISCO, SAP, IBM, Microsoft, and Salesforce are already part of the growing MongoDB development community.
It is the obvious industry choice when it comes to distributed document databases. Naturally, there is going to be a great demand for data professionals with in-depth knowledge in MongoDB.
MongoDB Industry Trends:
MongoDB accounts for about half of all the mentions of NoSQL technologies in LinkedIn profiles.
Big Advantages with MongoDB
- File Storage – Large files can be stored without the need to load entire files into memory
- Data Duplication – Fault-tolerance data duplication to keep the system up and running
- Replication – Disaster management in case of data loss from a server: master-slave configuration for intelligent copying, writing, and storing of data
- Load Balancing – Automatic deployment of data on different shards for smooth load balancing
- Capped Collections – Maintenance of objects in the collection as per the order of insertion
- AdHoc Queries – Support search by field, range queries, and regular expression searches
- Indexing – Any field in a document can be indexed to narrow down and match a query
- Aggregation – Batch processing of data and aggregate calculations without the need for complex map-reduce operations
MongoDB leverages horizontal scalability effortlessly by using commodity computers, and it does not require a complex or rigid schema to insert, store, and process different data sets.
The quest to replace relational database is already on and enterprises have started shifting towards NoSQL as a viable alternative. Given the changing landscape, professionals with MongoDB skills are sure to find promising jobs. For those pursuing or aspiring for a career in data analytics or in the database field, MongoDB is a must have skill to stay updated.