Today’s businesses are all about acquiring, storing, and managing data. In addition, rapidly growing data calls for real-time monitoring and analytics capabilities. Here is where powerful database-as-a-service platforms like MongoDB for analytics step in. MongoDB is battle-tested and renowned for its scalability, reliability, speed, user-friendliness, and flexible schema. Based on NoSQL, it is a document-oriented database that offers excellent performance. One of the most popular platforms, Analytics with MongoDB, is used by millions of professionals as it supports exploratory and predictive analysis.
Even though data analytics with MongoDB was not the original purpose of MongoDB when developed, the platform has grown exponentially to tackle such requirements. It is a versatile platform with a simple layout that anyone can get up and running in no time. Furthermore, unlike SQL relational databases, Analytics with MongoDB supports unstructured data like graphs, tables, texts, and documents stored in collections. As a result, it can handle a wide variety of workloads in a distributed fashion, including full-text searches, real-time analytics, and transactional data.
Analytics with MongoDB scales the ever-growing data with as many data nodes as necessary when it comes to cloud functionality. It can fetch data from the cloud instantly with the help of online archiving and data lakes. The system also focuses on automated security and backups to protect against data corruption.
Analytics with MongoDB offers many advantages when it comes to real-time analysis, let’s have a look at the top advantages of MongoDB:
MongoDB’s sharding feature allows it to manage and distribute data across multiple servers, datasets and nodes. Shards are multiple replicas sets that make use of horizontal scaling of databases. The platform balances out the data based on its volume and cluster size with automatic sharding.
Thanks to the document-oriented nature of the database, the platform delivers consistent performance. Data is queried quickly, and its indexing removes the need for separate caching tiers, making analytics with MongoDB faster than traditional relational databases.
Users can select the instance size that meets their requirements. They can adjust and scale the clusters automatically to fit the workload. MongoDB for analytics is easy to use and can be used efficiently by beginners compared to other databases. The simpler query syntax is much easier to grasp when compared to SQL. The reduced training time and set-up results in considerable savings in the long run.
Analytics with MongoDB offers a vast degree of freedom when storing, managing, and accessing data. Thanks to its NoSQL design, there’s no need to create tables. This advantage comes in handy when dealing with huge amounts of data, especially unstructured data. In addition, the platform can keep up with businesses as they continue to evolve with time by adapting to the changes and workload requirements.
As we discussed the advantages of MongoDB which make it the most popular NoSQL database for analytics, you might be wondering how to fuel your business analytics with MongoDB? There are many ways MongoDB can enhance your analytics but let’s discuss three of them in detail:
- Real-time analytics: Known for its robustness and next-generation capabilities, MongoDB for analytics is the top choice for the world’s leading enterprises to manage their data. Companies big and small, startups and entrepreneurs, rely on analytics with MongoDB for its cutting-edge solutions and sophisticated features. As a result, the database platform has made its mark in every sector, including IT, e-commerce, medicine, finance, manufacturing, and logistics. Some of the biggest brands that use MongoDB for analytics are eBay, Barclays, Metlife, EA, Uber, Bosch, Adobe, and Forbes, to name a few.
- Speeds up data conversions: Analytics with MongoDB offers high performance and low latency, even with a huge data volume workload. The platform has been designed with developers in mind and appeals to them in many ways. MongoDB provides real-time analytics tools and data science frameworks like Apache Spark widely used by scientists, engineers, and analysts. It is an ever-evolving platform that aims at improving analytics and speeding up data conversions, apart from being able to connect with other business intelligence tools.
- Edge over the competitors: MongoDB for analytics is more than capable of handling Big Data most efficiently. The document-based design and JSON data validation tools ensure that the correct data is stored and can be fetched using far lesser queries than a SQL database. The high-speed nature of analytics with MongoDB makes it far more suited for such purposes and is simple to use certainly gives it an edge over the competition.
We need to understand where and why we need to use MongoDB in real-life applications. MongoDB is preferred by the developers or managers when our primary concern is dealing with large volumes of data with high performance. MongoDB is outstanding at transferring thousands of records in a second. As you may be aware, horizontal scaling (adding new columns) is not an easy process in any RDBMS system. But with MongoDB, it is easier since it is a schema-less database. Also, there is no need for any administrative work to perform horizontal scaling in MongoDB.
MongoDB is suitable for the below types of situations:
- E-Commerce type of product-based applications
- Blog and Content Management systems
- High-Speed logging and caching in Real-time
- To maintain location-wise geospatial data
- To support data related to the Social and Networking sites.
Analytics with MongoDB offers high performance and easy data retrieval because of its embedded document-based structure. It helps businesses with better decision-making, thereby increasing revenue and sales. Organizations worldwide are investing a lot of money to fuel data analytics which the MongoDB platform can handle easily. With MongoDB, organizations serve more data, more users, and more insights with greater ease, thereby creating more value worldwide.
Rawcubes provide real-time analytics with MongoDB. We examine a range of solutions to undertake analytics against your data in MongoDB. These solutions range from performing analytics with MongoDB directly to moving MongoDB data to a data store suitable for analytics. Contact Rawcubes for an in-depth analysis of how to implement each of these alternatives and solutions to fuel your analytics, and how to evaluate which is right for your business.