If we want to improve our I/O speed and performance i.e. reading and writing the data from the storage then we can take the help of Hard drives of high performance.
But here we have on challenges if data comes in a huge amount (Big data) it will impact the I/O speed and performance of the Hard drives again.
For this issue by just organizing and structuring the data, we can optimize I/O operation. This structuring is known as Data Model. We have to first observe our data and use-case then accordingly create our data model. The tool which will help us to create this data model is databases.
There are many databases that don't have flexibility in their schema i.e. we can’t add any extra fields on the fly if any need come in the future.
For this challenge, we have to go for No Structure or schemaless databases which provide flexibility in the data fields on the fly. Files, where we keep our data, is called Tables and folder, where we keep the data is called database. One of such databases is MongoDB.
MongoDB is a NoSQL database. In MongoDB, tables are called collections, and records in collections are known as documents. MongoDB provides some extra-facility along with SQL facility i.e. why it comes under Not-only SQL (NoSQL)database.
We use different databases a/c to our use cases. Some of the use cases solved by MongoDB are listed below.
HSBC, one of the world leaders in the banking industry, has been focussing on the “Digital First” approach for quite a long time now.
Its main objective in this campaign is to improve data accessibility, insights, and data compatibility. Its data assets have gone on to double in the last five years.
HSBC had been using a relational database to store the operational database. It had limited the scope of data insights and data scalability.
With a huge increase in the database over the last five years, it had become more complex to manage the database easily.
HSBC wanted to implement a system wherein it could help its users to access data on simple terms like bonds and shares as well as complex terms like future trading predictions, derivative trading, etc.
Hence it partnered with MongoDB to fulfill these requirements.
This association has not only helped HSBC to achieve the objectives but also made the system simpler to access and faster at the same time.
2. WindyGrid project of Chicago city
WindyGrid project, which was inspired by a prototype made by one person on one laptop with MongoDB technology, focused on getting real-time insights from the Chicago municipality data.
WindyGrind project was one of the pioneers of the country in this field, and the project was executed within four months.
It does not just process the information on seven million different pieces of data, but it can also give insights into the things as they unfold.
This project is considered a vital step to make the city of Chicago safer and better with improved living standards.
WindyGrid deals with real-time updates on traffic delays, road works, mugging incidents, and public transport updates, etc.
Gathering all of these details is not just a time-consuming activity, but it is almost impossible to execute and analyze the results in real-time with relational databases.
But with MongoDB, the City of Chicago scaled up that was a mere prototype, working on a single laptop, and with one resource into a giant. It turned into a model that could analyze and predict the course of action based across the entire city.