If NoSQL is the proving out to be the future king of database technologies, then MongoDB is the jewel on the crown. With above 15 million downloads in a short time since its release, MongoDB is one of the top NoSQL database technologies of our times, which empowers the users to manipulate effectively, query, and gain interesting insights through analytics from big data.
In this excerpt, we will discuss a few essential things users with database administration needs to know about NoSQL database technologies and how MongoDB is making a difference? Apart from data storage and upkeep, MongoDB also makes the job of data scientists’ quicker and easier.
The key takeaways of NoSQL
- The increasing popularity of NoSQL DBMS over the last decade is primarily because it allows the users to make a query on unstructured data without any knowledge of SQL.
- MongoDB has grown largely from the level of being a simple JSON data store to now the most popular NoSQL database solution. This DB offers exceptional manipulation and administration features in terms of data.
- The aggregation and sharding frameworks of MongoDB while combined with its document validations capabilities and fine-tuned locking, make it a wonderful solution for DBAs. This broader ecosystem of database tools and highly vibrant community of the users are the reasons why MongoDB remains as a reliable database for many.
- The major challenges in database administration as data structuring, security, backup, etc. can be effectively addressed by MongoDB usage. Mastering MongoDB focused on addressing such pain points to build a robust and scalable database solution.
The reason for the popularity of NoSQL
There are many factors now contributing to the popularity of the NoSQL DB. The conventional relational databases are out there for 30 years now. At some point, especially with the exponential growth in data volume, we have realized that the one-fits-all type of database models is not applicable in many cases. While information technology is eating out the world, the breadth and diversity of the use cases where we use software applications had grown to an unprecedented volume.
We have seen then seen graph databases, column-wise databases, and also now the document-oriented NoSQL databases as MongoDB, which brought up new solutions to the long-standing database problems. If our enterprise problems can be resolved with document-oriented data structures, then it will be sensible to use the right database tools like MongoDB than taking the one-size-fit type RDBMS.
The growth of generated data
Over the past few years, we have also seen that there was an exponential growth in terms of data. More than 80% of the data from across the globe had been generated in the last three years, and this will grow soon with the invent of IoT. All these data should be stored appropriately and critically analyzed to derive more insights and strategies.
The apt solution to handle this data is to effectively separate analytical loads from the into corresponding OLAP and OLTP databases. In the big data ecosystem, Hadoop offers various frameworks which can effectively store and do a parallel analysis of data. The primary problem in data warehouses and data lakes in Hadoop is however multifold as you may need more experts to analyze the data and need expensive methods to derive solutions.
In the modern database management world, MongoDB will help bridge this gap by ensuring excellent analytics capabilities. This database can help the developers and administrators to get some quick data-centric insights and ad also define the directions for the data scientists to work. By using proper tools like the BI connector or charts, MongoDB and concept of data warehousing and now converging.
MongoDB will not be substituting the Hadoop-based ecosystem, but it will effectively compliment it by reducing the time to market for the data-centric solutions. MongoDB is out there from 2009 onwards. Over time, MongoDB got matured as a robust database to handle wider sets of use cases. However, there had been many features which didn’t end up as they were originally planned for. An example to point out is the MapReduce framework of MongoDB, which didn’t live up to expectations and overtime got superseded by some other advanced Aggregation frameworks.
The striking features of MongoDB
When it comes to day to day developmental scenario, the Aggregation framework helps to prototype the data pipelines quickly which can convert data into a format, which can be easily collaborated with the data scientists. This approach will help them derive some actionable insights in minimal time.
The primary need from any technology is to get a reliable solution to better to achieve the business goals. In terms of usage of MongoDB, one can store the data easily in JSON format to process and analyze it. This can be easily transferred to many other front-end and backend systems too without much difficulty.
The major difficulties data architects and developers now face with Mongo DB is that, whether they come from a relational database management practice or NoSQL background, it a difficult task to design a database which can solve all the current and future issues in hand. Another big challenge is security and backup of data, which DBAs face so often. Backups seem to be ignored many times.
Considering the above issues, MongoDB has not commanded a major market share in the NoSQL database sector, as it is being identified as a more comprehensive and user-friendly solution to many of these issues.