At the initial stage of its development, MongoDB was considered as a simple database which aims at the quick implementation of the database by building from scratch. On the go, it took the format of the JSON document in the cloud set-up to handle complex and unstructured data types also effectively. Unlike the need of the relational databases to squeeze data into rows and columns, these new age data structures brought in a revolutionary change in data management in the time of big data and live data streaming.
At this time, this flexibility and simplicity made MongoDB one of the tops among the most popular new-generation DB-engines. However, this simplicity of the platform itself proved out to be one of the biggest hurdles of low performance. There came other NoSQL DBMS platforms like HBase, Redis, and Couchbase, etc. which featured quicker writer as their strength. Even though these issues were effectively addressed overtime, MongoDB had to further grow competently to response to the changing needs of the audience, including the practitioners and the end-users as well.
With this exponential growth of audience and their diversified needs, MongoDB is not moving from the simple user points of view to creating goal-specific apps. With this, they are striving to cater to the needs of enterprise-standard cloud, security terms, SLAs to get adapted to the new modes of data querying. The year 2018 was proved out to be a year of metamorphosis for databases like MongoDB.
To enhance the database writing and reading performance, MongoDB adopted the MySQL model itself and also its storage engine as Wired Tiger. This new composition made MongoDB more acceptable for the management teams with the advantage of in-memory and independent data stores. In continuation of the changes that happened to this platform last year, now we can see that this suite is further expanding with a lot of inclusions to be expected in the year 2019 also.
Other capacities MongoDB plans to include in its architecture
Expert prediction is that MongoDB will be focusing more on enhancing its analytical performance to meet up with the increasing demand for business intelligence through data analytics. For this, it adopts the Spark connector which can effectively handle huge volume data analytics tasks. A columnar data storage method can also be possible as the data streaming through the data engine will be made available.
However, the RemoteDBA.com experts point out the fact that the root of MongoDB is still based on the erstwhile open-core methodology. But it has lately become very intricate in terms of value-added entities which can be accessed only through subscription. These functional features include extra in-memory, management console, storage engines (encrypted), business intelligence connectivity, database administrator tools, access control, and authentication options.
Integrating cloud into business
When it comes to database administration, the evolutionary steps now are towards integrating cloud database into business. Even though companies can mount MongoDB into cloud easily to the infrastructures of AWS, Azure, Google Cloud, etc., it needs to be managed by one’s own. This means someone needs to get a third-party application of MongoDB by considering Database as a Service. However, now MongoDB launching its own DBaaS, which is named as Atlas in competitive pricing to make things easier for the users. The features of Atlas are expected to be wider replication capabilities and better disaster recovery solutions.
The entire database market is expanding
This revolution in the database management sector is not however confined to MongoDB. The whole database market witnesses a shift to platform-centric applications. Say, for example, the industry giants like Oracle, Teradata, DB2, and even SQL Server introduced columnar, in-memory, graphics, and geospatial capacities. These capabilities are achieved through a query language extension or by grafting new storage engines.
In response to this move by the other NoSQL opponents like Hadoop, MongoDB also launched many new options for data protection and visualization. MongoDB is now well known for its capability to read and interpret complex data. As of now, MongoDB is more focused on a new set of live workloads like real-time operational data. This database platform is thereby undergoing a complete evolution, but not by forgetting its breeding ground, which is a developer-centric platform for managing application handling document-style data.
There is also increasing pressure for data applications to react to the changes instantly. As an add-on feature to latest MongoDB version, change streams will enable applications to do live streaming of data by leveraging its replication abilities. You can consider the need for the trading apps to get updated in real-time by reacting to the changing stock prices every minute. Another instance is the creation of IoT-centric data like generating alerts whenever a vehicle connected to IoT moves outside of a geo-fenced location. This requires complex updates of dashboards, search engines, and analytics systems as the operational data change.
All these can be effectively taken care of by change streams, which can notify the applications of all the new writes and deletes of documents and provide users access to such info as soon as the change occurs without incurring a higher workload. Thus, MongoDB also will ensure no miss of any opportunities at real-time.
The future of MongoDB
The introduction of Atlas is now helping MongoDB to retain the clients from the development further to the deployment phase, which is more so for those who adopt cloud solutions. The popular tools like Tableau and MicroStrategy, which can run through the BI connectors will enable MongoDB to handle the analytic workload too.
It is also expected that MongoDB will add on the option for federated queries too by adding their own front-end aggregation framework. This will help pull down query processing to data location. The major move among all these owns the query. This will strengthen MongoDB to compete with the opponents in business intelligence and analytics like IBM, Microsoft, Oracle, and Teradata, etc. In fact, they are also vulnerable in case of querying. MongoDB may not turn into a full data warehouse but remain as a platform by getting strengthened with the extension of operational apps which would stay on the native platform through embedding analytics.