MongoDB Atlas Data analytics is an essential part of business intelligence, development, trend insights, and marketing strategy. An offshoot of this is analysis, which allows users to get useful insights from collected data. It’s a great way to make more informed decisions regardless of what industry you may be in.
Investopedia breaks down data analytics into descriptive, diagnostic, predictive, and prescriptive analytics. These systems permeate the Six Sigma program, travel industry, web development, and retail, among other spaces. Various techniques are used to process data for specific needs and trend monitoring and simulations. As businesses continue to digitalize, data analysis will become even more important for business optimization.
A popular option is to try MongoDB. The program is responsible for MongoDB Atlas, which is a cloud-based database service that handles all of the infrastructure for servers. Its smooth integration of different libraries and programming languages has made it very accessible for users of disparate skill levels. Plus, it has robust developer tools perfect for data analysis. So, how exactly does one use this to analyze data?
1. Creating and Configuring Your Cluster
The first step to analyzing data is to set the platform up to receive, manage, and go over your data sets. The benefit of making use of such a process is to ensure lossless data transmission regardless of the source. Thanks to the simple installation process, it shouldn’t be hard to get everything up and running for data analysis.
Assuming you already have a MongoDB account, you will need to set up defined users and teams under organizations. From there, you can establish projects. These will enable you to create and configure clusters, under which you will have environments for development, production, testing, and management.
Experienced developers may use a command line interface (CLI) to work with data on Atlas, but the platform does offer a graphical user interface (GUI) that is more forgiving for different levels of coding experience. Regardless of what interface you choose, you will have to create one of the following: shared cluster, dedicated cluster, or ‘multi-cloud & multi-region’ cluster.
It’s as simple as deploying the cluster, choosing the cloud provider, and connecting to the cluster. This is the starting point where you start collecting and inputting data. As you build your database, you will be able to analyze it using different commands.
2. Defining Your Schema
Now that you’ve got Atlas set up, you will want to define your schema. This will be dependent on your own definitions or can be taken from an existing source. One of the best things about Atlas is how it is flexible with how you can define the different fields of your schema. This is a crucial part of data analysis for MongoDB, as it allows developers to load different result sets from various types.
The exact commands you will use depend on the type of analysis you want to conduct as well as the type of items or data frames under your defined schema. MongoDB Atlas also supports the use of different schema within one collection. Furthermore, users can validate each schema within its own collection for more distinction.
3. Navigating Analytics
At this point, you will finally get to the point where you actually get to analyze the data that you’ve managed. A considerably “simple” solution is to do so by a direct query. The NoSQL nature of Atlas makes the process very fast, though this path does hinge heavily on the user’s own reference points. It also presents the data for further interpretation.
For more insights and analysis, users can also use data warehouses or data virtualization tools. Rockset happens to be one of the most often used real-time analytics tools that can be connected with MongoDB for consistently indexed data from large data-intensive apps. Rockset’s addition of vector embedding support also makes it easier to analyze unstructured, structured, and semi-structured data with a more comprehensive model.
A good way to get datasets for statistical analysis is to use R with Atlas. This will require R packages to be installed, namely mongolite, RMongo, and rmongodb. This can take on quite a workload, so it holds much potential as big data overwhelms the mainstream.
From there, you can use the Atlas UI to run an aggregation framework. With the rise of IoT production, automation, AI, and other revolutionary tech, MongoDB Atlas’ simple implementation can be useful for users looking to analyze data in a more accessible manner.
If you liked this article, why not take a read of our post on ‘Seamless Data Migration’.