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MongoDB ranked third among the most loved databases in Stackoverflow’s 2022 analysis. The other two databases have been the leaders in database technologies for many years now: PostgreSQL and Redis. However, in the ranking of No-SQL databases, MongoDB is the one that is the most popular choice.
What are NoSQL databases? Often called “non-relational,” they can handle vast amounts of rapidly changing, unstructured data. Although known since the 1960s, they are becoming increasingly popular. This is all due to the massive amounts of data generated by the Internet, social media, and mobile devices. NoSQL databases make it easy to create systems to store new, unpredictable information quickly.
What is MongoDB?
MongoDB is an example of a non-relational database capable of storing large amounts of data. Unlike traditional relational databases that use tables and rows, MongoDB uses “collections” and “documents”. MongoDB is widely used in many companies and is one of the most powerful NoSQL databases on the market. Here are some main characteristics of MongoDB’s architecture:
- Each document is a collection of “key-value” pairs.
- Each “key-value” pair is called a field.
- Each document has a field_id, which is a unique identifier for that document.
- It is permissible to nest a document within a document.
- Documents can have a different number of fields (they can also be empty).
- A group of documents is a “collection.”
What makes the MongoDB database so well-liked?
First of all, it is great for Data Science and Machine Learning purposes. Why? Here are 5 key reasons:
Reason #1: Flexible data model
MongoDB stores documents in BSON (JSON-like format), which allows objects with different sets of fields to be in one collection. A very simple example is documents describing different users – when some users have a second name, there is no reason to store an empty field for users who do not have a second name. MongoDB’s document model allows for easy modeling and manipulation of almost any data structure.
MongoDB allows data validation and schema modification without downtime or lack of access to the database. This flexibility is an incredible asset when handling real-world data and changing requirements, or environments.
Reason #2: Powerful query language.
MongoDB allows you to query deep into documents and even perform complex analytical pipelines with just a few lines of declarative code. You have the option to filter, sort, and aggregate the data, selecting and transforming the fields you need to use. This is an essential step for preparing the data used for machine learning. This level of query sophistication is not available in most NoSQL databases.
Reason #3: Easily store and retrieve trained predictive models as JSON-type documents.
MongoDB is an ideal place to store, share and retrieve trained models. It is also possible to store historical versions of models in the database, allowing you to easily restore an archived model if you choose to do so.
Reason #4: Full “data platform” in the cloud.
MongoDB is much more than a database – it is a complete “data platform”. MongoDB Atlas is the cloud offered by MongoDB. It gives you access to many services that all integrate nicely with your database i.e. recommendations for optimizing your database, or an interface for creating reports and visualizations. What’s more, running MongoDB is almost seamless, whether you’re using a single set of replicas or a fragmented cluster containing hundreds of terabytes. MongoDB Atlas allows you to maintain high performance and horizontal scalability for your database.
Reason #5: Access from within Python.
The constant development of technology forces us to constantly update our knowledge and improve our professional skills. For the role of Data Scientist, it is worth having a basic knowledge of databases. MongoDB, used in many companies as one of the main databases, is a good start to expand your knowledge of database technologies.
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About the author
AI Engineer | Microsoft Certified Azure Data Engineer