Mongo DB is used in our migration work: we pull data from the Facebook API, load it into Mongo collections, and then move that Mongo data into BigQuery. The best part about Mongo is that we can handle most of the major transformations directly within the Mongo collections before loading it onward.

What I like best about MongoDB Compass is how it makes working with MongoDB data much more visual and intuitive. Instead of relying entirely on queries, I can quickly explore collections, inspect documents, and understand data structures through the UI. Schema analysis helps understand document structures and field distributions quickly. Query building is straightforward, making it easier to validate data without memorizing complex syntax. Document filtering and searching are fast, even when investigating production issues. Index visualization provides insight into query performance and optimization opportunities. The ability to view and edit documents directly simplifies debugging and data verification.

I like MongoDB for its data-based storage and schema flexibility. It allows me to store data in non-table formats and handle any kind of data, unlike SQL databases where I had to stick with a predefined schema. The horizontal scaling feature and the easy learning curve also make MongoDB valuable to me. The initial setup of MongoDB was very easy for my team.