This chapter is about getting a bird's-eye view of the Nobel Prize data's structure. You will relate MongoDB documents, collections, and databases to JSON and Python types. You'll then use filters, operators, and dot notation to explore substructure.
Now you have a sense of the data's structure. This chapter is about dipping your toes into the pools of values for various fields. You'll collect distinct values, test for membership in sets, and match values to patterns.
You can now query collections with ease and collect documents to examine and analyze with Python. But this process is sometimes slow and onerous for large collections and documents. This chapter is about various ways to speed up and simplify that process.
You've used projection, sorting, indexing, and limits to speed up data fetching. But there are still annoying performance bottlenecks in your analysis pipelines. You still need to fetch a ton of data. Thus, network bandwidth and downstream processing and memory capacity still impact performance. This chapter is about using MongoDB to perform aggregations for you on the server.