Introduction to Modern Databases/

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Querying in NoSQL Systems

Querying in NoSQL Systems

Build your data analysis skills by mastering MongoDB’s Aggregation Pipeline and understanding when to use MapReduce.

We'll cover the following...

Imagine we are running a massive online store with millions of products and customer interactions happening every second.

While our database is excellent at storing all this information, how do we make sense of it? For example, how would we find out which product category generated the most revenue last month, or identify our top 10 most loyal customers based on their total spending?

Simple queries that fetch one document at a time won’t be enough.

We need more powerful tools to analyze, summarize, and transform our data, uncovering valuable insights. This is precisely where advanced querying in NoSQL systems comes into play. In this lesson, we will explore the powerful techniques used to perform complex data analysis in NoSQL databases.

By the end, we’ll be able to:

  • Understand the purpose and structure of the MongoDB Aggregation Pipeline.

  • Use common aggregation stages like $match, $group, and $sort to process data.

  • Explain the core concepts of the MapReduce paradigm.

  • Recognize when to use the Aggregation Pipeline versus MapReduce for data processing tasks.

The need for advanced querying

So far, we know how to perform basic CRUD operations. For example, using a method like db.products.find() in MongoDB is perfect for retrieving specific documents. However, when we need to answer bigger questions or perform calculations across a large number of documents, fetching everything and processing it in our application can be very slow and inefficient. It would be like trying to find the average height of a city’s population by having each person come to our office one by one.

This is why NoSQL databases provide powerful, built-in tools for data aggregation. These tools allow us to perform complex data processing directly within the database, which is much faster and more scalable. They let us summarize data, run calculations, and transform the structure of our documents to generate reports and insights. Let’s explore two key approaches ...