Extract Customer Insights with MongoDB Queries

Extract Customer Insights with MongoDB Queries

In this lab, you will explore how to extract customer insights using MongoDB queries. You will practice filtering, projecting, sorting, and aggregating data to identify high-value customers, segment user behavior, and analyze purchasing patterns.

You will write MongoDB code inside Python using the pymongo package. You do not need to know the Python syntax to complete this lab.

MongoDB methods:

In this lab, you will use the following MongoDB methods, along with many other operators:

1. find()
2. sort()
3. limit()

Database

You will be working with the customerdb database, specifically the customers collection. You will access this collection through a read-only user account.

The collection contains seven fields, though some of them may be missing in certain documents, as outlined below:

1. first_name: 12 documents
2. last_name: 12 documents
3. email: 12 documents
4. country: 12 documents
5. active: 12 documents
6. total_orders: 10 documents
7. last_purchase: 11 documents

Let's go! 🚀

In the first step, you will connect to the database and perform filtering operations. You will work in the src/step1.py file to perform three tasks. Congratulations! You have completed the first of five steps in this lab. Be sure to run client.close() at the end of the script to close the MongoDB connection and free up resources.

Let's move to the next step. 🚶‍➡️

Not everyone belongs to a country, city, or a building. There are multiple parameters that you can associate with a user. Dealing with these parameters require the use of logical operators like AND, NOR, GREATER THAN, etc.

In this step, you will learn how to work with logical operators in MongoDB. To implement them, you will work in the src/step2.py file.

You must run the script using python3 src/step2.py in the Terminal.

Previously, you explored how to filter documents using logical operators and conditions. In this step, you will focus on limiting the fields in scope before applying any logical or conditional logic.

You will work in the src/step3.py file and you must use the python3 src/step3.py command in the Terminal to call the script functions.

Sorting is essential in MongoDB queries when the order of results matters. For example, displaying the most recent purchases, top customers by order count, or alphabetical lists.

In this step, you will learn how to sort documents based on a single or multiple fields. You will work in the src/step4.py file and must use the python3 src/step4.py command to run the script in the Terminal.

Limiting the number of documents in a query helps reduce memory usage and improves performance, especially when dealing with large datasets.

In this final step, you will learn how to limit documents directly in a query and after applying operations like sorting or filtering.

For this task, you will work in the src/step5.py file and run the functions in the Terminal using the command: python3 src/step5.py. Congratulation! 🥳🎉 You have completed all five lab steps.

In this lab, you learned how to connect to a collection, filter documents using logical operators, project specific fields, sort results, and limit the number of documents returned.

You can now use these techniques to extract meaningful customer insights from your data.