Imagine you manage an e-commerce platform that processes thousands of transactions daily. You want to analyze sales trends, track revenue growth, and forecast future income. Traditional database queries can’t handle this scale or speed. So you need a faster way to process large datasets and gain real-time insights.
Apache Spark lets you analyze massive volumes of data efficiently. In this tutorial, I’ll show you how to connect Django, MongoDB, and Apache Spark to analyze e-commerce transaction data.
What you’ll learn
You’ll learn how to set up a Django project with MongoDB as the database and store transaction data in it. Then, you’ll use PySpark, the Python API for Apache Spark, to read and filter the data. You’ll also perform basic calculations and save the processed data in MongoDB. Finally, you’ll display the processed data in your Django application.
Check it out:
https://www.datacamp.com/tutorial/how-to-integrate-apache-spark-with-django-and-mongodb
