In our Parallel Processing course, you’ll learn how to improve the performance of your code by processing data in parallel rather that iterating through rows sequentially.
You'll learn how to start multiple processes and run functions on multiple processes at the same time, as well as share data between multiple processes. You'll learn how use a process pool executor, practicing all of these skills as you dig into some data about the demand for data engineering jobs.
Then you'll learn about MapReduce. You'll learn how to use process pools, how to actually implement MapReduce, and how to effectively process data with it.
At the end of the course, you'll complete a project using your new skills that challenges you to dig into data from Wikipedia pages and analyze them quickly and efficiently using MapReduce.
In this parallel processing course, you will:
Parallel Processing Course: Lessons
Introduction to Parallel Processing
Learn how to start and share data between multiple processes.
Process Pool Executors
Learn how to gather the results of a function that is executed on several processes.
Introduction to MapReduce
Learn how to implement MapReduce and use process pools.
Processing Data Quickly with MapReduce
Practice working with the MapReduce framework.
Guided Project: Analyzing Wikipedia Pages
Use threads and MapReduce to analyze Wikipedia pages more quickly.