Description

In this tutorial, you’ll learn how to speed up your data processing workflows by using parallel and distributed computing. Working with large datasets can be time-consuming, and parallel processing allows you to split tasks into smaller chunks and execute them concurrently, dramatically reducing processing time.

You will start by learning the basics of parallel computing in Python, using libraries like multiprocessing and joblib to perform simple parallel tasks. These libraries allow you to parallelize loops, apply functions across multiple processors, and manage tasks efficiently.

Next, the tutorial will cover distributed computing with frameworks like Dask and Apache Spark. These tools enable you to scale data processing across multiple machines, making them essential for handling large datasets that don’t fit into memory. You’ll learn how to set up a Dask or Spark cluster, manage distributed tasks, and process data in parallel across multiple nodes.

You’ll also explore parallelizing common data processing tasks, such as data loading, transformation, and aggregation. You’ll see how to split large datasets, process them concurrently, and combine the results. This is particularly useful when dealing with big data or running machine learning pipelines on large datasets.

In the hands-on section, you’ll work with a large dataset and apply parallel processing techniques to speed up tasks like data cleaning, feature extraction, and analysis.

By the end of this tutorial, you’ll have a solid understanding of parallel and distributed computing, enabling you to process large datasets efficiently and significantly reduce computation time.

You’ll also receive a Jupyter Notebook with code examples, a PDF summary of parallel processing techniques, and practice exercises to test your skills.