Scaling Python Data Applications with Dask 1
Learn how to work with very large datasets without leaving familiar and rich Python data ecosystem. This course will teach you how to leverage power of Dask library in order to handle data that is too big for regular tools like Pandas or NumPy.
What you'll learn
Working with so-called ‘Big Data’ can be a daunting task and many tools that solve this problem have a very steep learning curve. Also, developers familiar with Python may not want to resort to solutions built on another technology stack. In this course, Scaling Python Data Applications with Dask 1, you will gain the ability to work with very large datasets using a Python-native and approachable tool. First, you will learn how to use Dask when your application written using standard Python stops working because of the growing size of the data. Next, you will discover how Dask works underneath and what techniques it uses to make processing large datasets in various scenarios possible and accessible. Finally, you will explore how to exchange Pandas and NumPy for their Big Data variants, with practically no changes to the code. When you’re finished with this course, you will have the skills and knowledge of Dask needed to confidently write data applications that scale, using exclusively Python stack.
Table of contents
About the author
Paweł is a software engineer passionate about knowledge sharing. He's especially focused on processing and exploring data sets (be it big or small) and is always searching for emerging tools that will make working with data simpler in the future. Paweł is currently at Farfetch where he develops data applications and sees his favorite tools like Pandas, Kafka or ElasticSearch (just to name a few) being applied to solve complex business problems. He mostly works with JVM languages and Python, but ... more