The theory behind how a tool is supposed to work and the realities of putting it into practice are often at odds with each other. Learning the pitfalls and best practices from someone who has gained that knowledge the hard way can save you from wasted time and frustration. In this episode James Meickle discusses his recent experience building a new installation of Airflow. He points out the strengths, design flaws, and areas of improvement for the framework. He also describes the design patterns and workflows that his team has built to allow them to use Airflow as the basis of their data science platform.
There is a vast constellation of tools and platforms for processing and analyzing your data. In this episode Matthew Rocklin talks about how Dask fills the gap between a task oriented workflow tool and an in memory processing framework, and how it brings the power of Python to bear on the problem of big data.