Slides for my talk at PyData London 2016 on Building Data Pipeline with Python: http://pydata.org/london2016/schedule/presentation/7/
This talk discusses the process of building data pipelines, e.g. extraction, cleaning, integration, pre-processing of data, in general all the steps that are necessary to prepare your data for your data-driven product. In particular, the focus is on data plumbing and on the practice of going from prototype to production.
Starting from some common anti-patterns, we'll highlight the need for a workflow manager for any non-trivial project.
We'll discuss the case for Luigi as an interesting option to consider, and we'll consider where it fits in the bigger picture of deploying a data product.