Simple Data Engineering in python 3.5+ with Bonobo

Simple is better than complex, right? That’s true for data pipelines too. For the last 5 years, I hacked together extract-transform-load (ETL) processes in various different positions (ETL is just a fancy term for «bunch of things that take data somewhere and put it elsewhere, eventually transformed»). I did it as a founder, as a consultant, as a technical co-founder, for some side projects, big corporates and small side projects. In each case, I felt frustrated with the tools available, and in some serious cases, I had to hack things myself to get the job done. Bonobo is the repackaging of my past experiences for python 3.5+, and grasping the basics should not take more than the length of the presentation. Outline (subject to small changes, for the greater good) : INTRO : The ETL market, why a new tool, what it is, what it is not. Basics and concepts. Simple example. Complete data pipeline example, using SQL, RDF and a small Django frontend. OUTRO : A glimpse at the future. Q&A Bonobo is the glue you need to tie together regular functions in a transformation graph (think unix pipes). Execution strategies are abstracted so you can focus on the real operations. As a result, you can engineer simple and testable systems, using the same good computer development practices as you use in .