Simple Data Engineering in python 3.5+ with Bonobo
Developer, sysadmin, technical team builder, founder of two companies), advisor.
Currently helping start-ups to achieve more with less in our acceleration programs in Paris, and in charge of our product development activities.
Sometimes, I play go and make music, but not at the same time. </div>
Tags: Python Business Data-Engineering ETL Simple Bonobo
Simple is better than complex, and that's True for data pipelines, too.
Bonobo is a python 3.5+ tool used to write and monitor data pipelines. It’s plain, simple, modern, and atomic python.
This talk is a practical encounter, from zero to a complete data pipeline.
Spoiler : no «big data» here.
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.
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