Architecture Big Data Data Distributed Systems Scaling
This talk will focus on how one can build complex data pipelines in Python. I will introduce Luigi and show how it solves problems while running multiple chain of batch jobs like dependency resolution, workflow management, visualisation, failure handling etc.
After that, I will present how to package Luigi pipelines as Docker image for easier testing and deployment. Finally, I will go through way to deploy them on Kubernetes cluster, thus making it possible to scale Big Data pipelines on-demand and reduce infrastructure costs. I will also give tips and tricks to make Luigi Scheduler play well with Kubernetes batch execution feature.
This talk will be accompanied by demo project. It will be very beneficial for audience who have some experience in running batch jobs (not necessarily in Python), typically people who work in Big Data sphere like data scientists, BI devs, data engineers and software developers. Familiarity with Python is helpful but not needed.
Type: Talk (30 mins); Python level: Beginner; Domain level: Intermediate
I work as Data Engineer at Breuninger.com, where my team is responsible for data lake/platform. We use Python/Luigi for data processing and scale our workloads on Google Kubernetes Engine.
I have many years of software development experience ranging from web services, backend systems, CMSs and data platforms.