04-17, 15:45–16:15 (Europe/Berlin), B09
We evaluated time-series databases and complementary services to stream-process sensor data. In this talk, our evaluation will be presented. The final implementation will be shown, alongside python-tools we’ve built and lessons learned during the process.
Understanding time-series data is essential to handle automatically generated data, be it from server logs, IoT devices or any other continuous measurement.
In order to handle the large amounts of incoming data from concrete mixing trucks, we evaluated a number of time-series databases as well as services to stream-process the data. For all of those decisions a key question was, of course, how well any of these tools integrate with our existing, all-Python backend.
The right angle on time-series data will help you move tons of data with little engineering effort. In this talk, you’ll learn from our practical experiences of choosing and implementing a time-series database in a Python context. You’ll go away with a better understanding of how you can efficiently store, analyse and exploit streaming data.
Intermediate
Expected audience expertise: Python –Intermediate
Abstract as a tweet –A concrete guide to time-series databases with Python - how to choose the right time-series database for your application.
Heiner leads the truck-IoT effort at alcemy GmbH, where he's responsible for hard- and software. He holds a PhD in Physics and has a knack for building things that open a new dimension for their users.
