06-15, 16:30–17:10 (Europe/London), Minories
Event stream processing enables real-time analytics and decision-making, which is crucial in
financial services, healthcare, manufacturing, and more industries. However, real-time stream
processing also presents various challenges due to the complexity of systems and new
paradigms involved. This talk delves into the event stream processing landscape and potential
roadblocks in implementing real-time event streaming and discusses the fundamentals of
building streaming applications with a real-world example.
Streaming data empowers organizations to analyze data in real-time, optimize operations,
mitigate risks, and deliver exceptional customer experiences via personalized interactions and
immediate responses to changing scenarios, making it widely applicable to industries and
applications like healthcare, financial services, and earth and climate sciences. Yet, the journey
from raw data to actionable insights is riddled with challenges, particularly in building reliable,
production-quality systems.
In this talk, we’ll explore the current stream processing landscape, discuss elements of an
effective event streaming system, and walk attendees through the stages of building an app
using Python, familiar PyData tools, and a recently open-sourced library CSP (Composable
Stream Processing).
Through this talk, attendees will get a better understanding of:
* Event stream processing and the current landscape of tools and techniques
* Challenges in developing applications like offline simulation, varied infrastructure, and
performance
* How to develop, test, and deploy a stream processing application on any infrastructure
(with a real example)
Practicing data scientists and data engineers will benefit the most from this talk. That said, the
talk is designed to be accessible and valuable for the broader community looking to understand
stream processing and build interesting applications.
You can follow along comfortably if you have a beginner-intermediate knowledge of Python for
data science. Familiarity with declarative programming and stream processing is a nice bonus.
Previous knowledge expected
Adam Glustein is a Quantitative Developer at Point72 Asset Management in New York, USA. He is a contributor to CSP, a reactive stream processing library for both realtime and historical data.