Stream Processing Meetup at Trade Republic

Join us on Tuesday, May 9th for an evening of talks and conversations about Stream Processing. We're hosted by the good people at Trade Republic. Space is limited so sign up in good time! Note you'll need to bring an ID to Trade Republic for access.

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Stream Processing at Trade Republic. My Journey to Hell and Back - Amsterdam Lima, Senior Backend Engineer at Trade Republic

In this talk, Amsterdam Lima will share his personal journey of grappling with stream processing. He'll reveal the challenges he faced and the numerous mistakes made in the hope to help software engineers new to building real-time data applications.

Amsterdam works in the Market Data team at Trade Republic. They process the price feeds and enable responsive data flow to help their customers to make real-time trading decisions.

Simplifying Real-Time ML Pipelines with Quix Streams: An Open Source Python Library for ML Engineers - Tomas Neubauer, CTO Quix

As data volume and velocity continue to increase, the need for real-time machine learning (ML) is becoming more pressing. However, building real-time ML pipelines can be complex and time-consuming, requiring expertise in both ML and streaming application development.

This talk will address this problem by introducing Quix Streams (https://github.com/quixio/quix-streams), an open-source Python library that makes it easy for data scientists and ML engineers to build real-time ML pipelines without having to learn the intricacies of building a streaming application from scratch.

In this talk, we’ll cover:
- The growing importance of real-time ML in today's application stack, and the use cases for real-time ML processing.
- A comparison of different ML architectures (batch, request-response, stream, and hybrid) and their pros and cons
- The current state of streaming architecture, which is typically Java-based, and the challenges this poses for data scientists and ML engineers who primarily work in Python
- An overview of Quix Streams and its features, including a demo of how to use it to build real-time ML pipelines

This talk is relevant for data scientists, ML engineers, and software engineers who are looking to adopt new technologies and practices in order to build real-time ML pipelines and stay current in their field.

Analytical streaming queries in distributed systems - Jan Mensch

Are you tired of not understanding how your streaming system works under the hood? Did you ever wonder what happens to the events that you feed into your streaming processor? If so then this talk is for you!

In this presentation, I'll take you on a journey into the world of analytical streaming data processing, where we'll explore how to process data in real time.

You'll learn:
- How streaming data processing works in distributed systems
- How incremental updates to materialized views work under the hood
- Additional tips and approaches to build resilient and scalable streaming data processing systems. I'll demonstrate these examples using RisingWave (https://github.com/risingwavelabs/risingwave).

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If you're interested in speaking at or hosting a meetup, get in touch - theo@quix.io

This meetup is sponsored by Quix, a platform for building real-time applications and the company behind Quix Streams, an OSS library for telemetry streaming - https://github.com/quixio/quix-streams