Quix + Redis | Python stream processing, fast and easy

Back in January, we sat down with Tun Shwe, VP of Data at Quix, to discuss the challenges of real-time stream processing and how Quix aims to solve them. Tun delves into the company's origin story, emphasizing the importance of Python as Quix's primary programming language and how Quix streamlines the development process by handling infrastructure management, allowing devs to focus on their code.
Code changes fast and Quix's library and platform have changed a lot since then, so check it out now and learn what's new.
Try Quix: www.quix.io
Join Quix's Slack: ​​quix.io/slack-invite
Follow Quix on CZcams: ‪@QuixStreams‬
Star Quix's Repo: github.com/quixio/quix-streams
Try Redis too: redis.io
Join the Redis Discord: / discord
About Tun
Tun Shwe is the VP of Data at Quix, where he leads data strategy and developer relations. He is focused on helping companies imagine and implement their strategic data vision with stream processing at the forefront. He was previously a Head of Data and Data Engineer at high growth startups and has spent his career leading T-shaped teams in developing analytics platforms and data-intensive AI applications. In his spare time, Tun goes surfing, plays guitar and tends to his analogue cameras.
About Quix
Quix combines Quix Streams with Quix Cloud.
Quix Streams is an open-source library that makes stream processing simple and accessible to Python developers. It’s 100% pure Python (no Java required) and combines Docker with Apache Kafka so that you can easily build containerized real-time applications and pipelines. Its processing is based on Streaming DataFrames, a tabular format intuitive to Pandas and PySpark developers. Providing a high-level abstraction over Kafka, you get the power of a distributed system in a lightweight library that provides fault tolerance and scalability for common streaming tasks, e.g. downsampling, real-time alerting, and custom stateful window functions. You also get to seamlessly leverage the entire Python ecosystem, making it easy to build real-time machine learning and generative AI pipelines using LangChain, LLMs, and vector databases.
Quix Cloud is a fully managed serverless platform for building real-time data pipelines with Quix Streams and deploying them to production easily. It removes the complex setup and management of ETL components and unifies Kafka, Git, Docker, CI/CD, infrastructure provisioning, and monitoring. It has been built with care using engineering best practices learned from Formula 1 Racing, and you can bring your existing Kafka cluster or get your project started quickly using Quix’s managed Kafka with open-source connectors for Redis, MQTT, InfluxDB, Snowflake, and more.
0:00 Introductions
2:01 Tun's origin story
6:08 How'd you end up with Qux?
9:50 What's Quix & why do I care?
14:59 What makes Quix special?
18:41 Why doesn't this exist? (and a hot take)
24:55 What are users using Quix for?
30:47 Demo
48:28 Guy wants to play with Quix
50:46 What's next for Quix?
56:29 Final question

[@gacerioni]

GREAT session!
Thank you! 🙏🏻

[@csfSergo]

Thanks Guy. Really interesting guest.