May 30, 2023
Industry insights

The Stream — May 2023 edition

A monthly round-up of the most interesting news coming out of the stream processing ecosystem

The Stream May 2023 banner.
Real time data revolution Quix banner.

Join us in London on June 12th

As a part of London Tech Week, we're running a one-day side conference exploring how leading businesses are using the latest advances in real-time data platforms to unlock growth and retention despite a tough climate.

Learn more ->

Bridging the gap between data scientists and engineers in machine learning workflows

Two people on two sides of a cliff illustration.

Moving code from prototype to production can be tricky—especially for data scientists. There are many challenges in deploying code that needs to calculate features for ML models in real-time. I look at potential solutions to ease the friction.

Read the blog post ->

Quix Streams – Stream Processing with Kafka and Python

Python, Kafka, Quix, Flink logos.

We’ve featured many of Kai Waehner’s writings in this newsletter and it’s great to see Kai dedicate his latest post to the Open Source library Quix Streams (Github) and Python stream processing. He demonstrates with context and examples why pure Python is an excellent strategy for many ML use cases.

Read more on Kai's blog ->

More news and insights

  • Quix has launched a new developer forum! It's a place to ask and find questions about our OSS library and our Python streaming platform  - Learn more
  • Early adopters of data streaming see 10x returns - Read more
  • Apache Flink awarded the prestigious SIGMOD Systems Award - Read more
  • Last month in San Francisco the inaugural Real Time Streaming conference took place. Quix’s CTO, Tomas Neubauer, presented Quix Streams and how to build real-time applications with Python and Kafka - Watch now
  • Tomas also spoke with Jesse Anderson at length to discuss the challenges data/ML engineering face and how Python stream processing can address this - Listen on Youtube
  • Hubert Dulay's "Streaming Data Mesh" book is out now - Read more
  • Twitter thread of Confluent CEO Jay Kreps’ keynote at Kafka Summit London. We’re excited to see that queueing support is being added to Kafka and learned that the number of monthly active unique users is close to 1 million - Read more

Meme of the Month

Person on a red car waving from the window meme.

What’s a Rich Text element?

The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

Static and dynamic content editing

A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!

How to customize formatting for each rich text

Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.

Related content

Banner image for the article "Streaming ETL 101" published on the Quix blog
Industry insights

Streaming ETL 101

Read about the fundamentals of streaming ETL: what it is, how it works and how it compares to batch ETL. Discover streaming ETL technologies, architectures and use cases.
Tun Shwe
Words by
LLMOps: large language models in production with Quix
Industry insights

LLMOps: running large language models in production

LLMOps is a considered, well structured response to the hurdles that come with building, managing and scaling apps reliant on large language models. From data preparation, through model fine tuning, to finding ways to improve model performance, here is an overview of the LLM lifecycle and LLMOps best practices.
Tun Shwe
Words by
What is stream processing
Industry insights

What is stream processing?

An overview of stream processing: core concepts, use cases enabled, what challenges stream processing presents, and what the future looks like as AI starts playing a bigger role in how we process and analyze streaming data
Tun Shwe
Words by