October 27, 2021
Industry insights

The Stream — October 2021 edition

The October 2021 edition of The Stream: covering this month in stream processing on the internet.

The Stream October 2021 banner.
Quix Streams combines an Apache Kafka client with a stream processing library, offering a feature-rich pure Python alternative to the kafka-python library. Deploy on your stack or on Quix Cloud for scalable, stateful and fault-tolerant ETL without the headache of cross-language debugging.

👉 Star the repo on GitHub and pip install quixstreams to get started.

You’re reading the first edition of Quix’s monthly newsletter, designed to catch you up on our best content and product updates from the prior month. If you’d like to subscribe, scroll to the bottom of this page and sign up. We’d rather write software than emails, so we promise we’ll never spam you.

Our team has been hard at work through October, partnering with customers to deliver some great new use cases. Watch our blog (or subscribe) as we share more customer stories and in-depth tutorials.

Some of these seem impossible without a massive team of data and infrastructure specialists. Before Quix, that was the challenge for many of our customers. We break down the steps to show you how to deploy a machine learning model to production in as little as 15 minutes. We share architectural diagrams and how we run Quix on Quix to deliver a better pricing model than subscriptions and contracts: usage-based billing.

We’d love to hear from you, our community, on how you’re using Quix — whether for your day job or a passion project. Come on over to Slack to chat with us. And until next month, happy streaming!

Quix, Flink, Spark icons.

Quix outperforms Spark and Flink in stream processing test

We compared client libraries — find out why Quix delivered 50x greater performance than incumbents.

See the results

Laptop showing Quix logo on top.

Take Quix for a test drive

See stream processing in action, powered by your mobile device in our no-code game/demo.

Drive the gameo →

Streaming data hard to handle scheme.

Why is streaming data so hard to handle?

CTO Tomas breaks it down: stream processing is not for the faint of heart or thin of wallet.

But there’s hope →

Close-up of a bunch of neon lights in blue and purple.

Use case: Cybersecurity

How data stream processing, together with machine learning or AI, can be applied to detect cyberthreats in real time.

Stop, thief! →

More insights

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

Featured image for the "Navigating stateful stream processing" post published on the Quix blog
Industry insights

Navigating stateful stream processing

Discover what sets stateful stream processing apart from stateless processing and read about its related concepts, challenges and use cases.
Tim Sawicki
Words by
windowing in stream processing
Industry insights

A guide to windowing in stream processing

Explore streaming windows (including tumbling, sliding and hopping windows) and learn about windowing benefits, use cases and technologies.
Daniil Gusev
Words by
real time feature engineering architecture diagram
Industry insights

What is real-time feature engineering?

Pre-computing features for real-time machine learning reduces the precision of the insights you can draw from data streams. In this guide, we'll look at what real-time feature engineering is and show you a simple example of how you can do it yourself.
Tun Shwe
Words by