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February 15, 2022
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Industry insights

The Stream — February 2022 edition

The February 2022 edition of The Stream: covering this month in stream processing on the internet.

The Stream February 2022 banner.

Python stream processing, simplified

Pure Python. No JVM. No wrappers. No cross-language debugging. Use streaming DataFrames and the whole Python ecosystem to build stream processing applications.

Python stream processing, simplified

Pure Python. No JVM. No wrappers. No cross-language debugging. Use streaming DataFrames and the whole Python ecosystem to build stream processing applications.

Data integration, simplified

Ingest, pre-process and load high volumes of data into any database, lake or warehouse, without overloading your systems or budgets.

Guide to the Event-Driven, Event Streaming Stack

Practical insights into event-driven technologies for developers and software architects
Get the guide
Quix is a performant, general-purpose processing framework for streaming data. Build real-time AI applications and analytics systems in fewer lines of code using DataFrames with stateful operators and run it anywhere Python is installed.

A roundup of our best content and product updates

The best proof of Quix’s product, besides trying it yourself, is the inside story of how one of our customers solved a major business problem. In this month’s edition of our newsletter, we highlight two major case studies.

First, how did a race-winning telemetry company use Quix to increase performance and resiliency? This case study features our customer Control, which was able to reduce hands-on time for its engineers while ensuring peak connectivity from anywhere on the racetrack. I think the most impressive aspect of this use case is the fact that we delivered 82 machine learning models in just two weeks’ time.

Second, take a deep dive into the business strategy behind usage-based billing, which delivers maximum value for both customers and companies. It’s no wonder this pricing model is rapidly replacing subscription models in SaaS and beyond. My colleague Patrick Mira Pedrol, our Head of Software, invested months in this technical project for Quix and gives you the all-access tour in his deeply technical post.

But maybe I’m getting ahead of myself. You might be wondering about this whole data stream processing revolution, too. And truth be told, it can be a bit scary — especially since it was once solely the domain of big tech companies and their army of infrastructure engineers.

So in addition to case studies, we’re reporting on some fantastic use cases in stream processing (regardless of whether they use Quix to make it easier). Stream processing helps organizations:

Here’s a sample of the real business gains these organizations reported:

Real time business gains.

Enjoy this month’s newsletter with highlights of our top content. And if you’ve got a use case, let’s talk. Book a consultation to chat with us about your project.

Multiple open windows on desktop.

Pay as you grow

An insider’s guide to building a bulletproof usage-based billing system. Get the business strategy and the deep technical details of this project in a two-part series

How we built it

White racing car.

Two weeks, 82 ML models, one goal: performance

See how Quix customer Control delivers on its race-winning telemetry promise, in an intriguing case study that significantly reduced hands-on engineering time.

The network effect →

Customer centric data illustration.

How to take a customer-centric approach to data

Three ways that data stream processing helps organizations deliver better customer experiences, from greater responsiveness to product feedback loops.

For the customer-obsessed →

Future modern data stack conference banner.

The future of the modern data stack

Just as cloud replaced on-prem, data processing is moving from static to highly efficient stream processing. See highlights from a recent conference panel.

Ask the experts →

More insights

  • Three architectural diagrams (absolutely not simplified) for modern data infrastructure, from the folx and A16Z.
  • What’s your take on Kappa Architecture vs. Lambda? Here’s Confluent CTO Kai Wehner’s approach.
  • Explore the Periodic Table of Realtime from Ably — an interactive way to learn about various players and protocols.
  • Our team is growing, and it’s great! We welcomed frontend engineer Chris Gilchrist, head of technical content Kiersten Thamm, and community manager Theo England to the team.

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