The Quix blog

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.

Learn how to reprocess a stream of data with the Quix Streams Python library and Apache Kafka. You'll ingest some GPS telemetry data into a topic and replay the stream to try out different distance calculation methods.

Explore the differences between Quix and Apache Flink and find out when it's better to use Quix as a Flink alternative. If you’re searching for Apache Flink alternatives, this guide offers a detailed, fair comparison to help you make an informed decision.

A comparison of Apache Kafka & Amazon Kinesis covering operational attributes, pricing and time to production, highlighting their key differences and use cases

How Brompton's experiments with Quix and AWS technology are paving the way for an enhanced e-bike riding experience.

Learn why data compression is vital and how use it with Kafka and kafka-python, focussing on gzip—one of the strongest compression tools that Kafka supports.

Lightweight, powerful, no JVM and no need for separate clusters of orchestrators. Here’s a look at our next-gen streaming library for C# and Python developers including feature summaries, code samples, and a sneak peek into our roadmap.

Build a simple event-driven system to get ML predictions with Python and Apache Kafka

Use the Quix Streams Python library to continuously stream email records from a CSV file, get an ML inference for each record, then stream the results back to a new Kafka topic.

Use Python to process time series data and send it to Apache Kafka. You'll use a large sample data set from an online-retailer and send the records in batches with Pandas and the kafka-python library.

Why we've partnered with MMC Ventures, Project A Ventures and Passion Capital to accelerate our growth.

Create a CDC pipeline and publish data to Kafka topics in just a few minutes with our open source SQL Server connector.

As the internet of things evolves, so does its application in industrial settings. Find out how to take Industrial IoT from a helpful tool to transformative technology with streaming data integrations.

If your last IoT project wasn’t wholly successful or you’d like to progress along the learning curve faster, we’ve got you covered.

We’ve got your introduction to the modern data stack and why you might want to invest in one.

It turns out that “real time” is a bit of a misnomer — there’s always some lag between an event and its processing — even if it’s only a few milliseconds. But we can help decrease even that item.

The internet of things has expanded from small personal devices to warehouses and factories. This post will look at how IIoT impacts various industries and how to start or accelerate your transformation.

Most data in streaming applications such as IoT, finance, user behavior analysis and automotive is time-series data. Learn how to capture, process and apply it to get the most value from it.

Bring any type of data into the Quix platform. Here’s how to use the APIs when the SDK doesn’t fit.

When, why, and how to contribute code samples to the Quix library.

When, why, and how to contribute code samples to the Quix library.

Learn what you can do with Quix Streams, the Quix SDK, and why we dedicated more than two years building it.

Ademen aims to empower community clinical teams to screen for and monitor health problems using the smart stethoscope it built on streaming data with Quix.

Behind the scenes building McLaren’s F1 stream processing system, and why the world needed a new stream processing client library.