Data engineering

Don’t dump data. Build better pipelines.

Data warehouses are where good decisions go to die. Reinvent your data pipelines with the power of stream processing — extracting value from data the moment it is created.

Person looking at two screens in the dark.

What can you build with Quix?


Extract, Transform and Load — this process dumps valuable data in a lake. Dump, pull, transform makes data difficult to manage, duplicative and unreliable. Modernize this process by handling data as it is created (in a stream), processing it in-memory, and keeping only clean, orderly data. Future users will thank you.

Change data capture

You need to know when data changes at the source so you can take action — whether to modify a database, automate new responses or change the way you process it. Using ELT to sync data stores doesn’t cut it. But with stream processing, you’ll immediately and reliably recognize data changes and be ready to act on new variables.

Smart caching

Much of the data stored in warehouses is hidden deep in the cloud — so logging into your bank for a transaction history will take time to query, retrieve and serve. Why not do it better? Recognize customers and use ML to predict what that customer is likely to query. Then selectively pre-load data to serve it immediately, reducing latency.

Stream analytics

Build better business intelligence dashboards using the streaming infrastructure. Aggregate data from any source, layer on business logic, then easily query to generate high-value reports. Now you can report on-demand or on any schedule, so reporting is always current in real time. This automation regardless of when or how often data arrives.

Machine learning

Empower your data scientists to self-serve streaming data. Let them explore, build, test, deploy and monitor their ML models without (much) support from you. Just create a sandboxed workspace, connect it to raw data streams, and off they go. You can also hook their real-time model results to production to connect them to your data consumers.

Person looking at two screens in the dark.

Changing data engineering for good

Quix was founded by Formula 1 engineers to bring the power of stream processing to data engineers in any industry.

Their expertise in unifying hundreds of data sources and millions of data points per second created a platform that helps data engineers manage a massive volume and velocity of data.

By processing data in-memory at the moment it is created, you can capture more value from your data while capably managing a high-volume pipeline.

It’s flexible, powerful, resilient and efficient. Ready to learn more? Chat with a friendly expert about what else you can do with Quix.

Graphic showing Quix Streams windowing code

Introducing Streaming DataFrames

Learn how Streaming DataFrames can simplify real-time data processing in Python with a familiar DataFrame approach.
Graphic showing Quix Streams windowing code
Words by
Tomáš Neubauer
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.
Featured image for the "Navigating stateful stream processing" post published on the Quix blog
Words by
Tim Sawicki
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.
windowing in stream processing
Words by
Daniil Gusev

Why the data pipeline is changing everything

Find out why analysts and market-watchers agree that traditional data processing must be replaced by stream processing. It’s happening now as innovators embrace new opportunities for greater personalization, automation and revenue.

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Quix Report cover.