Consolidate high frequency sensor data in the cloud

Industrial companies use Quix to integrate large volumes of data from legacy systems, enabling use cases like real-time monitoring and analytics, model-based product development, digital twins, and more.

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Collect more high frequency data from more devices

Cost-effectively collect Terabytes of data per day into a centralized cloud data store, providing a single pane of glass for your infrastructure. Pre-process data to ensure data quality and consistency across devices.

Quickly integrate legacy plant data

Easily integrate data from legacy systems, even without software engineering skills. Quix provides out of the box connector templates for popular technologies like MQTT, OPC-UA and Telegraf, that can also be customised for your use case.

Rapidly innovate with governance

Visualise and query data in real time, or run advanced MATLAB or ML models. 'Low DevOps' tooling ensures R&D teams can build fast, while governance features like projects, environments, permissions, auditing, monitoring, lineage and observability ensure IT maintains control over data quality and production apps.

The transition towards model-based product development is challenging

Level 1

Capability

Leverage data historians to understand operations at the device or machine level, to improve device or machine operations.

Challenges

Data historians only collect low resolution data, limiting your ability to analyse and optimise machine performance.
Data historians collect data on a per machine basis, co-located with each machine. This makes it difficult to compare performance of each device.
Cherry picking: every team has to go back into the plant network to get data for their use cases which leads to data duplication and data silos.
Data historians can get expensive over time, especially if there is a high turnover of machines, as you pay for unique signals, even from machines that are no longer collecting data.

Level 2

Capability

Consolidate data in a centralized cloud data store to see all operations globally through a single pane of glass.

Challenges

Integrating with legacy technologies can be difficult, especially if there are no out of the box source connectors.
Heterogeneous data sources lead to different data frequencies and formats that are challenging to consolidate.
Edge connectivity issues lead to late arriving data, large data bursts and potential data loss.
Large data volumes lead to cost and performance issues.
Lack of data engineering skills make these projects challenging for existing teams to progress.

Level 3

Capability

Move to real-time monitoring and analytics minimise impact of issues and failures.

Challenges

Real-time visualisation tools like Grafana struggle with large volumes of high frequency data.
Developing analytics and models on streaming data is more complex than on batch.
Legacy analytics and modeling tools such as MATLAB are expensive.
Because it's proprietary, MATLAB is inhibited by the speed of Mathworks, which limits adoption of cutting edge machine learning and AI technologies. Python is leading the way with its vibrant open source ecosystem, with millions of developers building innovative solutions.

Level 4

Capability

Increase innovation through enabling domain experts to leverage data for simulation (digital twins) and predictive AI.

Challenges

Domain experts have limited software and data engineering skills, which means they cannot effectively use data for their use cases.
API-based systems for serving ML / AI models can become bottlenecks under high request loads.

Accelerate your journey with Quix

Decrease time-to-value: Start consolidating your data in hours, not weeks, thanks to out-of-the-box integrations and intuitive self-service tools.

Reduce operational complexity: Focus on delivering value from your data rather than managing data infrastructure with Quix’s scalable 'no DevOps' platform.

Future-proof your tech stack: Whether it’s integrating decades-old, mission-critical code in Java or C++, or running cutting-edge Python ML models, Quix accommodates any Docker-compatible app.

Benefit from decades of industry experience: Created by former Formula 1 engineers who specialize in leveraging real-time data for decision-making, Quix is your strategic partner.

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Real world success stories

Learn how customers are leveraging Quix to accelerate innovation and deliver more business value from their data.

Customer story

Optimizing manufacturing efficiency with streaming data and ML

How CloudNC makes better predictions for maintenance, generates insights with very low latency, and transforms its factory operations with streaming data.

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Get started with templates for common use cases

Check out our easily customisable templates for building your first integration.

Use case

Code snippet

Modernize legacy SCADA infrastructure with real-time data processing

Build real-time data processing pipelines for modern energy systems that integrate data from smart meters, IoT sensors, and legacy SCADA systems.

Energy sector

Project template

Use case

Code snippet

Integrate data from multiple sources into an Iceberg Lakehouse

Ingest data from multiple sources into Iceberg tables on S3. Use Quix Streams to normalize and standardize the data before it reaches your data lakehouse.

Data integration

ETL

Project template

Use case

Code snippet

Predictive maintenance

Simulates data from a fleet of 3D printers and predicts which ones are going to fail before the print is finished using a time series forecasting algorithm

Alerting

Anomaly detection

IoT

View all templates

Choose the deployment option that fits your business

Run the Quix platform in the cloud or on-premise, depending on your use case. Or build your own with Quix Streams, Apache Kafka and Kubernetes.

Quix Cloud

Quix Cloud empowers you to build, deploy, and optimize data integration pipelines – without needing an army of data engineers.

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Quix Platform

Run Quix on-premise for non-negotiable compliance where serious scale meets extra low latency.

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Quix Streams

An open-source Python framework for real-time data processing and integrations with Streaming DataFrames.

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Explore the platform

Quix Streams repo

Interested in Quix Cloud?

Learn how companies are building data integration pipelines with Quix.

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Get started with Quix Streams

An open source library for processing data in Kafka using pure Python.

Quix Streams repo

Getting started with Quix

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Talk with our team of experts to learn more about how companies are building data integration pipelines with Quix.

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Ask our community

Join our Slack to get help with Quix Streams from our friendly developer community.

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