Quix – Bringing the Power of Stream Processing to Data Scientists, Developers, and Product Teams

With the growth of the Internet of Things (IoT) and customer expectations, the significance of data streaming and stream processing has increased. Companies can have thousands of data sources that get piped to different destinations. The data can be processed using stream processing techniques, and generally consists of small chunks of data. Streaming data allows pieces of data to be processed in real or near real-time. Data streams allow an organization to process data in real-time, giving companies the ability to monitor all aspects of its business. The real-time nature of the monitoring allows management to react and respond to crisis events much quicker than any other data processing methods. Data streams offer a continuous communication channel between all the moving parts of a company and the people who can make decisions. Data in the format of streams are highly significant in today’s world. Numerous IoT devices and internet users generate huge volumes of continuous, real-time data every second. Processing this data in real-time is both a challenge and an opportunity for organizations.

Quix was founded by four Formula 1 engineers, who harnessed the power of data stream processing to make real time decisions. From their experience at McLaren, they knew the value of streaming data. So they created Quix as a platform to empower developers and data scientists from all industries. Quix helps data engineers manage the ever-greater volume and velocity of data. It makes streaming data easily accessible to Python professionals and ML models. And it lets developers build and test projects in an always-live environment, so code performs flawlessly throughout the production lifecycle. Quix's platform is trusted by developers at McLaren, Deloitte, the National Health Service (UK), and across industries including manufacturing, financial services, gaming, and automotive.

Why Quix

Quality: Streaming-first improves data quality by cleaning and preparing data in motion. With well-ordered data flowing into a warehouse, more people can self-serve information for analytics and machine learning.

Efficiency: Streaming-first increases efficiency by processing data “in memory” using a message broker. The distributed architecture needs no I/O operations, and can efficiently share intelligence with any downstream system.

Speed: Streaming-first helps organizations deliver modern domain-driven design architecture. Microservices provide simple and reusable code so data practitioners can deliver higher quality features, faster.

Data Stream Processing Made Simple with Innovative Product Suite

Data Integration Made Easy: Meet your data where it’s at. Quix’s APIs allow you to ingest and consume data from HTTP and web applications over WebSockets. The SDK for Python and C# lets you take advantage of Kafka’s reliability and efficiency, even if you’re not a Kafka expert. Use monitoring tools to explore historic data and real-time data flowing in your pipelines to check the performance of your system. Connect your tools seamlessly. Easily integrate microservices and deploy pipeline components with the open-source library. You can customize them or plug and play — no coding necessary. Get to work faster. Start developing streaming applications less than a minute after you create an account with the ready-to-use developer environment. Plan for long-term success. Clean and normalize data in Quix before it goes to a warehouse. This reduces storage costs and makes it easier to scale your architecture.

Data Applications: Keep your data in order with the SDK, which bundles data and metadata in a stream. You’ll be able to easily differentiate data streams when integrating, transforming and delivering them. Keep costs low — even when you’re processing multiple data streams at high frequencies. You can balance latency and cost with the SDK’s customizable buffer feature for managing high volumes of data. The SDK automatically serializes data from native types into your preferred language for top performance. You can work with familiar types, such as Pandas DataFrames, or use their ParameterData class without worrying about the conversions happening behind the scenes.

Asset Tracking: Some of the most valuable assets in your business are deployed far beyond warehouse walls. Monitor your fleet to produce real-time visualizations for operational teams. Event-based notifications alert you to traffic, weather and delays. Automate responses for fulfillment timelines and trigger messages to downstream customers. The data you need to plan and adapt your mobility solution is out there — it’s just stuck in silos. Quix unifies data in streams that are relevant to a delivery or asset. Use ML to calculate the most efficient journey, accounting for traffic and demand. Make smarter loading decisions, weigh speed against cost factors, and even calculate CO2 emissions. Stream processing helps you share credible, precise arrival times with your customers. This reduces delivery wait time to improve driver productivity. Use ML to compare predicted progress against actual progress to identify wasted minutes.

Surge Pricing: The busiest times don’t have to be painful — they can be more profitable with surge pricing, while also dialing back some peak demand. Uber uses stream processing to implement surge pricing and entice more drivers to work during peak periods. Quix makes it easy for companies to predict and respond to peaks and dynamically change pricing or incentives. Your customer service hub is only as good as their information. When data is locked into historical reports, operators can merely react to problems. Quix gives service leaders up-to-the-millisecond data to make better decisions. Use layer in ML and automation for decision support and troubleshooting.

Customer Retention: Real-time data helps you better understand your product lifecycle, using ML to detect signals that a customer is likely to churn. Use this data to automate “surprise and delight” moments for your customers, such as sending an unexpected gift shortly before a subscription renewal. You can also support customer service by detecting problems early. The data load and query time needed for personalized experiences severely slows digital products. But customers won’t tolerate waiting for pages to load — latency is the new outage. Quix helps you build lightning-fast applications powered by real-time data, to immediately serve high-value content, wrapped in a customized experience. 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 use their real-time model results to production to connect them to your data consumers. 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.

Data Science: It is business-critical — but too often, you must rely on data engineers to provide access to data, or deploy your ML models. Quix provides you with independence. 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. Build better business intelligence dashboards using the streaming infrastructure. Aggregate data from any source, layer on business logic, and then easily query to generate high-value reports. A simple platform for you to build real time data pipelines, both for your ETL and model deployment. Quix offers tate-of-the-art real time MLOps with the click of a button. Run your ML artifact in the Quix development environment — crafted specifically for Python professionals so there are no language barriers. Back test your results in real time against historical or live data streams. You can also A/B test models in parallel to uncover new insights and optimize. Online learning models re-train themselves in real time as new data emerges, adapting quickly to changing environments. Simply combine an online learning library such as River with Quix to create next-generation adaptive ML products.

Meet the Leader

Mike Rosam is a co-founder and the Chief Executive Officer of Quix.

"With software engineering best practices built in, Quix enables every engineer to develop, test, version control, and CI/CD mircoservices to process data in the cloud."