The Quix blog

All Posts
Banner image for the article "Rethinking Build vs Buy" published on the Quix blog
Industry insights

The challenges of processing data from devices with limited connectivity and how to solve them

Need to process data from frequently disconnected devices? Better use an event streaming platform paired with a powerful stream processing engine. Here's why.
Mike Rosam
CEO & Co-Founder
Banner image for the article "Rethinking Build vs Buy" published on the Quix blog
Industry insights

Rethinking “Build vs Buy” for Data Pipelines

“Build vs buy” is outdated — most companies need tools that provide the flexibility of a build with the convenience of a buy. It’s time for a middle ground.
Mike Rosam
CEO & Co-Founder
Banner image for the article "How to Empower Data Teams for Effective Machine Learning Projects" published on the Quix blog
Industry insights

How to Empower Data Teams for Effective Machine Learning Projects

Learn how to boost success rates ML projects by empowering data teams through a shift-left approach to collaboration and governance.
Mike Rosam
CEO & Co-Founder
Banner image for the article "Gaming & ML: How Real-Time ML Enhances Player Experience" published on the Quix blog
Industry insights

Gaming & ML: How Real-Time ML Enhances Player Experience

Discover the benefits, applications and challenges of real-time ML in gaming, and learn how game development studios are implementing real-time ML systems.
Steve Rosam
Head of Content
Banner image for the article "Shifting Left: Discover What's Possible When You Process Data Closer to the Source" published on the Quix blog
Industry insights

Shifting Left: Discover What's Possible When You Process Data Closer to the Source

Learn how 'shifting left' in data engineering improves data quality by processing it closer to the source, following Netflix's example and modern best practices
Tun Shwe
VP Data
Banner image for the article "The Power of Real-Time Data in Modern Gaming Live Operations (Live Ops)" published on the Quix blog
Industry insights

The Power of Real-Time Data in Modern Gaming Live Operations (Live Ops)

Learn how Live Ops and real-time data analytics transform gaming, enabling developers to enhance player retention, optimize monetization, and improve gameplay.
Steve Rosam
Head of Content
Banner image for the article "AI Anti-Cheat Solutions and Real-Time Data: The Antidote to AI-Driven Cheating in Gaming" published on the Quix blog
Industry insights

AI Anti-Cheat Solutions and Real-Time Data: The Antidote to AI-Driven Cheating in Gaming

Discover how AI-driven cheating is poisoning online gaming and learn why real-time data processing is crucial for effective, custom AI anti-cheat solutions.
Steve Rosam
Head of Content
Banner image for the article "Real-Time Analytics for Gaming: Transforming Player Experience and LiveOps in Modern Gaming" published on the Quix blog
Industry insights

Real-Time Analytics for Gaming: Transforming Player Experience and LiveOps in Modern Gaming

Real-time gaming analytics offer a competitive edge, enabling game developers to create more engaging, personalized, and responsive gaming experiences.
Steve Rosam
Head of Content
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.
Tim Sawicki
Senior Python Engineer
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.
Daniil Gusev
Lead Python Engineer
real time feature engineering architecture diagram
Industry insights

What is real-time feature engineering?

Pre-computing features for real-time machine learning reduces the precision of the insights you can draw from data streams. In this guide, we'll look at what real-time feature engineering is and show you a simple example of how you can do it yourself.
Tun Shwe
VP Data
Banner image for the article "Streaming ETL 101" published on the Quix blog
Industry insights

Streaming ETL 101

Read about the fundamentals of streaming ETL: what it is, how it works and how it compares to batch ETL. Discover streaming ETL technologies, architectures and use cases.
Tun Shwe
VP Data
LLMOps: large language models in production with Quix
Industry insights

LLMOps: running large language models in production

LLMOps is a considered, well structured response to the hurdles that come with building, managing and scaling apps reliant on large language models. From data preparation, through model fine tuning, to finding ways to improve model performance, here is an overview of the LLM lifecycle and LLMOps best practices.
Tun Shwe
VP Data
What is stream processing
Industry insights

What is stream processing?

An overview of stream processing: core concepts, use cases enabled, what challenges stream processing presents, and what the future looks like as AI starts playing a bigger role in how we process and analyze streaming data
Tun Shwe
VP Data
Simplified diagram showing event-driven programming components (event listener, event queue, event loop, event handler)
Industry insights

The what, why and how of event-driven programming

Discover event-driven programming (EDP) use cases and technologies, and learn about the relation between EDP and event-driven architecture (EDA).
Tomáš Neubauer
CTO & Co-Founder
Simplified diagram of a machine learning pipeline.
Industry insights

The anatomy of a machine learning pipeline

Explore the characteristics, challenges, and benefits of machine learning pipelines, and read about the steps involved in training and deploying ML models to production.
Alex Diaconu
Technical Writer
Three data processing icons in blue background.
Industry insights

The fundamentals of real-time machine learning

What is real-time machine learning? How is it different from batch ML? What are common real-time ML use cases? What are the challenges of building real-time ML capabilities? All these questions and more are answered in this article.
Mike Rosam
CEO & Co-Founder
Man standing in front of a labyrinth illustration.
Industry insights

Real-Time infrastructure tooling for data scientists

Explore the evolution of new tools for real-time pipelines that aim to solve the ongoing problem of data scientists' need for more infrastructure expertise.
Tun Shwe
VP Data