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Banner image for the blog article "Get started in minutes with the Hello Quix template"
Tutorials

Continuously ingest documents into a vector store using Quix, Qdrant, and Apache Kafka

Learn how to set up a decoupled, event-driven pipeline to embed and ingest new content into a vector store as soon as it's published.
Merlin Carter
Senior Content Writer
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
kafka vs spark logos
Ecosystem

Kafka vs Spark - a comparison of stream processing tools

This comparison specifically focuses on Kafka and Spark's streaming extensions — Kafka Streams and Spark Structured Streaming. Kafka Streams excels in per-record processing with a focus on low latency, while Spark Structured Streaming stands out with its built-in support for complex data processing tasks, including advanced analytics, machine learning and graph processing.
Tun Shwe
VP Data
Banner image for the blog article "Get started in minutes with the Hello Quix template"
Tutorials

Continuously ingest documents into a vector store using Quix, Qdrant, and Apache Kafka

Learn how to set up a decoupled, event-driven pipeline to embed and ingest new content into a vector store as soon as it's published.
Merlin Carter
Senior Content Writer
Banner image for the blog article "Get started in minutes with the Hello Quix template"
Tutorials

Get started in minutes with the Hello Quix template

Learn how to get started quickly with Hello Quix base template and use it as a foundation for your projects.
Steve Rosam
Full-stack developer
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
Banner image for the blog article "AI Bots as difficult customers—generating synthetic customer conversations using Llama-2, Kafka and LangChain"
Tutorials

AI Bots as difficult customers—generating synthetic customer conversations using Llama-2, Kafka and LangChain

Learn the basics for running your own AI-powered support bots and understand the challenges involved in using AI for customer support.
Merlin Carter
Senior Content Writer
Banner image for the blog article "Analyze clickstream data in real time and trigger special offers based on user behavior"
Tutorials

How to create a project from a template in Quix

Learn how to get started quickly with Quix project templates and use them as a reference to build your own event-driven, stream-processing application.
Steve Rosam
Full-stack developer
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
Banner image for the blog article Predict 3D printer failures in real-time using sensor data
Tutorials

Predict 3D printer failures in real-time using sensor data

Deploy a reference application that shows you how to do real-time predictive analytics on sensor data from a simulated fleet of 3D printers.
Steve Rosam
Full-stack developer
Banner image for the blog article "Analyze clickstream data in real time and trigger special offers based on user behavior"
Tutorials

Analyze clickstream data in real time and trigger special offers based on user behavior

Learn how to analyze clickstream data in real time using Python. Trigger frontend events and show aggregations in a real-time dashboard—using Quix, Streamlit and Redis Cloud.
Steve Rosam
Full-stack developer
image with aws fargate and lambda logos
Ecosystem

Fargate vs Lambda: a comparison of serverless technologies

The main difference between these two serverless compute platforms is that AWS Fargate takes care of the underlying VMs, networking, and other resources you need to run containers using ECS or EKS, whereas AWS Lambda lets you run standalone, stateless functions without having to consider any of the infrastructure whatsoever.
Mike Rosam
CEO & Co-Founder
Graphic featuring Amazon ECS, EKS and Fargate logos
Ecosystem

Amazon ECS vs. EKS. vs. Fargate: a comparison of container management services

The main difference between them? ECS and EKS are container orchestration services for Docker and Kubernetes that simplify the deployment, management, and scaling of containerized apps. Meanwhile, Fargate is a serverless compute engine that works with both ECS and EKS, removing the need to manage underlying server infrastructure.
Mike Rosam
CEO & Co-Founder
Preview of the front end of a computer vision project template.
Tutorials

Build and deploy your own traffic monitoring app using computer vision

Learn how to fork our new computer vision template and deploy an application that uses London's traffic cameras to gauge current congestion by leveraging object detection to count vehicles.
Tomáš Neubauer
CTO & Co-Founder
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

Read about the fundamentals of event-driven programming (EDP): key concepts, advantages, and challenges. Discover EDP use cases and technologies, and learn about the relation between EDP and event-driven architecture (EDA).
Tomáš Neubauer
CTO & Co-Founder
Graphic featuring Apache Kafka and Redpanda logos
Ecosystem

Redpanda vs. Kafka: comparing architectures, capabilities, and performance

The main difference between them? Kafka is an established Java-based data streaming platform, with a large community and a robust ecosystem. Meanwhile, Redpanda is an emerging, Kafka-compatible tech written in C++, with an architecture designed for high performance and simplicity.
Mike Rosam
CEO & Co-Founder
Graphic featuring Apache Kafka and ActiveMQ logos
Ecosystem

ActiveMQ vs. Kafka: A comparison of differences and use cases

The main difference between them is that Kafka is a distributed event streaming platform designed to ingest and process massive amounts of data, while ActiveMQ is a traditional message broker that supports multiple protocols and flexible messaging patterns.
Mike Rosam
CEO & Co-Founder
2.0 text on radial gradient background.
Announcements

Announcing Quix 2.0—now with Git integration and multiple environments

Quix 2.0 is here 🚀 Designed around the concept of Infrastructure-as-Code, Quix 2.0 makes it easier to build and run reliable, powerful event-streaming applications that scale, with a single source of truth powered by Git.
Mike Rosam
CEO & Co-Founder
Graphic featuring Apache Kafka and RabbitMQ logos
Ecosystem

Apache Kafka vs. RabbitMQ: Comparing architectures, capabilities, and use cases

The main difference between them is that Kafka is an event streaming platform designed to ingest and process massive amounts of data, while RabbitMQ is a general-purpose message broker that supports flexible messaging patterns, multiple protocols, and complex routing.
Mike Rosam
CEO & Co-Founder
Spark vs Beam image.
Ecosystem

Apache Beam vs. Apache Spark: Big data processing solutions compared

The main difference between Spark and Beam is that the former enables you to both write and run data processing pipelines, while the latter allows you to write data processing pipelines, and then run them on various external execution environments (runners). But what are the other differences between Spark and Beam, and how are they similar?
Alex Diaconu
Technical Writer
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
Graphic featuring Apache and Kafka logo.
Ecosystem

Kafka vs Pulsar: Streaming data platforms compared

An in-depth comparison of Apache Kafka and Pulsar, covering criteria such as architectural differences, operational attributes, developer experience, ecosystems, deployment options, and security.
Alex Diaconu
Technical Writer
Quix ML model icons on black background.
Ecosystem

Accelerating AI-ready application development: Quix and Confluent partnership

Teams can now build AI applications on Confluent’s data in motion, with Quix, the AI-ready event streaming application framework.
Mike Rosam
CEO & Co-Founder
Four icons connected to one box in the center.
Ecosystem

Unlocking new use cases: Quix and Confluent partnership

Explore the AI applications that you can build when connecting Quix with Confluent.
Mike Rosam
CEO & Co-Founder
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
The stream

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