The Quix blog

Learn how Streaming DataFrames can simplify real-time data processing in Python with a familiar DataFrame approach.

Discover what sets stateful stream processing apart from stateless processing and read about its related concepts, challenges and use cases.

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.

Explore streaming windows (including tumbling, sliding and hopping windows) and learn about windowing benefits, use cases and technologies.

The auto.offset.reset configuration defines how Kafka consumers should behave when no initial committed offsets are available for the partitions assigned to them. Learn how to work with this configuration and discover its related challenges.

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.

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.

Learn how to get started quickly with Hello Quix base template and use it as a foundation for your projects.

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.

Learn the basics for running your own AI-powered support bots and understand the challenges involved in using AI for customer support.

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.

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.

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

Deploy a reference application that shows you how to do real-time predictive analytics on sensor data from a simulated fleet of 3D printers.

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.

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.

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.

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.

Discover event-driven programming (EDP) use cases and technologies, and learn about the relation between EDP and event-driven architecture (EDA).

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.

We explore the differences between Kafka and ActiveMQ, and which use cases each are best suited to.

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.

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.

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?