The Quix blog
Ecosystem
July 9, 2024
How to fix common issues when using Spark Structured Streaming with PySpark and Kafka
A look at five common issues you might face when working with Structured Streaming, PySpark, and Kafka, along with practical steps to help you overcome them.
Ecosystem
June 6, 2024
Quix Streams—a reliable Faust alternative for Python stream processing
A detailed comparison between Faust and Quix Streams covering criteria like performance, coding experience, features, integrations, and product maturity.
Tutorials
June 3, 2024
How to enrich a stream of data in real time with Quix and Redis
Learn how to enrich real-time sensor data streams by looking up device coordinates in Redis and appending them to the data stream using Quix.
Ecosystem
May 23, 2024
Debugging PyFlink import issues
Solutions to a common issue that Python developers face when setting up PyFlink to handle real-time data.
Ecosystem
May 16, 2024
Choosing a Python Kafka client: A comparative analysis
Assessing Python clients for Kafka: kafka-python, Confluent, and Quix Streams. Learn how they compare in terms of DevEx, broker compatibility, and performance.
Ecosystem
May 1, 2024
PyFlink — A deep dive into Flink’s Python API
Learn how to use PyFlink for data processing workloads, read about its architecture, and discover its strengths and limitations.
Tutorials
April 30, 2024
Clickstream analytics: creating a user interaction heat map for an e-commerce website
See Quix Streams in action by vizualizing mouse movement patterns in real-time using hopping windows. A Python data streaming tutorial for web analytics.
Announcements
April 9, 2024
Introducing Streaming DataFrames
Learn how Streaming DataFrames can simplify real-time data processing in Python with a familiar DataFrame approach.
Industry insights
March 26, 2024
Navigating stateful stream processing
Discover what sets stateful stream processing apart from stateless processing and read about its related concepts, challenges and use cases.
Industry insights
March 14, 2024
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.
Industry insights
March 14, 2024
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.
Ecosystem
February 28, 2024
Understanding Kafka’s auto offset reset configuration: Use cases and pitfalls
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.
Ecosystem
February 19, 2024
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.
Tutorials
February 12, 2024
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.
Tutorials
February 9, 2024
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.
Industry insights
February 6, 2024
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.
Tutorials
January 25, 2024
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.
Tutorials
January 22, 2024
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.
Industry insights
December 22, 2023
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.
Industry insights
December 21, 2023
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
Tutorials
December 8, 2023
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.
Tutorials
November 30, 2023
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.
Ecosystem
November 13, 2023
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.
Ecosystem
November 6, 2023
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.