The Quix blog

All Posts
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
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
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
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
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
Kafka vs Flink logo images.
Ecosystem

Apache Kafka vs Apache Flink: friends or rivals?

Explore the unique features and limitations of Apache Kafka and Apache Flink and learn how these open source streaming titans can either join forces or operate independently.
Tun Shwe
VP Data
Animated rocket going down.
Ecosystem

The drawbacks of ksqlDB in machine learning workflows

Using ksqlDB for real-time feature transformations isn't as easy as it looks. I revisit the strategy to democratize stream processing and examine what's still missing.
Mike Rosam
CEO & Co-Founder
Quix vs Flink logos on purple background.
Ecosystem

Quix as an Apache Flink alternative: a side-by-side comparison

Explore the differences between Quix and Apache Flink and find out when it's better to use Quix as a Flink alternative. If you’re searching for Apache Flink alternatives, this guide offers a detailed, fair comparison to help you make an informed decision.
Mike Rosam
CEO & Co-Founder
Kinesis vs Kafka logos on navy blue background
Ecosystem

Kinesis vs Kafka - A comparison of streaming data platforms

A detailed comparison of Apache Kafka and Amazon Kinesis that covers categories such as operational attributes, pricing model, and time to production while highlighting their key differences and use cases that they typically address.
Mike Rosam
CEO & Co-Founder
Flink vs Spark on grey background.
Ecosystem

Flink vs Spark: Benchmarking stream processing client libraries

We tested Apache Spark vs Apache Flink vs Quix Streams on performance and flexibility. The results surprised us.
Tomáš Neubauer
CTO & Co-Founder
Compare Quix, Flink and Spark illustration.
Ecosystem

A very detailed comparison of Python stream processing libraries

Dive deep into the performance and limitations of Python client libraries to choose the best stream processing solution for your data.
Mike Rosam
CEO & Co-Founder
Banner image for the article "Understanding Kafka’s auto offset reset configuration: Use cases and pitfalls" published on the Quix blog

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.
Tim Sawicki
Python SDK Engineer