The Quix blog

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.

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.

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.

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.

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?

An in-depth comparison of Apache Kafka and Pulsar, covering criteria such as architectural differences, operational attributes, developer experience, ecosystems, deployment options, and security.

Teams can now build AI applications on Confluent’s data in motion, with Quix, the AI-ready event streaming application framework.

Explore the AI applications that you can build when connecting Quix with Confluent.

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.

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.

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.

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.

We tested Apache Spark vs Apache Flink vs Quix Streams on performance and flexibility. The results surprised us.

Dive deep into the performance and limitations of Python client libraries to choose the best stream processing solution for your data.