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Apache Kafka
Data Products, Data Contracts, and Change Data Capture
Change data capture is a popular method to connect database tables to data streams, but it comes with drawbacks. The next evolution of the CDC pattern, first-class data products, provide resilient pipelines that support both real-time and batch processing while isolating upstream systems...
Introducing Confluent Cloud Freight Clusters
Learn how the latest innovations in Kora enable us to introduce new Confluent Cloud Freight clusters, which can save you up to 90% at GBps+ scale. Confluent Cloud Freight clusters are now available in Early Access.
Contributing to Apache Kafka®: How to Write a KIP
Learn how to contribute to open source Apache Kafka by writing Kafka Improvement Proposals (KIPs) that solve problems and add features! Read on for real examples.
Log Compaction – Highlights in the Apache Kafka® and Stream Processing Community – June 2017
We are very excited for the GA for Kafka release 0.11.0.0 which is just days away. This release is bringing many new features as described in the previous Log Compaction […]
Introduction to Apache Kafka® for Python Programmers
In this blog post, we’re going to get back to basics and walk through how to get started using Apache Kafka with your Python applications. We will assume some basic […]
Log Compaction – Highlights in the Apache Kafka® and Stream Processing Community – May 2017
We are very excited to share a wealth of streaming news from the past month! If you are looking for an ideal streaming data service that delivers the resilient, scalable […]
Optimizing Your Apache Kafka® Deployment
Apache Kafka® is the best enterprise streaming platform that runs straight off the shelf. Just point your client applications at your Kafka cluster and Kafka takes care of the rest: […]
The 2017 Apache Kafka Survey: Streaming Data on the Rise
In Q1, Confluent conducted a survey of the Apache Kafka® community and those using streaming platforms to learn about their application of streaming data. This is our second execution of […]
Stories from the Front: Lessons Learned from Supporting Apache Kafka®
Here at Confluent, our goal is to ensure every company is successful with their streaming platform deployments. Oftentimes, we’re asked to come in and provide guidance and tips as developers […]
Creating a Data Pipeline with the Kafka Connect API – from Architecture to Operations
Pandora began adoption of Apache Kafka® in 2016 to orient its infrastructure around real-time stream processing analytics. As a data-driven company, we have a several thousand node Hadoop clusters with hundreds of Hive tables critical to Pandora’s operational and reporting success...
Log Compaction – Highlights in the Apache Kafka® and Stream Processing Community – March 2017
Big news this month! First and foremost, Confluent Platform 3.2.0 with Apache Kafka® 0.10.2.0 was released! Read about the new features, check out all 200 bug fixes and performance improvements […]
The First Annual State of Apache Kafka® Client Use Survey
At the end of 2016 we conducted a survey of the Apache Kafka® community regarding their use of Kafka clients (the producers and consumers used with Kafka) and their priorities […]
Log Compaction – Highlights in the Apache Kafka and Stream Processing Community – February 2017
As always, we bring you news, updates and recommended content from the hectic world of Apache Kafka® and stream processing. Sometimes it seems that in Apache Kafka every improvement is […]
Apache Kafka: Getting Started
Seems that many engineers have “Learn Kafka” on their new year resolution list. This isn’t very surprising. Apache Kafka is a popular technology with many use-cases. Armed with basic Kafka […]
Log Compaction: Highlights in the Apache Kafka and Stream Processing Community – January 2017
Happy 2017! Wishing you a wonderful year full of fast and scalable data streams. Many things have happened since we last shared the state of Apache Kafka® and the streams […]
The Data Dichotomy: Rethinking the Way We Treat Data and Services
If you were to stumble upon the whole microservices thing, without any prior context, you’d be forgiven for thinking it a little strange. Taking an application and splitting it into fragments, […]
Log Compaction | Highlights in the Apache Kafka and Stream Processing Community | December 2016
This month saw the proposal of a few KIPs which will have a big impact on Apache Kafka’s semantics as well as Kafka’s operability. KIP-95 : Incremental Batch Processing for […]