Neu in Confluent Cloud: Daten & Pipelines für KI-fähiges Streaming zugänglich machen | Mehr erfahren
Technology
Queues for Apache Kafka® Is Here: Your Guide to Getting Started in Confluent
Confluent announces the General Availability of Queues for Kafka on Confluent Cloud and Confluent Platform with Apache Kafka 4.2. This production-ready feature brings native queue semantics to Kafka through KIP-932, enabling organizations to consolidate streaming and queuing infrastructure while...
AI Tools for Builders — Confluent's MCP Server & Agent Skills
Confluent's AI developer tools are now GA: an open-source local MCP server, a managed MCP server, and Agent Skills. Together they give AI coding assistants direct access to your streaming platform — the tools to act on it and the domain knowledge to build correctly.
New in Confluent Intelligence: Real-Time Context Engine Upgrade, New Model Support, ML Functions, and More
Explore new Confluent Intelligence features: enhanced querying with Real-Time Context Engine, PII detection, sentiment analysis, and support for TimesFM, Anthropic, and Fireworks AI models.
Building a Real-Time Streaming ETL Pipeline in 20 Minutes
There has been a lot of talk recently that traditional ETL is dead. In the traditional ETL paradigm, data warehouses were king, ETL jobs were batch-driven, everything talked to everything […]
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 […]
The Future of ETL Isn’t What It Used To Be
In his book Design Patterns Explained, Alan Shalloway compares his car to an umbrella. After all, he uses both to stay dry in the rain. The umbrella has an advantage […]
Build Services on a Backbone of Events
For many, microservices are built on a protocol of requests and responses. REST etc. This approach is very natural. It is after all the way we write programs: we make […]
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 […]
Getting Started with the Kafka Streams API using Confluent Docker Images
Introduction What’s great about the Kafka Streams API is not just how fast your application can process data with it, but also how fast you can get up and running […]
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: […]
Announcing Confluent Cloud: Apache Kafka® as a Service
Today, I’m really excited to announce Confluent CloudTM, Apache Kafka® as a Service: the simplest, fastest, most robust and cost effective way to run Apache Kafka in the public cloud. […]
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 […]
Watermarks, Tables, Event Time, and the Dataflow Model
The Google Dataflow team has done a fantastic job in evangelizing their model of handling time for stream processing. Their key observation is that in most cases you can’t globally […]
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...
Securing the Confluent Schema Registry for Apache Kafka®
Note: The blog post Ensure Data Quality and Data Evolvability with a Secured Schema Registry contains more recent information. If you use Apache Kafka to integrate and decouple different data […]