Author: Michael Noll
The Future of SQL: Databases Meet Stream Processing
SQL has proven to be an invaluable asset for most software engineers building software applications. Yet, the world as we know it has changed dramatically since SQL was created in […]
Project Metamorphosis Month 8: Complete Apache Kafka in Confluent Cloud
This is the eighth and final month of Project Metamorphosis: an initiative that brings the best characteristics of modern cloud-native data systems to the Apache Kafka® ecosystem, served from Confluent […]
Streams and Tables in Apache Kafka: Elasticity, Fault Tolerance, and Other Advanced Concepts
Now that we’ve learned about the processing layer of Apache Kafka® by looking at streams and tables, as well as the architecture of distributed processing with the Kafka Streams API […]
Streams and Tables in Apache Kafka: Processing Fundamentals with Kafka Streams and ksqlDB
Part 2 of this series discussed in detail the storage layer of Apache Kafka: topics, partitions, and brokers, along with storage formats and event partitioning. Now that we have this […]
Streams and Tables in Apache Kafka: Topics, Partitions, and Storage Fundamentals
Part 1 of this series discussed the basic elements of an event streaming platform: events, streams, and tables. We also introduced the stream-table duality and learned why it is a […]
Streams and Tables in Apache Kafka: A Primer
This four-part series explores the core fundamentals of Kafka’s storage and processing layers and how they interrelate. In this first part, we begin with an overview of events, streams, tables, […]
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 […]
Kafka Summit NYC is Almost Here – Don’t Miss the Streams Track!
Ever wondered what it’s like to run Kafka in production? What about building and deploying microservices that process streams of data in real-time, at large scale? Or, maybe just the […]
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 […]
Join us for Kafka Summit Hackathon in New York City
I’m happy to announce Confluent will be hosting another Kafka Summit Hackathon on May 7th in New York City! The free hackathon will take place a day prior to Kafka […]
Secure Stream Processing with the Streams API in Kafka
This blog post is the second in a series about the Streams API of Apache Kafka, the new stream processing library of the Apache Kafka project, which was introduced in Kafka v0.10. Current […]
Elastic Scaling in the Streams API in Kafka
This blog post is the first in a series about the Streams API of Apache Kafka, the new stream processing library of the Apache Kafka project, which was introduced in Kafka v0.10. Current […]
Distributed, Real-time Joins and Aggregations on User Activity Events using Kafka Streams
In previous blog posts we introduced Kafka Streams and demonstrated an end-to-end Hello World streaming application that analyzes Wikipedia real-time updates through a combination of Kafka Streams and Kafka Connect. […]
Apache Kafka and Kafka Streams at Berlin Buzzwords
At the beginning of June several Confluent team members attended Berlin Buzzwords 2016, where we gave three talks focused on stream processing and distributed computing. These talks, which we summarize […]