InfoQ Homepage Apache Kafka Content on InfoQ
-
Kafka Streams - from the Ground Up to the Cloud
Marius Bogoevici introduces the Kafka Streams API and the Kafka Streams processing engine, showing how to write and deploy Kafka Streams applications using Spring Cloud Stream.
-
Streaming SQL Foundations: Why I ❤Streams+Tables
Tyler Akidau explores the relationship between the Beam Model and stream & table theory, stream processing in SQL with Apache Beam, Calcite, Flink, Kafka KSQL and Apache Spark’s Structured streaming.
-
Cloud Event-Driven Architectures with Spring Cloud Stream 2.0
Oleg Zhurakousky overviews various types of event-driven architectures, and how the different message-oriented components of the Spring portfolio fit into the picture.
-
When Streams Fail: Kafka Off the Shore
Anton Gorshkov discusses how to evaluate and architect a resilient streaming platform, focusing on Kafka and Spark streaming and sharing his experience of using them to process financial transactions.
-
Real-Time Recommendations Using Spark Streaming
Elliot Chow discusses the data pipeline that they built with Kafka, Spark Streaming, and Cassandra to process Netflix user activities in real time for the Trending Now row.
-
I Can't Believe It's Not a Queue: Using Kafka with Spring
Joe Kutner talks about Kafka and where it fits in a Spring app and how to make it do things message queues simply can't.
-
Reactive Kafka
Rajini Sivaram talks about Kafka and reactive streams and then explores the development of a reactive streams interface for Kafka and the use of this interface for building robust applications.
-
Spring for Apache Kafka
Gary Russell takes a look at the features of the spring-kafka project as well as the new version (2.0) of spring-integration-kafka which is now based on the Spring for Apache Kafka project.
-
ETL Is Dead, Long Live Streams
Neha Narkhede shares the experience at LinkedIn moving from ETL to real-time streams, the challenges of scaling Kafka to hundreds of billions of events/day, supporting thousands of engineers, etc.
-
Ingest & Stream Processing - What Will You Choose?
Pat Patterson and Ted Malaska talk about current and emerging data processing technologies, and the various ways of achieving "at least once" and "exactly once" timely data processing.
-
Monitoring and Troubleshooting Real-Time Data Pipelines
Alan Ngai and Premal Shah discuss best practices on monitoring distributed real-time data processing frameworks and how DevOps can gain control and visibility over these data pipelines.
-
Large-Scale Stream Processing with Apache Kafka
Neha Narkhede explains how Apache Kafka was designed to support capturing and processing distributed data streams by building up the basic primitives needed for a stream processing system.