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Fast Kafka consumers are not automatically safe Kafka consumers. A Java service can keep up with partitions, restart cleanly, and still leave the business in a state that is hard t...
Some time ago, a former teammate of mine reached out with a very specific request: Can you add a way to mock Kafka in your app? I need something simple, just a way for me to prod...
TL;DR: Kafka interviews test fundamentals, architecture, producers, consumers, streams, and real-world troubleshooting. Strong candidates should explain topics, partitions, offsets...
Managing high-volume message traffic in distributed architectures is crucial. Efficient use of database and CPU resources is also very important. There are structures that allow us...
For over a decade, running Apache Kafka in production meant running two distributed systems simultaneously. Kafka handled message streaming; ZooKeeper handled cluster metadata, con...
In a microservices system, that tight coupling turns a small hiccup into a cascading slowdown. Thread pools fill, retries amplify traffic, and suddenly your simple request is block...
Learn about what causes the CommitFailedException when working with Kafka in Java and some approaches to help avoid it occurring. The post Understanding and Avoiding CommitFailedE...
Over half year ago I’ve open sourced klag, a lightweight Kafka consumer lag exporter. I’ve been working in data streaming systems and data infrastructure for over a decade and...
Reviewed and fact-checked by Sayantoni Das We work and live in a time when we rely increasingly on data to get things done. Applications, services, software, mobile devices, and o...
I've been running Kafka and Spark Structured Streaming together in production for about five years. Not in demo environments or proof-of-concept projects. In systems processing ins...
Привет, Хабр! Меня зовут Павел, я ведущий разработчик. В этой статье расскажу про Kafka, consumer groups, lag, offset commit и встроенное сжатие сообщений. Не в формате “что такое...
Learn how to develop lightweight, high-performance stream processing applications in Java with contemporary frameworks
Kafka and Temporal address different failure boundaries, and resilient distributed systems often need both rather than one as a substitute for the other. Kafka is built to move ord...
Apache Kafka has become the de facto standard for real-time data streaming and event-driven architectures. But simply adopting Kafka isn’t…Continue reading on Medium »
Event-driven architecture promises scalability, but in Java-based real-time systems the tradeoffs only surface in production. Drawing on a Java/Kafka contact center platform handli...
In this post, we demonstrate how to implement a production-ready CDC solution by using Amazon Aurora for PostgreSQL, Debezium connectors, and Amazon Managed Streaming for Apache Ka...
Streaming systems usually fail in one of two ways: Loudly, when infrastructure breaks Quietly, when one bad record keeps replaying until the pipeline is effectively dead T...
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Если вы продаёте на двух и более маркетплейсах, вы почти наверняка с этим сталкивались: товар продан на Ozon, но на Wildberries он ещё висит в наличии. Приходит заказ на то, чего н...
A Practical Walkthrough Most distributed systems eventually hit a wall with their messaging layer, whether it's Kafka's tight coupling between compute and storage, RabbitMQ's limit...
Привет, Хабр! Меня зовут Андрей Серебрянский. Раньше я строил платформы потоковой обработки данных в банках, а теперь вместе с командой разрабатываю YDB Topics и YMQ. После своих д...
In this post, we explore how Buildkite uses Amazon Managed Streaming for Apache Kafka (Amazon MSK) and Amazon Managed Service for Apache Flink to power Test Engine’s streaming-firs...
Kafka часто появляется на System Design интервью, когда нужно асинхронно обрабатывать события и масштабировать систему.Но за словами “добавим Kafka” скрывается много важных деталей...
Schema proliferation builds slowly and gets expensive fast. One schema per event type feels right until there are ten tables, union queries spanning all of them, and a single field...
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