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dzone.com /3 weeks ago

Implementing Asynchronous Communication Between Microservices Using Kafka and Spring Boot

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...

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simplilearn.com /3 weeks ago

Top 45+ Kafka Interview Questions And Answers | Simplilearn

TL;DR: Kafka interviews test fundamentals, architecture, producers, consumers, streams, and real-world troubleshooting. Strong candidates should explain topics, partitions, offsets...

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simplilearn.com /3 weeks ago

Kafka Vs RabbitMQ: Key Differences & Features Explained | Simplilearn

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...

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javacodegeeks.com /2 weeks ago

Kafka Without ZooKeeper (KRaft): What Java Developers Actually Need to Reconfigure

For over a decade, running Apache Kafka in production meant running two distributed systems simultaneously. Kafka handled message streaming; ZooKeeper handled cluster metadata, con...

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medium.com /1 week ago

7 Apache Kafka Design Patterns Every Backend Engineer Should Know

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 »

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devops.com /6 days ago

Developing Real-Time Event Processing With Micronaut and Kafka Streams: A Step-by-Step Guide

Learn how to develop lightweight, high-performance stream processing applications in Java with contemporary frameworks

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dzone.com /1 month ago

Combining Temporal and Kafka for Resilient Distributed Systems

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...

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feeds.feedblitz.com /2 weeks ago

Understanding and Avoiding CommitFailedException in Kafka

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...

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dzone.com /4 weeks ago

Parallel Kafka Batch Processing With Kotlin Coroutines in Spring Boot

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...

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dzone.com /1 month ago

Mocking Kafka for Local Spring Development

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...

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infoq.com /2 weeks ago

Article: Scaling Java-Based Real-Time Systems: The Hidden Tradeoffs of Event-Driven Design

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...

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dzone.com /1 month ago

Kafka and Spark Structured Streaming in Enterprise: The Patterns That Hold Up Under Pressure

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...

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dataengineeringcentral.substack.com /1 month ago

Databricks Zerobus - Event Streams + Lake House (be gone Kafka)

it's always something you know

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javacodegeeks.com /2 days ago

Building Idempotent Java Consumers for Replay-Safe Kafka Pipelines

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...

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dzone.com /1 month ago

Event-Driven Pipelines With Apache Pulsar and Go

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...

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habr.com /1 month ago

Простой API, умный сервер: третий класс брокеров, который пропускают между Kafka и RabbitMQ

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...

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habr.com /1 day ago

[Перевод] Как использовать Kafka на собеседовании по System Design

Kafka часто появляется на System Design интервью, когда нужно асинхронно обрабатывать события и масштабировать систему.Но за словами “добавим Kafka” скрывается много важных деталей...

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habr.com /1 month ago

Kafka в проде: как встроенное сжатие спасло кластер, когда retention не успевал чистить диск

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...

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dev.to /1 month ago

klag just got a bunch better — here’s what’s new.

Over half year ago IвЂ™ve open sourced klag, a lightweight Kafka consumer lag exporter. IвЂ™ve been working in data streaming systems and data infrastructure for over a decade and...

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aws.amazon.com /1 month ago

How Buildkite Operates Test Analytics at Massive Scale with Amazon MSK and Amazon Managed Service for Apache Flink

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...

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dzone.com /1 week ago

Dead Letter Queue Patterns in Apache Flink: Handling Poison Messages Without Stopping Your Stream

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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infoq.com /1 month ago

Article: The Schema Proliferation Problem in Kafka and Flink Pipelines: How to Solve It

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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habr.com /1 month ago

Динамические квоты и лимиты: как не завалить очередь в highload

Представьте: ваш сервис Y генерирует 10 000 событий в секунду, а сервис X может проглотить только 500. И при этом нельзя потерять ни одного события, а порядок обработки обязан быть...

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dataengineeringcentral.substack.com /3 weeks ago

Apache Datafusion Comet (Spark Accelerator)

any better than last time?

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