Modern and performant Kafka client library for Ruby based on librdkafka

Standalone Karafka library for producing Kafka messages

The Python fake data producer for Apache Kafka® is a complete demo app allowing you to quickly produce JSON fake streaming datasets and push it to an Apache Kafka topic.

Are you tired of collecting Apache Kafka client metrics using JMX? Worse, you even can't do that? Here is a solution. With the KIP-714, you can collect client metrics from the Kafka broker using OpenTelemetry.

An example project to showcase the capabilities of Apache Camel together with technologies like Kafka, Debezium, Artemis, and more.

Distributed Random Forest in Apache Flink

Implementation of Apache kafka with Spring boot Consumer and Producer application.

This application developed using Spring Boot, Java19, MySQL, Spring Data JPA, Maven, Lombok. This demo Spring Boot application is a updated version that provides high structure of Kafka messaging framework implementation with the Producer and Consumer configuration with the Kafka Template, Kafka Annotations.

Kafka program to ingest wikimedia's real time streaming data into MySQL for data analytics

Learn about Kafka

Apache Kafka Java producer from Digital Wristband (fake data)

Apache Kafka producer app with spring-cloud-stream-kafka

A parameterized Kafka Producer in Apache Flink

.NET 8 Microservices Architecture with CQRS and Event Sourcing supported by Kafka. Strictly CQRS pattern, Mediator pattern from scratch, DDD concepts, Event Store with MongoDB, Database-Per-Service Pattern, Replaying events from store implementation.

This project integrates stock market analysis, tweet sentiment analysis, and stock price forecasting using ARIMA and GRU. It fetches data from MySQL, MongoDB, and streams via Kafka. YCSB benchmarks MySQL and MongoDB performance. The system predicts stock prices and analyzes public sentiment.

A Spring boot application leveraging Apache Kafka to demonstrate the implementation of multiple producers and consumers for both String and JSON messages. The application utilizes MySQL as the underlying database to store efficiently processed and consumed messages.