#cplusplus #mysql #redis #http #embedded #kafka #tasking #asychronous #io_cpu

https://github.com/sogou/workflow

#shell #docker #docker_compose #kafka #kafka_rest_proxy #kafka_schema_registry #kafka_topics_ui #zookeeper

https://github.com/conduktor/kafka-stack-docker-compose

#go #amqp #cloudnative_services #containers #devops #devops_tools #golang #grpc #kafka #kubernetes #microservice #microservices #microservices_application #mizu #redis #rest #traffic_viewer #visibility

https://github.com/up9inc/mizu

#typescript #apache_kafka #big_data #cluster_management #event_streaming #hacktoberfest #kafka #kafka_brokers #kafka_client #kafka_cluster #kafka_connect #kafka_manager #kafka_producer #kafka_streams #kafka_ui #opensource #streaming_data #streams #web_ui

https://github.com/provectus/kafka-ui

#java #amqp #gelf #graylog #hacktoberfest #kafka #log_analysis #log_collector #log_management #log_viewer #logging #logging_server #secure_logging #security #siem #syslog

https://github.com/Graylog2/graylog2-server

#go #confluent #consumer #golang #golang_bindings #golang_library #kafka_client #librdkafka #producer

https://github.com/confluentinc/confluent-kafka-go

#java #ai #database #hadoop #kafka #ml

https://github.com/linkedin/venice

#go #asynq #dtm #elasticsearcg #filebeat #gitlab #go_queue #go_stash #go_zero #goctl #golang #gozero #grafana #harbor #jaeger #jenkins #k8s #kafka #microservices #modd #prometheus

https://github.com/Mikaelemmmm/go-zero-looklook

#python #asyncapi #asyncio #distributed_systems #fastkafka #faststream #hacktoberfest #hacktoberfest2023 #kafka #nats #propan #rabbitmq

https://github.com/airtai/faststream

#go #architecture #argocd #best_practices #codegen #csi_driver #ddd #ddd_sample #event_sourcing #example #gitops #golang #helm_chart #k8s #kafka #kubernetes #layered_architecture #microservices #mq #shortlink

https://github.com/shortlink-org/shortlink

#python #batch_processing #kafka #machine_learning_algorithms #pathway #real_time #streaming

https://github.com/pathwaycom/pathway

#go #client #golang #kafka #kafka_client

https://github.com/twmb/franz-go

#go #cqrs #event_driven #event_sourcing #events #go #golang #kafka #nats #rabbitmq #reactive #sagas #stream_processing #watermill

Watermill is a tool for working with message streams in Go. It helps you build event-driven applications easily and efficiently. You can use it with various messaging systems like Kafka, RabbitMQ, or even HTTP and MySQL. Watermill is designed to be easy to understand, fast, flexible, and resilient. It provides many examples and a getting started guide to help you get going quickly. Using Watermill, you can handle messages in a simple way, similar to how you work with HTTP requests, making it easier to build distributed and scalable services without needing deep knowledge of complex systems. This makes it beneficial for developers who want to focus on their application logic rather than the underlying messaging infrastructure.

https://github.com/ThreeDotsLabs/watermill

#go #gnmi #golang #influxdb #json #kafka #logs #metrics #modbus #monitoring #mqtt #opcua #telegraf #time_series #windows_eventlog #windows_management_instrumentation #xpath

Telegraf is a tool that helps collect, process, and send various types of data like metrics, logs, and more. It has over 300 plugins for different tasks such as system monitoring, cloud services, and messaging. You can easily configure it using TOML, and it runs as a standalone binary without extra dependencies. This makes it easy to set up and use. With Telegraf, you can choose plugins to monitor your devices, logs, networks, and more, making it very flexible and powerful for managing your data efficiently.

https://github.com/influxdata/telegraf

#cplusplus #consul #dag #http #kafka #mysql #redis #sogou #tasking

Sogou C++ Workflow is a powerful tool for building back-end services in C++. It supports creating HTTP servers, asynchronous clients for protocols like HTTP, Redis, MySQL, and Kafka, and even custom protocols. You can use it to build complex workflows, parallel computing tasks, and high-performance services with ease. It works on various platforms including Linux, macOS, Windows, and more. The benefit to you is that it simplifies the development of robust and efficient back-end services, allowing you to focus on your business logic without worrying about the underlying complexities.

https://github.com/sogou/workflow

#java #kafka #scala

To use Apache Kafka, you need to have Java installed. Here’s what you can do Use commands like `./gradlew jar` to build a jar file and follow the quickstart guide for running Kafka.
- **Testing** Use tools like Checkstyle and Spotbugs to ensure your code meets standards.
- **Dependency Management** You can contribute to Kafka by following the guidelines on the Apache Kafka website.

This helps you build, test, and maintain high-quality code for Apache Kafka, making it easier to work with the project.

https://github.com/apache/kafka

#java #batch #cdc #change_data_capture #data_integration #data_pipeline #distributed #elt #etl #flink #kafka #mysql #paimon #postgresql #real_time #schema_evolution

Flink CDC is a tool that helps you move and transform data in real-time or in batches. It makes data integration simple by using YAML files to describe how data should be moved and transformed. This tool offers features like full database synchronization, table sharding, schema evolution, and data transformation. To use it, you need to set up an Apache Flink cluster, download Flink CDC, create a YAML file to define your data sources and sinks, and then run the job. This benefits you by making it easier to manage and integrate your data efficiently across different databases.

https://github.com/apache/flink-cdc

#java #data_stream #data_streaming #data_streams #hacktoberfest #kafka #kafka_connect #kafka_streams #kubernetes #kubernetes_controller #kubernetes_operator #messaging #openshift

Strimzi helps you run Apache Kafka on Kubernetes or OpenShift easily. It provides quick start guides, detailed documentation, and a community support system. You can get help through Slack, mailing lists, or GitHub discussions. Strimzi also allows you to contribute by fixing issues, improving documentation, or participating in community meetings. This makes it easier to manage and use Kafka clusters in a cloud-native environment, which is beneficial for users who need reliable and scalable messaging systems.

https://github.com/strimzi/strimzi-kafka-operator