#typescript #javascript #go #opensource #kafka #reactjs #metrics #prometheus #self_hosted #tracing #stream_processing #druid #gsoc #kafka_streams #observability #distributed_tracing #application_monitoring #jaegertracing #opentelemetry #open_telemetry

https://github.com/SigNoz/signoz

#go #agent #cloudnative #kubernetes #log #opentelemetry

https://github.com/loggie-io/loggie

#csharp #cqrs #ddd #docker #dotnet #dotnetcore #ef_core #eventstore #grpc #kube #masstransit #microservices #mongo_driver #opentelemetry #redis #tye #vertical_slice_architecture #yarp

https://github.com/meysamhadeli/booking-microservices-sample

#erlang #demo_app #jaeger #opentelemetry #opentelemetry_collector #opentelemetry_dotnet #opentelemetry_erlang #opentelemetry_go #opentelemetry_java_agent #opentelemetry_javascript #opentelemetry_python #opentelemetry_rust #prometheus

https://github.com/open-telemetry/opentelemetry-demo

#csharp #aspnetcore #authentication #dotnet_core #jwt #opentelemetry #rest_api

https://github.com/davidfowl/TodoApi

#go #hacktoberfest #kubernetes_operator #opentelemetry #opentelemetry_collector

https://github.com/open-telemetry/opentelemetry-operator

#go #metrics #monitoring #observability #open_telemetry #opentelemetry #telemetry

The OpenTelemetry Collector is a tool that helps you manage telemetry data easily. It can receive, process, and export data from various sources like Jaeger and Prometheus to different back-ends without needing multiple agents. Here are the key benefits It comes with reasonable default configurations and supports popular protocols, making it easy to use out of the box.
- **Performant** It is designed to be easily monitored.
- **Extensible** It supports traces, metrics, and logs in a single codebase.

This makes it simpler to manage your telemetry data in a unified and efficient way.

https://github.com/open-telemetry/opentelemetry-collector

#go #cncf #distributed_tracing #hacktoberfest #jaeger #observability #opentelemetry #tracing

Jaeger is a tool that helps you understand how different parts of your software work together. It's like a map that shows where data goes and how long it takes to get there. This helps you find and fix problems faster. Jaeger is free and open source, meaning anyone can use and improve it. It's supported by a big community and has clear guides on how to get started and contribute. Using Jaeger can make your software run more smoothly and efficiently.

https://github.com/jaegertracing/jaeger

#python #cloud_native #cncf #deep_learning #docker #fastapi #framework #generative_ai #grpc #jaeger #kubernetes #llmops #machine_learning #microservice #mlops #multimodal #neural_search #opentelemetry #orchestration #pipeline #prometheus

Jina-serve is a tool that helps you build and deploy AI services easily. It supports major machine learning frameworks and allows you to scale your services from local development to production quickly. You can use it to create AI services that communicate via gRPC, HTTP, and WebSockets. It has features like built-in Docker integration, one-click cloud deployment, and support for Kubernetes and Docker Compose, making it easy to manage and scale your AI applications. This makes it simpler for you to focus on the core logic of your AI projects without worrying about the technical details of deployment and scaling.

https://github.com/jina-ai/serve

#typescript #apm #application_monitoring #distributed_tracing #go #good_first_issue #jaeger #log #logs #metrics #monitoring #nextjs #observability #open_source #opentelemetry #prometheus #react #reactjs #self_hosted #tracing #typescript

SigNoz is a tool that helps you monitor and troubleshoot your applications easily. It combines logs, metrics, and traces in one place, allowing you to spot issues before they happen and fix problems quickly. It's cost-effective and open-source, similar to Datadog and New Relic but without the high costs. With SigNoz, you can monitor application performance, manage logs efficiently, track user requests across services, create customized dashboards, and set alerts for unusual activities. This makes it easier to identify and solve problems quickly, ensuring your application runs smoothly.

https://github.com/SigNoz/signoz

#go #framework #go #go_framework #golang #golang_framework #grpc #grpc_go #grpc_golang #hacktoberfest #http_server #logging #metrics #microservice #microservice_framework #opentelemetry #performance #rest_api #server #tracing #web_framework

GoFr is a microservice development framework designed to simplify the creation and management of microservices. It offers features like easy API syntax, built-in observability, and support for REST and gRPC, making it efficient for developers. With GoFr, you can focus on building your application without getting bogged down by complex configurations. The framework also supports Kubernetes deployment, ensuring your services are scalable and maintainable. By using GoFr, you can accelerate your development process, improve application performance, and enhance overall productivity, allowing you to deliver high-quality software faster.

https://github.com/gofr-dev/gofr

#csharp #architecture #aspnetcore #clean_architecture #cqrs #ddd #dotnet #dotnetcore #event_driven_architecture #event_sourcing #kubernetes #masstransit #messaging #microservice #microservices #oauth2 #opentelemetry #software_architecture #software_design #software_engineering #vertical_slice_architecture

Migrating from a monolithic architecture to a cloud-native microservices architecture offers several benefits. It improves scalability, allowing different parts of the application to grow independently. This approach also enhances reliability by isolating faults, so if one service fails, others continue to work. Additionally, microservices enable faster deployment and updates, as each service can be developed and deployed separately. This flexibility allows teams to use the best technology for each service, making development more efficient and agile[2][3][5].

https://github.com/meysamhadeli/monolith-to-cloud-architecture

#typescript #cli #clustering #concurrency #dependency_injection #effect #error_handling #javascript #observability #opentelemetry #platform #schema #typescript #workflows

Effect is a powerful TypeScript framework that helps you build reliable and complex applications by managing side effects like logging, network calls, and database operations in a safe and organized way. It uses a core `Effect` type to describe workflows that are lazy, composable, and type-safe, allowing you to handle errors and dependencies explicitly. The framework is modular, with many packages for AI, CLI tools, distributed computing, SQL databases, and more, making it flexible for various needs. Using Effect improves code quality, concurrency handling, and maintainability, helping you write robust TypeScript apps efficiently[1][2][4][5].

https://github.com/Effect-TS/effect

#go #logging #metrics #opentelemetry #tracing

OpenTelemetry-Go is a tool for Go applications that helps you track how your software performs by collecting data like traces and metrics, then sending this information to monitoring platforms so you can see what’s happening inside your app in real time[2][3][4]. It works on many operating systems and Go versions, and you can use it by adding a few lines of code to your app and setting up an exporter. This makes it much easier to find and fix problems, understand how your app is running, and keep everything reliable and fast[2][3][4].

https://github.com/open-telemetry/opentelemetry-go

#go #open_telemetry #opentelemetry

The OpenTelemetry Collector Contrib is a collection of extra components that extend the core OpenTelemetry Collector, helping you collect, process, and export telemetry data like traces, metrics, and logs from your applications. It supports many features such as filtering sensitive data, batching, retries, and custom processing, which improve security, reliability, and performance of your observability pipeline. You can build custom distributions using these components to fit your needs. This helps you monitor complex systems more easily, reduce costs, and maintain flexibility by supporting many data formats and backends without changing your application code. It is maintained by a community of experts ensuring quality and support.

https://github.com/open-telemetry/opentelemetry-collector-contrib