The age-old dilemma of privacy and security vs. productivity pops up for developers every time they consider introducing a new technology to their stack. The dilemma is often viewed as a trade off: on one hand, privacy and security measures can slow down how quickly new features can be rolled out; on the other hand, prioritizing productivity and business enablement over privacy and security can increase the risk of breaches to an organization.

Organizations increasingly deploy and manage their applications using Kubernetes, which has emerged as the de facto standard for container orchestration. Monitoring these resources becomes critical for ensuring that the applications running on the platforms remain healthy.

Observability is a term that has been thrown around a lot in the past few years in the software development industry. Different people use it in different ways, but one thing that is clear is that it attempts to provide a solution to a real pain engineers are feeling. It is the pain of not knowing what is happening in the microservices architecture and how and why systems are behaving in production.

Applications are increasingly switching from the traditional monolithic design to a modern microservices-based design with several operational benefits. However, it also introduces challenges as conventional methods for collecting metrics and logs become ineffective due to the application design’s distributed nature.

Microservices architecture has become the new norm for modern applications due to its numerous advantages compared to traditional monolithic architecture. However, microservices also come with several challenges. Especially when it comes to observability, traditional monitoring tools and techniques can no longer handle microservices’ distributed and dynamic nature.

Python app monitoring and debugging can be challenging. Using distributed tracing and OpenTelemetry visualization minimizes MTTR and improves developer experience, as seen in this article.

APIs provide a way to simplify development, reduce costs, and create more flexible and scalable applications. Much of today’s development relies on APIs – in the integration of third-party services, in the communication between microservices, in mobile app development, and in other use cases. Some APIs even exist as products themselves for customers to use.

Adopting a distributed tracing solution to make an application more observable and maintainable is one of the most common key initiatives modern R&D teams have on their plates currently. With the move to microservices architectures, development teams are finding that it’s taking them longer to build applications due to tasks that are growing in complexity.

OpenTelemetry (OTel in short) is an open-source observability framework that provides a standard set of vendor-agonistic SDKs, APIs, and tools to connect with observability backends. It supports all major programming languages, including Java, Python, Node.js, and Go. However, Golang tracing by integrating OTel with Golang is particularly challenging due to several reasons.

OpenTelemetry (OTel) and distributed tracing can be handy tools when you’re a developer facing what we call app flow blindness – or not being able to see your application flows and microservices components in a distributed cloud environment. In distributed environments, application flows are handled by various services and cloud entities which are generally siloed.