This post was written by Siddhant Varma. Scroll down for the author’s bio. Observability is an essential aspect of a healthy software architecture and a highly performant system. It enables developers and engineers to understand and dive deeper into how their application behaves. This in turn helps them monitor it effectively.

Message brokers like Kafka enable microservices to scale. But this same quality makes them hard to troubleshoot. How can developers avoid messages and errors getting stuck in oblivion? In this post we look at a few solutions: Kafka Owl, Redpanda, and Helios.

As the trend toward microservices-based architectures continues to gain momentum, it’s becoming increasingly clear that distributed tracing will be a crucial tool for monitoring and debugging these complex systems in the future. When designing a microservices-based architecture, breaking extensive services into smaller, more manageable components is standard practice. Communication between these components becomes crucial, but finding the root cause can be challenging when issues arise.

Honeycomb recently released our Query Assistant, which uses ChatGPT behind the scenes to build queries based on your natural language question. It's pretty cool. While developing this feature, our team (including Tanya Romankova and Craig Atkinson) built tracing in from the start, and used it to get the feature working smoothly. Here's an example. This trace shows a Query Assistant call that took 14 seconds. Is ChatGPT that slow? Our traces can tell us!

This post was written by Mercy Kibet, a full-stack developer with a knack for learning and writing about new and intriguing tech stacks. In today’s digital world, software applications are becoming increasingly complex and distributed, making it more challenging than ever to diagnose and troubleshoot issues when they arise.

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.

While more observability vendors are providing tracing ingestion and visualization as part of their core service, only Coralogix, the leading in-stream observability platform, supports a set of data optimization features that drive down cost, maximize insights and create a scalable tracing strategy unlike others.

How to filter metrics by labels using OpenTelemetry Collector.