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.
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.
