Operations | Monitoring | ITSM | DevOps | Cloud

In today's fast-paced digital landscape, ensuring the reliability and performance of your applications is paramount. Heroku, a cloud-based Platform-as-a-Service (PaaS), simplifies application deployment and scaling. However, to fully leverage Heroku's capabilities, effective monitoring is essential. This guide delves into best practices for monitoring Heroku applications, providing context, practical steps, and unique insights to enhance your observability strategy.

We recently explored what the cloud is and how it has evolved over 60 years. Yet, cloud computingWe recently explored the cloud and how it has evolved over 60 years. Yet, cloud computing emerged into what it is today in 2002, when Amazon, the e-commerce giant, sought a more efficient way to expand its online marketplace. So, it is unsurprising that the company’s cloud services subsidiary (AWS), which launched in 2006, is today’s largest Cloud Service Provider (CSP).

As organizations move beyond traditional hypervisors, Harvester continues to lead the way as an open source, Kubernetes-native virtualization solution. With the release of Harvester 1.5, users now have greater flexibility and ecosystem alignment than ever before—with General Availability (GA) for ARM64 and support for CSI-compatible storage backends. Harvester is part of a growing shift toward cloud-native infrastructure that unifies VMs and containers under the Kubernetes API.

We work in a digital landscape where IT teams are expected to move faster, scale smarter, and deliver flawless digital experiences, automation is no longer optional; it’s foundational. But while most organizations have started down the automation path, far fewer can answer the critical question: How do we measure success? The problem isn’t just a lack of metrics. It’s that the wrong metrics are still steering the ship.

The world is changing every day. From geopolitical shifts to legislation like GDPR which requires localized processing – these all create a complex and uncertain landscape where data storage, processing, and cloud services could potentially come to a sudden halt or suffer heavy disruption overnight. As a result, organizations are increasingly interested in potential routes for shifting cloud services to safer alternatives closer to their country of operation.

Both Grafana Tempo and Jaeger are distributed tracing tools designed for modern microservice architectures. Jaeger, released as an open-source project by Uber in 2015, has matured into a graduated CNCF project. Tempo, announced by Grafana Labs in October 2020, is a newer entrant focused on high-volume tracing with a unique storage architecture. Before comparing these tools in detail, let's quickly review what distributed tracing is and why it matters.

When something breaks in production, logs are usually where you start. They help you figure out what happened, where, and why. But with microservices architecture, logging isn't simple anymore. In a traditional monolithic application, logs live in one place. With microservices, they're scattered across multiple services, containers, and sometimes even data centers. What used to be a simple grep command now feels like solving a mystery without most of the clues.