Operations | Monitoring | ITSM | DevOps | Cloud

Internal developer platforms (IDPs) have become a necessity for software development in today's ever-changing technological landscape. These platforms not only support engineering team velocity and business product strategies but also enhance communication and information flow, impacting successful product launches.

This second last part of the “Implementing Backstage” series explains how to use the Kubernetes plugin in Backstage using real-world scenarios. The previous installments covered getting started, using the core features, integrating with existing tools using plugins, and security and compliance. If you’re entirely new to Backstage and want to learn more, you can read the first entry in the “Evaluating Backstage” series.

This final part of the “Implementing Backstage” series focuses on how to deploy Backstage on Kubernetes. This tutorial is a direct continuation of Using the Kubernetes Plugin in Backstage, which you should complete before tackling this one. The other installments in this series covered getting started, using the core features, integrating with existing tools using plugins, and security and compliance.

The all-new Galileo SMARTboards open the possibility of creating your own personalized decision boards with unprecedented control and flexibility to make the best strategic and tactical decisions for your organization. Now, that’s SMART.

Amazon Simple Storage (S3) has become a cornerstone in the world of cloud computing, offering scalable and secure object storage solutions for a wide range of applications. However, as data accumulates over time, managing it in an efficient manner becomes a challenge. This is where S3 Lifeycle Rules shine. These rules allow us to automate the transition of data between different storage classes and even schedule automatic deletions, which allows users to optimize costs and operational efficiency.

Kubernetes integrations are now available for AutoSys, dSeries, and Automic Automation. It wasn’t that long ago that teams in many organizations started dipping their toes into the world of containers and microservices. It didn’t take long for this approach to application development and orchestration to take hold, and for Kubernetes to emerge as a dominant, broadly used technology.

As software engineers, we all know that troubleshooting often involves sifting through heaps of data points — scanning metrics, reading logs, checking resource status and analyzing events. We manually connect the dots, and if we're experienced enough, we might spot an issue that's about to become a problem. At StackState, we've faced these same challenges.