Operations | Monitoring | ITSM | DevOps | Cloud

Effective troubleshooting and resolution of critical production issues require DevOps and R&D teams to utilize logging and observability. However, selecting the right logging solution can be challenging, given the wide range of available options and associated costs. Additionally, the strategy for logging usage should be tailored to the needs of different personas and use cases, such as DevOps engineers versus developers.

In modern times, building and publishing an application has become very easy with Cloud-based deployment. Users don’t need to worry about infrastructure-related challenges like availability, reliability, scalability, etc. The cloud providers are responsible for keeping the deployment flow simple and intact. Providing many advantages and coherence, the high cost incurred for such benefits is the downside.

Grafana 9.4 is here! Get Grafana 9.4 With the latest Grafana release, we’re introducing a wealth of new features and improvements that makes getting started with Grafana even easier and that take your visualizations and observability best practices to the next level. In addition to enabling TraceQL, the new query language for distributed tracing in Grafana Tempo 2.0, for all Grafana Cloud users, the Grafana 9.4 release comes with a fresh round of features.

A cornerstone of network observability is the ability to ask any question of your network. That means having an unbound capacity to explore the tremendous amount and variety of network telemetry you collect. It means seeing trends and patterns from a macro level, but it also means getting very granular to pursue any line of analysis of your data. Collecting information from flow records, SNMP, streaming telemetry, BGP, eBPF, and so on is indeed very important.

Enterprise use of Microsoft’s Azure Cloud Services is expanding at an unprecedented rate as cloud computing usage expands. However, enterprises that have recently switched to Azure continue to have serious concerns about monitoring their applications efficiently. As the business overgrows, it is essential to understand its functioning and health simultaneously.

Have you ever wondered how you can help Kubernetes manage your pods in the most efficient way? Kubernetes can do a decent job “out of the box,” but it can be optimized just like any other system. One such optimization in the Kubernetes world is introducing liveness probes, and in this post, you’ll learn everything about them.

With the dawn of microservices and serverless, event-driven architectures have become the way to go when building a new system in the cloud. This approach has allowed for greater scalability, as the system can easily adapt and respond to changes in traffic or demand without having to overhaul the entire architecture. Additionally the Event-driven approach means your application is mainly concerned with routing event data to the right services.

The current popularity of Redis is well deserved; it’s one of the best caching engines available and it addresses numerous use cases – including distributed locking, geospatial indexing, rate limiting, and more. Redis is so widely used today that many major cloud providers, including The Big 3 — offer it as one of their managed services. In this article, we’ll look at how to monitor Redis performance using Prometheus, the similarly popular open-source monitoring system.

StormForge Optimize Live is a machine learning-powered performance and resource optimization solution for Kubernetes workloads. Optimize Live ingests and analyzes production observability data and recommends specific actions to optimize CPU and memory utilization. You can take these actions manually or set them to occur automatically, making it easier to maintain a high level of application performance while minimizing cloud costs.