Observability is a critical step for digital transformation and cloud journeys. Any enterprise building applications and delivering them to customers is on the hook to keep those applications running smoothly to ensure seamless digital experiences. To gain visibility into a system’s health and performance, there is no real alternative to observability. The stakes are high for getting observability right — poor digital experiences can damage reputations and prevent revenue generation.

If you’re in the cloud engineering and DevOps space, you’ve probably seen the name OpenSearch a lot over the last couple of years. But, what is your current understanding of OpenSearch, and the components around it? Let’s take a closer look.

Cloud infrastructure and application monitoring dashboards are critical to gaining visibility into the health and performance of your system. But what are the best metrics to monitor? What are the best types of visualizations to monitor them? How can you ensure your alerts are actionable? We answered these questions on our webinar Build the Ultimate Cloud Monitoring Dashboard.

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.

With the current financial climate, cost reduction is top of mind for everyone. IT is one of the biggest cost centers in organizations, and understanding what drives those costs is critical. Many simply don’t understand the cost of their Kubernetes workloads, or even have observability into basic units of cost. This is where FinOps comes into play, and organizations are beginning to implement those best practice standards to understand their cost.

The MongoDB document-oriented database is one of the most popular database tools available today. Developed as an open-source project, MongoDB is highly scalable and can be set up in your environment in just a few simple steps. When running and managing databases, monitoring is a key requirement.

Over the past decade, Prometheus has become the most prominent open source monitoring tool in the world, allowing users to quickly and easily collect metrics on their systems and help identify issues in their cloud infrastructure and applications. Prometheus was originally developed by SoundCloud when the company felt their metrics and monitoring solutions weren’t meeting their needs.

Kubernetes has come a long way, but the current state of Kubernetes open source monitoring is in need of improvement. This is in part due to the issues related to an unnecessary volume of data related to that monitoring. For example, a 3-node Kubernetes cluster with Prometheus will ship around 40,000 active series by default. Do we really need all that data?

How is your organization handling Kubernetes observability? What tools are you using to monitor Kubernetes? Is it a time-consuming, manual process to collect, store and visualize your logging, metrics and tracing data? And, what are you actually getting out of all that investment? At Logz.io we’re trying to make this process easier for customers who are serious about Kubernetes observability. We’ve made significant investments in this area for Kubernetes use cases.

Observability data provides the insights engineers need to make sense of increasingly complex cloud environments so they can improve the health, performance, and user experience of their systems. These insights can quickly answer business-critical questions like, “what is causing this latency in my front end?” Or, “why is my checkout service returning errors?” Observability is about accessing the right information at the right time to quickly answer these kinds of questions.