Operations | Monitoring | ITSM | DevOps | Cloud

Observability, monitoring, and telemetry are crucial for maintaining the performance and reliability of modern systems. Their concepts are often used interchangeably, but they have distinct differences that are important to understand. In this blog, we’ll explore each concept in detail, including key characteristics and examples of tools. We’ll also compare observability vs monitoring vs telemetry and discuss when it’s appropriate to use each.

If you were asked to evaluate how good crews were at fighting forest fires, what metric would you use? Would you consider it a regression on your firefighters’ part if you had more fires this year than the last? Would the size and impact of a forest fire be a measure of their success? Would you look for the cause—such as a person lighting it, an environmental factor, etc—and act on it? Chances are that yes, that’s what you’d do.

Observability has become increasingly important for IT professionals as the complexity of modern systems has grown. In the past, IT environments were typically composed of a few servers and applications that were all running on-site. However, with the rise of cloud computing, IT has become more distributed, with applications and services running on a wide variety of infrastructure and platforms.

Data routing is a crucial but complex task for companies of all sizes. Ensuring that the right data is sent to the right tools can be a time-consuming and difficult process, and when things go wrong, it can have costly consequences. This is why having a robust data routing strategy is essential for any organization.

Whether you’ve been following along with our Authors’ Cut series or doing some self-paced learning, our O’Reilly book Observability Engineering is one of the best resources for jumpstarting your observability journey. It serves as a blueprint to help you understand and map out the technical and cultural requirements of implementing observability into your organization.

Not to put too fine a point on it, but we think distributed tracing gets a very bad rap for being too complicated and labor-intensive. We’re here to show you three ways you can jumpstart a distributed tracing effort, starting small and expanding as it makes sense. These examples involve only a little code and perhaps a bit of a mindset change. Starting small with distributed tracing can even be fun, because who doesn’t like getting customized results without much work?

A new year has started and I've been pondering my hopes and dreams for the year to come. In the world of SRE, observability is the most prominent pillar of my work. So, I decided to drill into the topic of observability and what I'd like to see happen in the industry in 2023. Rather than focusing on any tool, technology, or methodology, I'lll be exploring concepts that can be broadly applied in any organization.

Elastic Observability 8.6 introduces a set of capabilities improving production operations through the introduction of host (EC2/GCP compute/Azure compute) observability, application dependency operations views (insights into databases, caches, etc), and a new connector for Opsgenie. These new features allow customers to: Elastic Observability 8.6 is available now on Elastic Cloud — the only hosted Elasticsearch offering to include all of the new features in this latest release.

While IT pros know they need to monitor IT services, they also know it can be the most difficult part of their job. Traditionally, enterprises have cobbled together several disparate monitoring products to address all their monitoring needs – but there are often gaps. Within these gaps, issues are missed, and the possibility of proactive issue resolution becomes nearly impossible.

I recently attended a virtual event and heard the speaker comment, “Relational databases don’t scale.” To my ears, this is about as silly a statement as saying, “No one can eat 26 hot dogs in 12 minutes” right before Kobayashi shows up and eats 50. In my experience, relational databases scale when they’re placed in the hands of someone who knows what they’re doing. Just imagine if Kobayashi was your data architect!