In data management, numerous roles rely on and regularly use telemetry data. The developer is one of these roles. Developers are the creative masterminds behind the software applications and systems we use and enjoy today. From conception to finished product, they map out, build, test, and maintain software.
Incidents happen. What matters is how they’re handled. Most organizations have a strategy in place that starts with log searches—and logs/log searching are great, but log searching is also incredibly time consuming. Today, the goal is to get safer software out the door faster, and that means issues need to be discovered and resolved in the most efficient way possible.
StackState commissioned Techstrong Research, a strategy and technology analyst firm, to delve into the current state of observability. The resulting report, “Observability Innovation Report 2023,” provides insightful information. 543 IT professionals were surveyed, globally, across 20 industries. The largest concentration of respondents were in the telecommunications, technology, Internet and electronics sectors, followed by financial services.
Observability has become a bit of a buzzword in the industry for the last few years. Exactly what "observability" means depends on who you ask, but most people would agree its about both: There's plenty of content out there telling you how to implement observability, or what good looks like. But what about bad observability? What are some anti-patterns to watch out for?
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.
Moving to the cloud is hard. Moving to the cloud and keeping systems secure, data governed, compliances met, and cyberattacks at bay, makes everyone’s jobs significantly harder. The number one concern we hear from Cribl customers about the cloud is, you guessed it — security. If you’re in this boat — eager to adopt the cloud ASAP but also worried about the risks that come with having sensitive data in the cloud — don’t fret. We’re here to help.
Elastic Observability provides a full-stack observability solution, by supporting metrics, traces, and logs for applications and infrastructure. In a previous blog, I showed you how to monitor your AWS infrastructure running a three-tier application. Specifically we reviewed metrics ingest and analysis on Elastic Observability for EC2, VPC, ELB, and RDS.
Most SaaS products have nice, organic growth when they work well. Employees log in, they click around and make stuff, then they share links with others who do the same. After a few weeks or months, there are thousand of objects. Some are abandoned, and some are mission-critical. Different people also bring different perspectives, so they name things that are relevant to their role and position in the team, which may be confusing to others outside their realm.
Not long ago, we announced the launch of Honeycomb’s Service Map, a new feature that gives users the ability to get an overall, filterable view of their system and how everything is connected, along with some exciting new enhancements to BubbleUp. What’s the story behind these changes? They make it even easier for developers to zero-in on issues, even when they are hidden in billions of lines of code.
Baking a delicious pizza in a wood-fired oven requires a combination of skill, experience and the right tools. The same is true for achieving optimal observability in a Kubernetes environment. In this post, we'll explore some of the lessons learned from baking pizza in a wood-fired oven and apply them to the world of Kubernetes observability.
