In today's digital age, organizations increasingly depend on their technology infrastructure to keep their operations running smoothly. These infrastructures include servers, networking equipment, IoT devices, and applications. The data generated by all this infrastructure (logs, metrics, traces) is known as telemetry data, which has a tremendous potential value to organizations. However, it can be challenging to control telemetry data and utilize it effectively.

Observability has become one of the most important areas of your application and infrastructure landscape, and the market has an abundance of tools available that seem to do what you need. In reality, however, most products - especially leading open source tools - were created to solve a single problem extremely well, and have added additional supporting functionality to become a more robust solution; but the non-core functionality is rarely best of breed. Examples of these are Prometheus and Grafana.

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.

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.

Gain executive-level insights on Cisco AppDynamics’ for on-premises, hybrid and cloud customers from Ronak Desai, Cisco SVP & GM AppDynamics and full-stack observability.

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.