Operations | Monitoring | ITSM | DevOps | Cloud

Splunk Observability is incredibly good at details! Many of us use it as a metaphorical microscope through which we observe our software. But how do you observe the long-term trends and usage of that microscope? There are numerous organization-level metrics provided in Splunk Observability that can be used to chart organization-level concerns. These can be leveraged in various ways to understand things like uptake, billing and just how much value Observability is providing.

The Spring framework is a robust server-side framework for modern Java-based enterprise applications. Since its introduction in 2003, its advantages have made it one of the most dominant server-side frameworks among many organizations. According to a research study by Snyk in 2020 on the usage of the server-side web frameworks, 50% of the respondents have said they use Spring Boot, and 31% of the respondents use Spring MVC.

“Overwhelming.” It was the only word Grafana Labs CEO and Co-founder Raj Dutt could use to describe how it felt to look out at the sea of more than 600 Grafanistas gathered together in Whistler, British Columbia, for the first company-wide employee event in two years.

“Too many cooks spoil the broth.” It’s an old saying we’ve heard many times in childhood. If we put it in today’s IT monitoring context, we could change it to “too many tools spoil the insights and efficiency.” IT teams across organizations have deployed multiple tools over the decades to monitor and track the performance of networks, databases, and applications and to ensure the smooth running of the business.

In the old days, the most senior members of an engineering team were the best debuggers. They had built up such an extensive knowledge about their systems that they instinctively knew the right questions to ask and the right places to look. They even wrote detailed runbooks in an attempt to identify and solve every possible issue and possible permutation of an issue.

IT teams have been relying on observability tools to (theoretically) provide intelligence and insights into operating conditions within an organization’s digital infrastructure for years. But most of these tools have come with significant shortcomings that leave IT teams wanting more.

Observability is a mindset that lets you use data to answer questions about business processes. In short, collecting as much data as possible from the components of your business — including applications and key business metrics — then using an AI-powered tool to help consolidate and make sense of this huge volume of data gives you observability into your business. Having observability for your business and applications lets you make smarter decisions, faster.

Recently I heard one of our prospects talk about a competitor who was promoting their data lake and ask, how are we different than that? His question got me thinking about why a data lake alone does not provide the depth of observability you really need. The goal of observability is to help SREs, IT Ops and DevOps teams run their IT systems with close-to-zero downtime. Consolidating data from across your environment into a data lake is certainly a good step.