Operations | Monitoring | ITSM | DevOps | Cloud

Cribl CEO Clint Sharp first announced Cribl Edge in March of 2022. Our SVP of Marketing, Abby Strong, complemented the announcement with a well-rounded blog post discussing why Cribl Edge is the first fully manageable and auto-configurable agent designed to collect telemetry data at scale. Even Aerosmith gave the product a shoutout! Well, not really, but wouldn’t it be fun if it was true? 🙂 We’re thrilled to be back with exciting news about the latest release of Cribl Edge (4.0).

A few weeks back, I got the chance to sit down with our very own Jordan Perks from the Cribl Customer Success Team. Jordan is an Observability subject matter expert AND knows a thing or two about Cribl Products! After geeking out a bit about data best practices, we started chatting about enabling our customer champions to have different conversations with stakeholders across their organizations. When someone becomes an observability engineer, they step into a much different role.

If you’re unfamiliar with observability, you might think an “observability engineer” is just a fancy way to say data admin — but while observability engineers often work with data admins, they work toward different goals. Data admins monitor information to identify and fix known security issues. Observability engineers work to provide a complete picture of all the data a company aggregates and what it means for a business.

The best part of my job is talking to you, our prospects, and customers, about your logging and data practices. I love listening to what you are doing and hope to accomplish, so I can get a sense of the end state. My goal is to brainstorm solutions that provide overall value across the enterprise, and not just aim for a narrow tactical win with limited impact. In late September, I hung out at a local DevOps conference in Brooklyn with the NYC Cribl sales team.

How enterprises store and split up observability and security data is a great analogy to how lint, spare change, and partially-eaten bags of popcorn end up under couch cushions. Or when you tell your kids to clean up the house when company is coming over and they stash their toys and your tools in various nooks and crannies.

In part 1 of this series, we talked about zombie data and what it means for your observability architecture. In this post, we’ll talk more about how to handle all of it. How well can your organization handle the firehose of data it’s collecting? Yes, you have the ability to collect it, but chances are you don’t have the financial or human resources available to analyze all of it effectively.

We’ve reached the point where our ability to collect data has actually exceeded our ability to process it. Nowadays, it’s commonplace for organizations to have terabytes or even petabytes worth of data sitting in storage, waiting patiently for well-intentioned systems admins to eventually analyze it.