March 9, 2020
In 2006, I was running an enterprise application development lab that about 300 people were dependent on, and I was struggling to keep things running smoothly: there were constant problems with the network, the storage, and the systems that ran everything. In the middle of searching Google for info on a problem I was troubleshooting, an ad popped up for a product called Splunk that promised a sort of grep on steroids for log data. Intrigued, I downloaded and tried it, and an epiphany struck. Immediately, I was able to see problems in the environment that I had been missing. I bought my first 2GB license shortly thereafter and began my “machine data journey”.
By way of introduction, I’m Steve Litras, and I’m the new Lead Evangelist at Cribl. For the last 15 years, I’ve been focused on solving IT problems across the board (storage, hosting, enterprise applications, etc.) for a company called Autodesk (If you have ever bought a new car, remodeled your home, or watched a movie with great special effects, know it or not, you’ve seen what Autodesk customers do with Autodesk’s products). During most of that time, I was an advocate of using our log data to streamline IT Operations, solve problems, and understand the efficacy of our environments.
At Autodesk, we ran into the same problems that many Splunk customers do – our volume of log data and our desire to retain that data was much larger than our budget could support. We scaled our Splunk license continuously but always seemed to be turning off the ingestion of large chunks of logs to ensure that we’d not exceed our license. We looked at alternatives, like Elastic, that allowed us to make different tradeoffs (e.g. ingest everything, but retain less), and we never were quite able to really get the value out of our investment that I knew was possible.
Last year, I was introduced to Cribl LogStream, and had that same kind of epiphany I had that first day with Splunk. I immediately saw that it was a way to help me get the most important data into the right tool and get the most value out of those tools, while at the same time meeting my retention requirements at minimal cost and opening up new uses for the data I was collecting. As I engaged more with the Cribl team and came to understand the vision for the product, my enthusiasm grew.
When I decided, late last year, that it was time for me to move on from Autodesk, Cribl was one of a couple of companies that I was really interested in joining. After some initial conversations with the leadership team and a chance to understand the long term vision and the roadmap, it became obvious to me that this is where I want to be. The role that Clint suggested, the one I’m now in, gives me the opportunity to advocate for something I’m passionate about – the value of machine data to a modern enterprise, and hopefully bring an additional “voice of the customer” to the company.
You’ll be seeing content from me around solving business problems and improving operational capabilities with machine data. I’ll be focusing on content and solutions from a business perspective to complement the great technical content about the product that our founders and my other colleagues create.