Cribl said on Aug. 25 it raised $200 million in a Series C round of funding, bringing total funding to date for the vendor to $254 million.
Based in San Francisco, Cribl develops data pipeline technology that provides data observability to organizations to better understand and organize data. The vendor last updated its flagship platform on June 2 with the general availability of LogStream 3.0.
LogStream acts as a data router in that it processes the data it receives from the source in order to help provides more context and structure before it is forwarded to a destination for analytics or business intelligence.
In this Q&A, Clint Sharp, co-founder and CEO of Cribl, details the challenges and opportunities for data observability and where the vendor is headed next.
Why are you now raising new money for Cribl's data observability efforts?
Clint Sharp: Our belief fundamentally is that we really want to get our story out to the market as rapidly as possible. We have plans to deploy that capital with aggressive expansion plans for North America and for Europe. Those plans include engineering as well as marketing and spending on awareness.
When we're talking about money, we're talking about hiring and going out and adding more people. We started the year at about 65 employees. We're probably going to finish the year with probably 250 or so, and that's huge expansion.
From a customer-first perspective, we'll be using the money to keep providing them value, by helping them get their data to the right place and in the right shape.
What is the data observability lake concept that Cribl is building toward?
Clint SharpCEO and co-founder, Cribl
Sharp: We're working on an emerging concept of an observability lake; it's kind of our new reference architecture for observability.
What we're seeing in the market is a need to not only process massively more data but also utilize cloud storage to store massively greater volumes of data in open formats. By using the cloud and cloud data lakes, that really gives organizations the ability to move data around to any tool at any time.
LogStream has the ability to put data into the lake and to bring data back out of the lake. There are already many different tools for analyzing data in data lakes, including AWS Athena and Databricks, among others. I think the important thing to understand about the lake is that it isn't owned by any one particular vendor.
At Cribl, we're helping organizations bring the data back out of the lake and connect it to any tool that they already have. People are looking to be able to have data in an agnostic format and then have multiple tools come in and analyze that data, no matter which vendor happened to put it there. I think that's a really new concept for the industry, specifically for observability.
What has been the biggest surprise since you started the company in 2017?
Sharp: In the early formation of the company we thought that data observability was a much more solved problem than it was.
Often organizations have more than one instance of every type of data tool and once they start with a tool, it just stays there. So that's another sort of like hard and fast rule within the enterprise, where once a tool comes into existence, it never fades away.
So the big learning for us was to meet customers where they were, and not coming in with an opinion on the technologies a company has already deployed. Rather, at Cribl what we're going to do is help organizations and provide value by helping them with the things they already have.
What do you see as the key challenges with data observability?
Sharp: The migration to the cloud is far from complete as most enterprises are dealing with on-premises data centers and multiple clouds. All those deployments need to be secured and have their own tools and then there is a data integration challenge too.
I think the biggest broad challenge that our customers see is that data is growing and they're going to have 2 1/2 times more data in five years than they have today, and most enterprise budgets are not growing at the same pace.
Organizations have this fundamental tension where through no fault of their own they have huge amount of data that's being generated by moving to the cloud. Yet they're not getting the budget to do that.