Venturebeat today presents a new startup participating in a hot enterprise trend around data observability.
Generally speaking, these types of tools are useful in today’s sophisticated and distributed architectures. A company called Cribl is wielding what Venturebeat writer Kyle Wiggers calls “a double-edged sword in enterprise” and innovating frameworks for data handling.
The firm, which was started in 2017, allows clients to “integrate IT and security data” with unique resources developed for data observability purposes.
Cribl has 150 employees, and has raised $254 million to date. The company moves five petabytes of data per day for 2300 members, and revenues are up 300% year-over-year. With big clients like Whole Foods and Cox Automotive, Cribl is on the move.
“Customers face the contradictory reality of needing to collect and process an explosion of metrics, event, log, and trace data, all of which are used to tell businesses the health and security of their systems,” Cribl CEO Clint Sharp told VentureBeat in explaining the firm’s utility in today’s business environment. “It’s within this new reality over the last several years that we built the company from the ground up to address … Our strategy going forward is to enable even more data to be observed and unlock all the value that data brings to analytics. At the end of the day, analytics tools are only as valuable as the data they’re working with and [Cribl] is the key to unlocking that value.”
In the same interview, Sharp also talks about why AI has not caught on with these kinds of systems, describing a strange kind of cat and mouse game between potential AI automation and human hackers looking to get an entryway.
“AI hasn’t gained much traction with telemetry data, which by its nature is constantly changing and problems are not generally repeated — the entire premise of observability is that you’re trying to answer questions that haven’t been asked before,” Sharp says. “If the AI-assisted analytics isn’t reviewing the entire end-to-end system, then it will make assumptions about missing data in order to make recommendations or automate activity. Those assumptions are the windows that hackers are constantly testing to see if they can crawl through.”
AI aside, the reality is that companies need these systems.
“If you can’t monitor your servers, containers, and data in the cloud, you can’t analyze and fix problems at the speed you need,” writes Lavanya Chockalingam August 3 at Observatory. “Given the complexity of cloud infrastructure and the sheer amount of data processed, observability is more important than ever. That’s why a paradigm shift in thinking about how observability works was needed. Cloud native observability is full stack.”
Keep an eye on startups like this one as data observability advances.
