Originally posted on searchdatamanagement.techtarget.com. Written by Sean Michael Kerner.
Observability data vendor LogDNA said today it raised $50 million in a Series D round of funding and will use the new money to build out its technology and expand go-to-market efforts.
LogDNA’s observability data platform enables organizations to collect and analyze log data from both structured data and unstructured data sources and formats.
On Sept. 8, LogDNA introduced a preview of its Observability Data Streaming capability that enables organizations to analyze data in motion to help make the data actionable in real time.
In this Q&A, Tucker Callaway, CEO of LogDNA, explains observability data technology what approach the vendor is taking to enable organizations benefit from the data to help enable better IT, DevOps and security operations.
Why are you now raising a Series D?
Tucker Callaway: Our plan is to really focus on the customer opportunity that we see around observability data, and specifically the observability pipeline. I think there is a really strong two-year focus and then looking at what is the next event to go towards an IPO.
We are in a very financially strong position, and we have strong receivables and very low cash burn.
What is the market opportunity for observability data that you see at LogDNA?
Callaway: There is a set of data that gets commonly referred to as observability data and we're specifically really focused on the log data. What we see is that observability data is increasingly being consumed by a broader set of consumers. The observability data, like the log data, that you use for troubleshooting and debugging for DevOps teams, is the same data that is getting leveraged by security teams for cybersecurity use cases. And then the same data is being used for AI and machine learning models, pumping information that helps to go train models.
We're seeing this explosion of data across all these different consumers because they all have different purposes for it. People are trying to build capabilities on top of log data, on top of this observability data and leverage it for multiple use cases. We want to enable those people.
Our DNA, if you will, is very focused on the builders, the developers, the people who are trying to design new system. So that's kind of a big opportunity that we see in the market is one is to help people make better use of how they process, route, store and analyze observability data from multiple use cases.
Is handling all the different types of format for observability data a challenge for LogDNA and its users?
Callaway: You have a couple decisions to make in life, you can try to control chaos or you can accept chaos. So our approach is to accept it for what it is.
We're not going to be able to structure all data, especially as we explode into IoT and who knows what else in the future. You can't design a system assuming that you can control all the inputs to it.
In our observability data pipeline product we start with the assumption that we're going to have no control of what comes our way. Then once we get the data, we are designed and organized to normalize the data so that when we actually stick it in storage, it's structured and we've kind of wrangled the chaos through our pipeline.
What do you see as the key metrics for observability data?
Callaway: I think it's about the productivity of the consumer of the data. I think that the real measure has got to be on who we're enabling and what productivity they gain.
Engineers are near impossible to find these days, so anything we can do to help drive their productivity is a good thing. We're not an old-style assembly line anymore; the unit of production now is the effectiveness of the engineer.
