Databand raises $14.5M led by Accel for its data pipeline observability tools Posted on : Dec 01 - 2020

DevOps continues to get a lot of attention as a wave of companies develop more sophisticated tools to help developers manage increasingly complex architectures and workloads. In the latest development, Databand — an AI-based observability platform for data pipelines, specifically to detect when something is going wrong with a datasource when an engineer is using a disparate set of data management tools — has closed a round of $14.5 million.

Josh Benamram, the CEO who co-founded the company with Victor Shafran and Evgeny Shulman, said that Databand plans include more hiring; to continue adding customers for its existing product; to expand the library of tools that its providing to users to cover an ever-increasing landscape of DevOps software, where it is a big supporter of open source resources; as well as to invest in the next steps of its own commercial product. That will include more remediation once problems are identified: that is, in addition to identifying issues, engineers will be able to start automatically fixing them, too.

The Series A is being led by Accel with participation from Blumberg Capital, Lerer Hippeau, Ubiquity Ventures, Differential Ventures, and Bessemer Venture Partners. Blumberg led the company’s seed round in 2018. It has now raised around $18.5 million and is not disclosing valuation.

The problem that Databand is solving is one that is getting more urgent and problematic by the day (as evidenced by this exponential yearly rise in zettabytes of data globally). And as data workloads continue to grow in size and use, they continue to become ever more complex.

On top of that, today there are a wide range of applications and platforms that a typical organization will use to manage source material, storage, usage and so on. That means when there are glitches in any one data source, it can be a challenge to identify where and what the issue can be. Doing so manually can be time-consuming, if not impossible.

“Our users were in a constant battle with ETL (extract transform load) logic,” said Benamram, who spoke to me from New York (the company is based both there and in Tel Aviv, and also has developers and operations in Kiev). “Users didn’t know how to organize their tools and systems to produce reliable data products.”  View More