Datameer, the company that was born as a data prep startup on top of the open-source Hadoop project, announced a $40 million investment and a big pivot away from Hadoop, while staying true to its big data roots.
The investment was led by existing investor ST Telemedia. Existing investors Redpoint Ventures, Kleiner Perkins, Nextworld Capital, Citi Ventures and Top Tier Capital Partners also participated. Today’s investment brings the total raised to almost $140 million, according to Crunchbase data.
Company CEO Christian Rodatus says the company’s original mission was about making Hadoop easier to use for data scientists, business analysts and engineers. In the last year, the three biggest commercial Hadoop vendors — Cloudera, Hortonworks and MapR — fell on hard times. Cloudera and Hortonworks merged and MapR was sold to HPE in a fire sale.
Starting almost two years ago, Datameer recognized that against this backdrop, it was time for a change. It began developing a couple of new products. It didn’t want to abandon its existing customer base entirely, of course, so it began rebuilding its Hadoop product and is now calling it Datameer X. It is a modern cloud-native product built to run on Kubernetes, the popular open-source container orchestration tool. Instead of Hadoop, it will be based on Spark. He reports they are about two-thirds done with this pivot, but the product has been in the hands of customers.
The company also announced Neebo, an entirely new SaaS tool to give data scientists the ability to process data in whatever form it takes. Rodatus sees a world coming where data will take many forms, from traditional data to Python code from data analysts or data scientists to SaaS vendor dashboards. He sees Neebo bringing all of this together in a managed service with the hope that it will free data scientists to concentrate on getting insight from the data. It will work with data visualization tools like Tableau and Looker, and should be generally available in the coming weeks.
The money should help them get through this pivot, hire more engineers to continue the process and build a go-to-market team for the new products. It’s never easy pivoting like this, but the investors are likely hoping that the company can build on its existing customer base, while taking advantage of the market need for data science processing tools. Time will tell if it works.