logo
logo

Gitlab-Spinoff Meltano Raises Another $8.2M For Its Open-Source Dataops Platform

Jun 08, 2022•over 3 years ago

Amount Raised

$8.2 Million

Information TechnologySoftware

Description

Almost exactly a year ago, GitLab spun out Meltano, an ELT (extract, load, transform) service that is now setting its sights on becoming a more fully-featured ‘DataOps’ service. At the time, GitLab, which had originally built the service to improve its own data lifecycle platform, Alphabet’s GV and a number of angels, invested $4.2 million in seed funding. Today, the company announced that it has raised an additional $8.2 million to extend its seed round to $12.4 million. The new round was led by Venrock, with GV, Uncorrelated Ventures, Data Tech Fund and a number of angel investors also participating.

Company Information

Company

Meltano

About

Today, the company announced that it has raised an additional $8.2 million to extend its seed round to $12.4 million. Now, with this new funding as a basis and the launch of Meltano 2.0 today, the 17-people company is returning to its original vision of becoming an end-to-end platform. But as Maan also noted, while the company found product-market fit with its ELT tool, it is now going back to the drawing board to get to the same stage with its end-to-end platform — and that’s also why the team decided to extend the seed round. Just like GitLab, the Meltano team also believes that it needs to build its tools in close collaboration with its users and to do so, the company needs to be highly transparent. In its early days, Meltano actually tried to follow the GitLab model, but now the company wants to embrace these third-party products and integrate them into a cohesive service. In practice, that means that in addition to Singer and dbt, the company also uses Airflow for its scheduling and workflow orchestration, Great Expectations for data quality assurances and Superset for its data exploration and visualization capabilities. We believe Meltano is the missing layer for building, connecting and managing the various data services that make up a modern data stack, allowing data teams to build new services similarly to how software teams build applications.”