- Airbyte today raises $5.2 million in seed funding.
- Extract Transform and Load (ETL) offers an open-source.
- Internal IT teams have employed ETL tools to move data between the repository.
- Accel leads the current round of funding, with participation from Y Combinator; 8VC.
Airbyte is the open-source data integration alternative running in the safety of your cloud. It syncs data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is based in San Francisco. To learn more, visit
The corporate, which provides an open supply extract rework and cargo (ETL) device used to create data pipelines, is now looking to additional democratize that course. This contains, for instance, constructing complementary open supply instruments to control and safe knowledge, Airbyte cofounder and CEO Michel Tricot.
Airbyte raises $5.2 Billion in its Seed Funding
Airbyte immediately introduced it has raised $5.2 million. In seed funding as a part of an effort to make open supply instruments for managing and integrating knowledge extra accessible. Airbyte plans to ultimately present variations of its instruments licensed by organizations. Together with a choice to enter these instruments by way of a service hosted by Airbyte. Tricot mentioned the corporate can also be planning a managed integration service.
Accel led the present spherical of funding, with participation from Y Combinator; 8VC; Section cofounder Calvin French-Owen; former Cloudera GM Charles Zedlewski; Datavant cofounder and CEO Travis Could; Machinify president Alain Rossmann; and Auren Hoffman, co-founder, and CEO of LiveRamp and CEO of Safegraph.
Extract Transform and Load (ETL) offers an open-source.
ETL processes, along with other classes of data preparation tools, are being reevaluated. As organizations increasingly realize that the quality of any AI model they build is dependent on how reliable the data used to train machine learning algorithms is. Data scientists also want to be able to easily update data needed to retrain models as business conditions evolve. Data science teams can easily find themselves spending more time addressing data plumbing issues construct constructing AI models. Successfully building an AI model, as a consequence, can often require months of time and effort.
ETL tools are not going to resolve that issue on their own. But the easier data becomes to manipulate, the less time it will take to build an AI model and then continuously maintain it as new data sources become available. It’s not clear what impact the availability of open-source ETL tools is having on providers of the rival commercial offerings some organizations have been employing for decades.