In 2021, Airbyte expanded monthly deployments by 6x, doubled GitHub rating, and acquired more than $181 million in investment. As Airbyte’s technology becomes the industry standard for data integration, the firm also announced an integration with the data orchestration platform Dagster.

The major accomplishments of Airbyte include:

• The deployments have grown from 400 in the first quarter to 2500 in the fourth quarter, approximately six times.
• GitHub reviews reached 5000 by the fourth quarter.
• With a company worth $1.5 billion, the company closed three investment rounds totaling $181.2 million.
• A total of 165 data connectors, which is more than any firm in the company’s 16-month period.

“The total funding in 2021 will allow us to reach our 2022 goals much quicker: expanding our team from 30 people today to over 200 members; growing from 16,000 deployments today to 100,000; and increasing from 150 connectors currently to 500 by the end of the year,” said Michel Tricot, co-founder and CEO, Airbyte. “The integration with Dagster brings additional productivity to users, especially in scaling their data pipelines.”

Elementl’s open-source project, Dagster, is an orchestration platform for the development, production, and observation of data assets. Dagster combines various data technologies into a single platform, which increases productivity by expanding from “single-player mode” to big companies and delivering unrivaled context to all stakeholders. It allows practitioners to design jobs in terms of data flow and build and test them quickly.

“Airbyte is an emerging open-source standard for data ingestion with a ton of momentum. With their focus on openness, fast development, it was an ideal partnership. We’re were thrilled to work with them to build a best-in-class experience for the modern data practitioner looking to make Airbyte work seamlessly in production,” said Nick Schrock, CEO of Elementl.

The Airbyte provides a solution to two major issues. Most of the less common “long tail” data interfaces are not supported by closed-source ELT solutions. Firms must always design and maintain their data connectors. Secondly, to make pre-built connectors work inside their specific data infrastructure, data teams must often do custom work around them.