Late 2019: Commercializing a tool into a SaaS product

What happened:

• With strong community adoption (~1,000 WAPs) and establishing ourselves as the industry standard, it became clear that more types of users wanted to tap into the “dbt workflow” but weren’t necessarily comfortable with a CLI, or with a locally-hosted/open source tool.

  Enter: dbt Cloud.

  A SaaS implementation of dbt with robust features to round out the dbt developer experience, namely:

  • Scheduling (orchestration)

  • Collaboration (more dev interfaces, semantic layer, mesh architecture),

  • Governance and data quality (data catalog, ecosystem integrations, observability and alerts)

  • Enterprise features like security, support, and SLAs

Why it mattered:

• Adoption. In this time period, adoption accelerated rapidly. From 1,000 WAPs at the beginning of 2019 to 10,000 only 3 years later (10x growth). With this rapid adoption, we became the defacto standard for how data transformations are done in a modern cloud environment.

• Ubiquity. We didn’t see real competition in this time frame. Most leads were inbound, and sales conversations were easy and straightforward, as organizations already knew us, were passionate about how we improved their lives/jobs, and wanted a managed version of dbt. We tended to only compete against ourselves (dbt Cloud vs dbt Core, or in some cases, against dbt Core runners.) But we were always at the heart of the conversation.

• Collaboration. With a browser-based IDE and user friendly interface, folks who were uncomfortable coding in a command line were able to freely participate in the transformation workflow. Analytics were unlocked to more than just data engineers, with these newly minted “analytics engineers” empowered with a governed way of building modern data transformations. Fewer tickets, better governance, and more alignment.

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