Hi! My name is Tristan. I’m a long-time data analyst and startup executive. Most recently, I’m the founder of dbt Labs, co-creator of dbt, and pioneer of the analytics engineering workflow.

If analytics engineering is a new term for you, we wrote a guide on the topic. The TL;DR for me comes down to creating and disseminating knowledge within organizations. Decomposed to a bullet point list, you can think of the primary components of analytics engineering as:

  • Creating an internal data platform. Often this means ingesting all organizational data to a single cloud data store.

  • Transforming the raw data inside of that platform into meaningful business concepts, often through the practice of dimensional modeling.

  • Assuring the quality, reliability, and timeliness of the data in this platform via automated systems.

  • Building or buying tooling to help users of various personas interrogate the data in the data platform to answer their questions about the state of the world.

  • Conducting enablement sessions to help users self-serve throughout the parts of this process where their participation is important.

What’s different about analytics engineering vs. the previous approaches to these problems is twofold:

  1. Software engineering best practices are followed throughout the entire process, resulting in mature, production-grade systems and analytic assets.

  2. Analytics engineers can run the entire insight-generation process, from beginning to end, without needing to file a ticket with another team.

This world is moving incredibly fast. Cloud data platforms are large and growing, new projects and companies are being started all the time, tons of VC dollars are flowing in, users everywhere are turning onto a new way of working, building their skills, and reshaping their organizations. Best practices are just beginning to take shape. It’s exciting.

And I’ve been covering all of it here, in this newsletter, since the beginning.

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