#dbt #analyticsengineering #semanticlayer In Episode #1021, Tristan Handy (Founder and CEO of dbt Labs) joins @JonKrohnLearns to explain how a study of about a hundred companies in 2016 became analytics engineering, and then became a tool that over a hundred thousand data teams rely on. Tristan coined the term, chose SQL when Spark was the fashionable answer, and spent a decade turning down acquisition offers because none of them were good for the people using dbt. He is now merging dbt Labs with Fivetran and taking on the presidency of the combined company, the first deal he says cleared that bar. In this episode, Tristan walks through what dbt does to your raw data, argues that the semantic layer matters more once analytics agents are asking the questions, explains the type safety behind the Fusion engine, and details how a 12-kilobyte skill file collapses a million-dollar migration into six weeks. This episode is brought to you by: • Notion: http://notion.com/superdata • Anthropic: https://claude.ai/superdata • Gurobi: https://www.superdatascience.com/gurobi Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: • (00:00:00) Introduction • (00:07:22) Why Tristan chose SQL over Spark, and what progressive complexity means • (00:10:44) How a dbt project turns raw data into modeled tables • (00:19:35) Why a decade of acquisition offers kept failing his one test • (00:41:32) How 12-kilobyte skill files cut year-long migrations to six weeks Additional materials: https://www.superdatascience.com/1021