Table of Contents

  1. Data transformation
  2. Data testing
  3. Implementations + deployment
  4. Documentation + metadata
  5. The modern data stack
  6. Data dream teams

Taking off with dbt: JetBlue's dbt journey!

Ashley and her team have been working with dbt for the past several months, and have used it to give their enterprise data warehouse a complete makeover. She loves working with dbt and believes it is an essential ingredient for building a modern data warehouse.

Carly is a recovering Texan and technology consultant who has spent the past two years building a BI stack (using dbt!) for a well-known Denver-based photo printing company. When she’s not geeking out in SQL, you’ll find her enjoying the Colorado outdoors with her husband, daughter, and Wally the Labrador.

Mila spent her college years caught between computer science, gaming, communication, logic, ancient ethics, and ancient languages. She wandered into data engineering by happenstance, but fell in love with dbt at first sight. Having found a home in the dbt Slack, she now looks after community health as well as tinkers on projects behind the scenes. Her inbox and DMs are always open to community concerns and inquiries.

Here's some facts about JetBlue's dbt project — they have 1800 models, on top of 280 data sources, have defined 8500 tests and they built their entire dbt project in six months!

In this session, Ashley will share how a small team of data engineers successfully migrated their entire [data warehouse]( workload to dbt, and their tips for setting up your dbt project for success.

Browse this talk’s Slack archives #

The day-of-talk conversation is archived here in dbt Community Slack.

Not a member of the dbt Community yet? You can join here to view the Coalesce chat archives.

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