Analytics Engineering in Data Lakes with dbt
Within the past few years, a new persona has emerged on the modern data team: the analytics engineer. On platforms that seek to enable the intuitive workflows of data warehousing in the cloud data lake—powered by engines like Dremio, Spark and Presto—the analytics engineering toolset, including dbt, is a natural fit. By writing all transformation logic in SQL, critical business rules are accessible to the greatest number of people; by templating that SQL with Jinja, storing it in version control, wrapping it with automated tests and documentation, and persisting valuable metadata, the analytics workflow gains the rigor of software engineering principles.
Topics Covered
Speakers
Jeremy Cohen
Jeremy Cohen is the Associate Product Manager at Fishtown Analytics, committed to the development of open source dbt and the sustainable growth of its community. Other hats he has worn include analytics engineer, consultant, course instructor and package maintainer.
Fabrice Etanchaud
Fabrice Etanchaud is a lead dev at Maif-vie, a French life insurance company. He has developed data services for fifteen years, mainly in the environmental remote sensing and intellectual property realms. Fabrice originally fell in love with analytics through his work in enterprise information systems.
