Build Your Data Analytics Like An Engineer With DBT

Episode • May 20, 2019 • 56m

Summary

In recent years the traditional approach to building data warehouses has shifted from transforming records before loading, to transforming them afterwards. As a result, the tooling for those transformations needs to be reimagined. The data build tool (dbt) is designed to bring battle tested engineering practices to your analytics pipelines. By providing an opinionated set of best practices it simplifies collaboration and boosts confidence in your data teams. In this episode Drew Banin, creator of dbt, explains how it got started, how it is designed, and how you can start using it today to create reliable and well-tested reports in your favorite data warehouse.

Announcements

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Your host is Tobias Macey and today I’m interviewing Drew Banin about DBT, the Data Build Tool, a toolkit for building analytics the way that developers build applications

Interview

Introduction
How did you get involved in the area of data management?
Can you start by explaining what DBT is and your motivation for creating it?
Where does it fit in the overall landscape of data tools and the lifecycle of data in an analytics pipeline?
Can you talk through the workflow for someone using DBT?
One of the useful features of DBT for stability of analytics is the ability to write and execute tests. Can you explain how those are implemented?
The packaging capabilities are beneficial for enabling collaboration. Can you talk through how the packaging system is implemented?
- Are these packages driven by Fishtown Analytics or the dbt community?
What are the limitations of modeling everything as a SELECT statement?
Making SQL code reusable is notoriously difficult. How does the Jinja templating of DBT address this issue and what are the shortcomings?
- What are your thoughts on higher level approaches to SQL that compile down to the specific statements?
Can you explain how DBT is implemented and how the design has evolved since you first began working on it?
What are some of the features of DBT that are often overlooked which you find particularly useful?
What are some of the most interesting/unexpected/innovative ways that you have seen DBT used?
What are the additional features that the commercial version of DBT provides?
What are some of the most useful or challenging lessons that you have learned in the process of building and maintaining DBT?
When is it the wrong choice?
What do you have planned for the future of DBT?

Contact Info

Email
@drebanin on Twitter
drebanin on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

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