Data Engineering Podcast Build Your Data Analytics Like An Engineer With DBT
56:46
Add Schema Tests To Models
- Add schema tests (unique, not null, referential, accepted values) to models to catch regressions and bad joins early.
- Write custom tests as SQL that return zero rows when the assertion holds.
Model As Single Source Of Truth
- dbt turns reusable SQL select statements into named, versioned database objects that serve as single sources of truth.
- Updating a model updates all downstream reports without editing each report manually.
Auto-Generated Data Catalog
- dbt auto-generates documentation from your models and tests, producing a browsable catalog of your data models.
- This keeps documentation fresh because it lives with the code and updates as models change.
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Introduction
00:00 • 3min
How Did You Get Your Start With Fishtown Analytics?
02:35 • 2min
DBT - What's It All About?
04:06 • 3min
DBT and Performance Tuning on Redshift Tables
06:36 • 4min
DBT and Continuous Integration in Continuous Integration
10:45 • 2min
Adding Tests to DBT Models and Operations
12:22 • 3min
DBT and DBT Run - The Secret of DBT
15:03 • 4min
DBT Docs Generate a Single Page App of All of Your Data Models
19:11 • 2min
DBT
20:43 • 5min
Is There Any Support for Custom Filters in Dbt?
25:36 • 3min
Using DBT to Make SQL Reusable
28:58 • 4min
DBT
32:47 • 4min
Data Warehouses and Spark Session
37:05 • 2min
The Secret Sauce of DBT - Data Sources
39:15 • 2min
DBT Cloud - What's the Difference?
41:33 • 3min
What Are Some of the Most Interesting Ways That You've Used Dbt?
44:42 • 2min
The Most Interesting or Useful Lessons That You've Learned From DBT
46:28 • 4min
Is DBT the Right Tool for Data Warehouse Transformation?
50:54 • 3min
What's the Biggest Gap in Data Management?
53:56 • 3min
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
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
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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
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- DBT
- Fishtown Analytics
- 8Tracks Internet Radio
- Redshift
- Magento
- Stitch Data
- Fivetran
- Airflow
- Business Intelligence
- Jinja template language
- BigQuery
- Snowflake
- Version Control
- Git
- Continuous Integration
- Test Driven Development
- Snowplow Analytics
- dbt-utils
- We Can Do Better Than SQL blog post from EdgeDB
- EdgeDB
- Looker LookML
- Presto DB
- Spark SQL
- Hive
- Azure SQL Data Warehouse
- Data Warehouse
- Data Lake
- Data Council Conference
- Slowly Changing Dimensions
- dbt Archival
- Mode Analytics
- Periscope BI
- dbt docs
- dbt repository
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA