In this data management session, Christopher describes how to build robust and reliable data products in BigQuery and dbt, for PPC and SEO use cases. After an introduction to the modern data stack, six principles of reliable data products are presented, followed by the following use cases:
- Google Ads Conversion upload
- SEO sitemap efficiency report
- Google Shopping product rating sync
- Large-Scale link checker with advertools
- Inventory-based PPC campaigns with dbt
Here is the referenced selection of gists on github: https://gist.github.com/ChrisGutknecht
Building Data Products with BigQuery for PPC and SEO (SMX 2022)
1. How To Build Data Products
in BigQuery for PPC & SEO
Christopher Gutknecht | @chrisgutknecht | Bergzeit
2. 1. Intro
Our Plan: Build Data Products & Activate Data
3. PPC & SEO Use Cases
2. Product Principles
3. About Chris: Acquisition & Analytics at Bergzeit
Digital Marketer
Data Nerd
Climber
1997 2008 2010 2022
Dad of 2
Online Store for Mountain Gear
145 M Revenue in FY 21/22
14 Countries, 5 Languages
World-class, data-driven team 🔥
Hiring a PPC!
4. I’d like to Set Clear Expectations for This Session
No BigQuery Intro
Data Management-Talk
What this session IS What it’s NOT
No BigQuery tactics
No ML Focus
Google Cloud & dbt focus
Data Product mindset
75% PPC, 25% SEO
5. Large-Scale PPC is Becoming the
Science of Managing Data Pipelines
RECAP FROM SMX 2019
Recap of My SMX 2019 Talk
13. Are there Alternatives to dbt? Not really
No extra setup
No control
Saved queries Google Dataprep
No SQL (no-code)
Transformation &
scheduling only
Google Dataform
Free for GCP
Smaller ecosystem
SQL framework
15. Wait: What about Javascript and Python?
Easy to get started
Instantly ready
Javascript Python
Powerful libraries
Leading data tools
SQL
Super scalable
Ideal for production
Centralized code
Only for smaller
data tasks
Harder to centralize
16. 1. Intro
Plan: Build Data Products & Activate Your Data
3. PPC & SEO Use Cases
2. Product Principles
30. Keep Your Documentation Close To Your Code
Documentation in .yml and .md Files
Document all Sources, Models and Exposures
Get documentation as HTML
YAML File for Model Metadata
Markdown File for Description
SQL Files with Model code
58. To This SQL-based Transformation in dbt
better control for complex transformations
easier to extend and reuse parts of logic
lower cost in long run
automated testing and documentation
59. There Are More Data Products in the Lab…
(Join Our Team!)
61. Your Takeaways from this Session
1. What the modern data stack is and why it’s exciting
3. Which principles to apply for data products in production
2. Why dbt is the best tool for data warehouse transformation
4. A few interesting PPC and SEO use cases for you to try
62. Thanks for Your Time.
Looking Forward To Questions!
Chris Gutknecht | Teamlead A&O | Hiring a PPC!