Tag
marketing-data
Ads Data Hub: How User-Provided Data Matching (UPDM) Actually Works
A technical reference for Ads Data Hub user-provided data matching. Hashing requirements, match table architecture, match rate calculation using is_updm_eligible, privacy thresholds, EEA consent, and practical tips for improving match rates - sourced from Google's official documentation.
What a Creative and Campaign Intelligence Data Platform Needs Before AI
A field note on the ingestion, warehouse, validation, and prompt-traceability layers I look for before adding AI analysis to campaign and creative data.
Dataform vs dbt for Marketing Analytics: Differences That Actually Matter
A practical comparison of Dataform and dbt for marketing analytics warehouses on BigQuery. When to choose each, what the real differences are, and what does not matter as much as the articles say.
DV360 Reporting and BigQuery: Understanding the Three Data Paths
How Display & Video 360 data actually reaches BigQuery. The BigQuery API Connector, Reporting Data Transfer, and Bid Manager API serve different purposes - here is what each gives you and how to use them together.
Freelance Data Engineer vs Data Engineering Agency: What Marketing Teams Should Know
An honest comparison of hiring a freelance data engineer versus a data engineering agency for pipeline, warehouse, and reporting projects. When each makes sense.
How to Connect Google Ads and Meta Ads Data to BigQuery
A practical guide to moving Google Ads, Meta Ads, GA4, LinkedIn Ads, and DV360 data into BigQuery for marketing analytics. What each connection looks like and what breaks.
Why Marketing Dashboards Break When Spend Is Modeled at the Wrong Grain
A practical note on campaign, ad, creative, order, and CRM grain problems in marketing warehouses, and how I model them before they reach dashboards.
What a Marketing Data Pipeline Project Costs
An honest breakdown of what marketing data pipeline projects cost, what drives the price up or down, and how to scope a project before asking for a quote.