tag
bigquery
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.
Connecting BigQuery to Claude: Prepare the Reporting Layer Before MCP
A practical example of connecting AI to business reporting: shared metric definitions, scoped access, freshness, and answer checks over BigQuery data.
What a Creative and Campaign Intelligence Data Platform Needs Before AI
The ingestion, modeling, output validation, and data-serving work behind useful AI-assisted campaign and creative analysis.
Data Engineering & Automation: A Complete Guide for Growth Teams
Learn how data engineering and automation can transform your business operations. Expert insights from Ahmad Humayun on building scalable data solutions.
Recomputing BigQuery Reporting Models with Dataform After Inputs Change
Changed historical dates, attribution settings, refresh checkpoints, and safe replacement: practical recomputation design for BigQuery reporting models.
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.
Building a Reliable DV360 and Ads Data Hub Pipeline with BigQuery, Cloud Run, and AWS SQS
Engineering lessons from building a reliable marketing data pipeline with DV360, Ads Data Hub, BigQuery, Cloud Run, Cloud Tasks, and AWS SQS.
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.
Recovering Missing Meta Ads Data in BigQuery: Reconnects, Pagination, and Backfills
How to identify incomplete advertising history, recover a bounded account/date scope, and validate source completeness before rebuilding reporting models.
Meta Ads to BigQuery: The Reporting System Behind the Pipeline
Meta advertising ingestion, BigQuery reporting layers, recovery, attribution-aware transformations, and campaign analysis within a production analytics product.
When Google Sheets Automation Is Enough, and When You Need a Warehouse
How I decide whether a reporting workflow should stay in Google Sheets and Apps Script or move into BigQuery, a database, or a custom dashboard.