Here’s the full architecture behind a 10-table data stack that combines raw Meta, TripleWhale, and Northbeam data into one place — every creative, every ad, every day, tagged with 52 media-buying fields.
Why bother building a stack at all
This removes the guesswork that comes from checking each platform’s dashboard separately. When every creative is tagged consistently and every metric lives in one schema, finding which ads to scale stops being a judgment call and starts being a query.
What the stack actually holds
In production, this stores roughly 800,000 daily metric rows going back to April 2023 — about 27 million individual performance readings across 38,610 cataloged creatives, with 8,180 of those fully enriched with metadata. That depth is what makes it possible to see not just which ads win, but why they win, which is the part that actually informs product and account direction.
The ten tables
Accounts holds one row per brand tenant. User Accounts controls who has access to which brand. Daily Ad Metrics stores all 52 fields per ad per day. Creatives keeps thumbnails, AI tags, ad copy, and on-screen text together in one place. Tag Rules convert messy ad names into structured, queryable fields. Brand Content embeds brand text as vectors for semantic search. Monthly Budgets tracks allocation and pacing over time. Saved Views stores curated reports so people aren’t rebuilding the same query every week. Login Events logs access for audit purposes. Meta Change Log records account and setup changes so you can trace what changed and when.
Getting data in without a heavy integration
The design accepts CSV drops from Meta, TripleWhale, and Northbeam, which means you can get moving without building full API integrations up front — those can come later once the schema is proven out. That flexibility is what makes it approachable for teams without a dedicated engineering function.
Keeping brands separated and secure
Tenant-based row separation keeps each brand’s data isolated from the others, role-based access through User Accounts controls who sees what, and logging every login and change gives you a real audit trail if you ever need to prove data separation for compliance reasons.
Why the tagging layer is the real value
Tagging every creative across 36 fields — hook type, awareness level, and more — is what lets you actually isolate which creative elements are driving results, rather than just which ad happens to be winning this week. Tag Rules do the unglamorous work of turning inconsistent ad names into clean, structured fields you can actually build a report on.
Building it in six steps
Design the schema and tenant model first. Prepare CSV templates for each data source. Build ingestion and validation jobs so bad data gets caught early. Enrich creatives with AI tags and vectors. Set up access control and logging. Then build the saved views and dashboards people will actually use day to day. Document each step as you go — that’s what makes it repeatable for the next brand you onboard, and none of this requires a deep engineering team to pull off.
Where to learn more about the pieces
Meta Business Help covers ads best practices directly, Google Cloud Architecture has solid general patterns for data integration, and IAB is a good source on measurement approaches more broadly.
Related reading on this blog:
- Scale Ads by Incremental Attribution
- 7 Skills That Replaced Our Ads Workflow
- High-Converting Google Ad Anatomy
- Facebook Ads Limits That Kill Performance
- Free Claude Skills for Paid Ads
A stack like this is a real competitive edge — brands with rich, structured data act with clarity and speed, while teams relying on surface-level metrics keep missing the causal signal underneath.