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Chapter 10 of 12 · min 30:53 to 33:19

How to store your ad images in your own bucket

A tour of the loaded data in BigQuery: the TikTok table, the Meta table with 3 million rows, and the creative field pointing to your own bucket instead of the platforms' expiring URLs.

The data in BigQuery: tables, rows and creatives in your bucket

Watch on YouTube (from 30:53) · Spanish audio

Touring the dataset in BigQuery

From the destination in Detrics, one click opens the dataset in BigQuery with the tables the transfers generated. The ad-level TikTok table has the same schema defined when creating the table groups; Details shows row count and size, and Preview shows the data.

The creative field

The video pauses on one particular field: the creative. The ad’s image is stored in a bucket, not in the URL the platforms return. The column holds your own cloud’s URL, so each ad’s metrics stay tied to its image permanently, everything centralized in BigQuery and everything on your side.

Preview in BigQuery: every ad's creative points to your own bucket

The Meta table

The Meta table is even bigger: almost 3 gigabytes and 2.5 million rows, with its preview and, in the image column, the same thing: every creative stored in your own bucket.

The transfers already ran and keep running on their own. With the 30 accounts loaded and the Sync All feature (enabled for enterprise customers), any new account added to the Meta account you configured joins automatically: its history downloads and it enters the same steady state as the rest.

Frequently asked questions

How do the ad images end up in my bucket?

With image storage enabled on the destination, Detrics downloads every creative and uploads it to your cloud. In the BigQuery table, the image column stores your own bucket's URL, not the platform's.

What do I gain from hosting the creative in my cloud?

The URLs Meta and TikTok return expire after 7 days and break your reports. With the image in your bucket, each ad's metrics stay tied to its creative permanently.

All chapters in this series

  1. 0:00 1. The problem: ad-level data
  2. 2:19 2. The solution: warehouse, history and incremental deduplication
  3. 5:15 3. A look at the final result in BigQuery
  4. 8:27 4. Step one: connect Claude to Detrics via MCP
  5. 11:02 5. Step two: explore the sources and design the data schema with Claude
  6. 13:51 6. How the Detrics warehouse works
  7. 19:31 7. The table groups created in Detrics
  8. 22:04 8. Step three: create the destination in your own Google Cloud
  9. 25:27 9. Step four: transfers and the historical load
  10. 30:53 10. The data in BigQuery: tables, rows and creatives in your bucket
  11. 33:19 11. Analyzing the data with Claude and the BigQuery MCP
  12. 37:41 12. Final step: the unified Meta + TikTok view

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