Okay, I have to ask... is it just me, or is pulling any meaningful insight from Amazon Ads reporting like trying to drink from a firehose? 😅
I've been trying to build a simple daily performance dashboard for our team, pulling data from the Amazon Ads API into Snowflake. The sheer number of report types is overwhelming—Sponsored Products, Brands, Display, plus all the different granularities (campaign, ad group, keyword, search term, product). Just figuring out which one I need is a project.
My main issues so far:
* The metric names aren't always consistent across different report types. Something called `clicks` in one report might be `totalClicks` in another.
* Joining a "campaign" report with a "search term" report to get a full picture feels weirdly complicated. The keys don't always feel... joinable?
* And the documentation! It's huge, but finding the specific field definition or knowing why a certain metric is blank in a report takes forever.
I literally just spent three hours debugging a pipeline because my `sponsoredProductsCampaignReport` data had duplicate rows for the same campaign on the same day. Turns out it was because of different bidding strategies? Maybe?
Here's a snippet of the chaos it caused in my transform step before I figured it out:
```
DuplicateKeyError: Found duplicate key for date, campaign_id. Data: [(2024-10-01, 12345), (2024-10-01, 12345), ...]
```
Has anyone else built a reliable pipeline for this? What's your source of truth report? I'm currently stitching things together with dbt, but it feels more fragile than I'd like.
Any tips or war stories would be a lifesaver. I'm drowning in CSV downloads and API docs over here.
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It's definitely not just you. That initial feeling of drinking from a firehose is a universal experience with the Ads API. Your points about inconsistent metric naming and complicated joins are key. I've found you often need to create a dedicated mapping layer in your transformation tool (like dbt) just to standardize `clicks`, `totalClicks`, and `clickThroughs` into a single field before any analysis can begin.
The duplicate rows issue is a classic one, and you've hit on a common cause. Different bidding strategies or even different ad placements within the same campaign can generate separate records. You'll likely need to decide on a consistent grain for your fact tables - for campaign reports, that often means aggregating by `campaignId`, `date`, and `portfolioId` (if you use them). Including the `placement` and `bidStrategy` fields in your initial pull, even if you don't report on them, can help debug those duplicates.
For the documentation, I agree it's a labyrinth. My strategy has been to start with the two or three report types that are absolutely critical for your business logic, map those exhaustively, and treat anything else as a separate, siloed data source until you need to combine it. Trying to understand the entire schema upfront is a recipe for frustration.
Your data is only as good as your pipeline.