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You don’t need a data warehouse to build on your Amazon data

SellerMate Team

Content Writer

10/24/20265 min read2 views
Analyze uploaded CSV and Excel data files alongside live Amazon data

Ask an AI assistant to build you an Amazon Ads dashboard and the first thing it will tell you is what it needs: a way to get the data. Then it will describe the project you were trying to avoid. Apply for API access. Request reports. Poll for them. Parse them. Store them somewhere. Repeat for every ad type, every marketplace, every client.

That project is why most internal Amazon tools never get built. It is also, for anyone on SellerMate, already done. This is a plain comparison of the two routes, so you can decide where your own effort should go.

The native route, honestly

Amazon exposes its data through two main APIs: the Amazon Ads API for advertising and the Selling Partner API for retail, inventory and orders. Both are capable. Both are a lot of work to build on, for reasons that have nothing to do with your tool.

Access. The Ads API needs a developer application and approval. The Selling Partner API needs its own, with a different process. An agency needs the authorisation per client account, and keeps the tokens alive per client.

Reports are asynchronous. You do not query the Ads API for last week's campaign performance. You request a report, wait for Amazon to generate it, poll until it is ready, download a compressed file, and parse it. Minutes per report, and a report per ad type per date range.

Every source has its own shape. Sponsored Products, Sponsored Brands and Sponsored Display report different columns. DSP is a separate API. Amazon Marketing Cloud is a separate query language. Retail sales come from the Selling Partner API in yet another form. A single "spend and sales by product" view means reconciling five schemas.

History is your problem. Amazon's report windows are limited. If your dashboard needs a year of daily data, you have to have been collecting it for a year, and storing it, and backfilling the gaps when a job failed.

Attribution moves. Sales attributed to a click keep arriving for days after the click. The number you stored on Tuesday is wrong by Friday. Either you re-pull trailing windows constantly, or your dashboard quietly disagrees with Amazon's.

Rate limits and retries. Both APIs throttle. Every integration ends up with a queue, backoff and a scheduler.

None of this is unreasonable. It is what a data pipeline is. It is just not the thing you wanted to build.

The SellerMate route

SellerMate runs that pipeline for every connected account, because the app and its AI agent need it. Ads, DSP, AMC, Seller Central and Vendor Central are collected on a schedule, normalised into one set of metric names, kept for as long as your plan retains them (30 days on Free, 90 on Pro, unlimited on Unlimited and Enterprise), and refreshed as attribution settles. Hourly stream data is there on Unlimited and Enterprise.

The Public API is the door into that. One key for your workspace, one call shape for every tool, and the same data the SellerMate dashboard shows:

1curl -s -X POST https://api.sellermate.ai/public/v1/tools/get_campaign_performance \
2  -H "X-API-Key: $SELLERMATE_API_KEY" -H "Content-Type: application/json" \
3  -d '{"amazon_account_id":"<id>","start_date":"2026-09-01","end_date":"2026-09-30","page_size":500}'

That returns campaign rows for the month, a summary across the whole set, and a pagination block. No approval, no report job, no parsing, no storage. The same call works for every account in the workspace, which is the part Amazon's APIs cannot do at all: an agency's whole book in one query.

The documentation lists all seventy-plus tools with their parameters, in plain HTML, so an AI assistant can read it in one fetch and write the tool you asked for.

So when do you still want a warehouse?

Sometimes. Three honest cases:

  • You are joining Amazon data with data from somewhere else. Shopify orders, your ERP's margins, Google Ads spend. The join has to happen in a place you control, and that place is a warehouse or at least a sheet.
  • Your BI team already lives in BigQuery, Snowflake or Power BI and wants Amazon data as one more table. Fine. The difference is that the table is fed by a nightly script calling SellerMate, not by a pipeline you wrote against Amazon.
  • You want a copy you own, for contractual or audit reasons. Also fine, same answer.

For all three, the nightly script is a short one: call the reporting tools for each account and write the rows to your warehouse. Describe it to your assistant with the docs URL, or start from the Google Sheets prompt, which is the same idea for teams who do not want a warehouse at all.

What you no longer need a warehouse for is the thing most people build one for: a dashboard, an alert, a report, a tool that reads today's Amazon numbers and does something with them. For those, the warehouse is SellerMate, and the API is enough.

What this changes about the build

With the data layer removed, an internal Amazon tool becomes what it should have been all along: a thin piece of software that asks for numbers and presents or acts on them. That is the kind of thing an AI assistant writes well in one sitting. It is why the prompt library works: every prompt is about the tool, not the plumbing.

A reasonable test of whether you have accidentally started building a pipeline: if your code has the words "report request" or "poll" in it, stop, and call SellerMate instead.

Getting started

  1. In SellerMate, open Profile → API Keys and create a read-only key. It is included in every plan.
  2. Call get_user_context to see your accounts.
  3. Paste a prompt into Claude, ChatGPT or Cursor, or point it at the docs and describe your own tool.

Get your API key · Developer API · API documentation

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