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Ad data and automation · ETL

Marketing ETL: advertising data pipelines built for your warehouse

Marketing ETL is the work of pulling data out of advertising sources, cleaning it and loading it into a warehouse you control. Vexio builds these advertising data pipelines as a service: connectors for Amazon Ads, Google Ads, Meta Ads and other sources, loading into BigQuery or PostgreSQL in your own account, with backfills, a ledger of loaded dates and alerts.

By Updated

Book a scoping call How an engagement works

Final frame. Gaps found, backfilled and alerted, in your own warehouse.

Illustration of a generic pipeline: request, poll, download, clean, merge, load.

What an engagement includes

From request to load, and everything that keeps it correct.

Getting data out of a source once is easy. These are the parts that keep it correct every day and tell you when it is not.

  1. 01

    Request

    The pipeline asks the source for the report or dataset for a date range, within the source's rules.

  2. 02

    Poll

    Many advertising sources build reports asynchronously, so the pipeline checks until the report is ready.

  3. 03

    Download

    The finished file is fetched and kept as the raw record of what the source returned.

  4. 04

    Clean

    Types, names, currencies and dates are made consistent, and rows that break the rules are set aside with the reason.

  5. 05

    Merge

    Data from different sources and accounts is brought into shared tables you can query together.

  6. 06

    Load

    The result is written to your warehouse in a way that is safe to repeat.

  • Config-driven schemas

    Tables are defined in configuration. The pipeline creates them, and alters them when a source adds a field, so a change is a reviewed edit and not a rewrite.

  • Idempotent loads

    Running a load twice for the same dates gives the same result as running it once. A retry never duplicates rows.

  • Timezone-aware scheduling

    Each account reports in its own timezone. Schedules and date boundaries respect it, so a day means the same day everywhere.

  • Historical backfills

    We load the history you need, as far back as each source still returns it, before the daily schedule takes over.

  • A loaded-dates ledger

    Every date loaded for every source and account is recorded. Missing and stale dates are flagged instead of silently absent.

  • Retries with backoff

    Rate limits and temporary failures are retried with increasing delay, so a short outage at the source does not become a gap in your data.

  • Alerts

    Failures and gaps go to Slack or email, so a person hears about a problem before a report shows it.

  • Infrastructure as code and CI deploys

    The cloud setup is defined in code and released through a pipeline, so it can be reviewed, repeated and rebuilt.

  • Destinations in your own warehouse

    BigQuery; PostgreSQL; Amazon S3, Google Cloud Storage or Azure Blob (raw landing, parquet). The data lands in your account, under your access control.

Why these parts matter is explained in idempotent ad reporting pipelines, backfills and late data, and the basics in marketing ETL explained.

Sources

Sources we build connectors for.

This list is the scope of the service, not a catalogue of ready-made connectors. A source is built, tested and handed over per engagement, against the access you have.

  • Amazon Ads

    Advertising reports through the Amazon Ads API. Where near-real-time delivery matters, Amazon Marketing Stream is a scoping question.

  • Amazon Selling Partner API

    Sales and order data through the Selling Partner API (SP-API), so ad spend can be read against revenue.

  • Walmart Connect

    Advertising reporting through the Walmart Connect API.

  • Instacart Ads

    Retail media reporting from Instacart Ads.

  • Criteo Retail Media

    Retail media reporting from Criteo Retail Media.

  • Google Ads

    Account and campaign reporting loaded into BigQuery or PostgreSQL through the Google Ads API.

  • Meta Ads

    Facebook and Instagram advertising reporting through the Meta Marketing API.

  • TikTok Ads

    Advertising reporting through the TikTok API for Business.

  • DV360

    Display and video reporting from Display & Video 360.

Need a source that is not listed? Ask, and we will say whether it fits and what access it needs. Beyond the pipelines, we also do end-to-end work on each ad platform. On the Amazon side, Amazon Ads API reporting explained and Amazon SP-API vs Amazon Ads API show where each fits. Source names are trademarks of their owners and appear as text only; Vexio is not affiliated with, endorsed by or certified by them. Each source's own documentation is the reference for what it returns, for example Cloudflare Workers documentation.

Destinations

Marketing data pipeline destinations: BigQuery, PostgreSQL and cloud storage.

The data lands where you already work, in an account you own.

  • BigQuery
  • PostgreSQL
  • Amazon S3, Google Cloud Storage or Azure Blob (raw landing, parquet)

Raw files are kept as a record of what each source returned, and cleaned tables are loaded for analysis, so you get the benefits of both ETL and ELT. Your AWS data pipeline, or one on Azure or Cloudflare, is set up through our cloud and DevOps service, and the code is part of our custom software work.

How an engagement works

Six steps from a scoping call to a monitored pipeline.

  1. 01

    Scoping call

    We agree the questions the data must answer, the sources, the destination and what history you need. You get a written scope.

  2. 02

    Source and credential review

    We check what access exists for each source, what it returns, how far back it goes and how credentials will be held safely.

  3. 03

    Schema design

    We design the tables and the configuration that defines them, with you, before any code loads data.

  4. 04

    Build

    The connectors, the cleaning and merging, the ledger, alerts and the infrastructure as code, released through a pipeline.

  5. 05

    Backfill

    We load the agreed history and check it against the source before the daily schedule starts.

  6. 06

    Monitoring hand-off

    Alerts, runbooks and a handover session, so your team knows what a failure looks like and what to do.

Who it is for

Data engineering services for advertising teams.

The work suits anyone who needs advertising data in one place and cannot rely on manual exports.

  • Agencies

    One dataset across many client accounts and sources, instead of a different export for each.

  • Brands

    Advertising spend and results next to your own sales data, in your own warehouse.

  • Marketplace sellers

    Ad data from the marketplace read against sales and orders, so spend can be judged against revenue.

  • Data teams

    Reliable ingestion of advertising data so analysts stop maintaining scripts and start answering questions.

Comparing providers? Read the buyer's guide to data engineering services.

FAQ

Marketing ETL: questions we are asked.

Marketing ETL is the process of extracting data from advertising and commerce sources, transforming it into clean, consistent tables and loading it into a warehouse. It turns the separate reports of Amazon Ads, Google Ads, Meta Ads and others into one dataset you can query. Vexio builds these advertising data pipelines for you as a service.

We build a pipeline that requests reports through the Amazon Ads API, polls until each report is ready, downloads it, cleans and merges it, and loads it into BigQuery tables defined in configuration. Credential review, schema design and a backfill of history are part of the engagement. Where near-real-time data matters, we discuss Amazon Marketing Stream while scoping. For other sources, see Google Ads to BigQuery options.

In ETL the data is transformed before it is loaded; in ELT it is loaded raw and transformed inside the warehouse. For advertising data the practical answer is often both: raw files land in storage as a record of what the source returned, and cleaned tables are loaded for analysis. We recommend one design during scoping, with reasons. More in marketing ETL explained.

It depends on the number of sources, how much history you need and the access you already have, so we do not quote a standard duration. We scope it on a call and send a written scope that lists the sources, the destination, the history and the milestones before any work starts.

BigQuery and PostgreSQL, with raw files landed as parquet in Amazon S3, Google Cloud Storage or Azure Blob. The destination is in your own account, so your data stays under your control. An AWS data pipeline for ad data is part of this work, and hosting runs on AWS, Azure or Cloudflare as agreed. See cloud and DevOps.

Failed calls are retried with increasing delays, and loads are idempotent, so a re-run does not duplicate rows. A ledger of loaded dates flags any date that is missing or stale, and late-arriving data can be backfilled. Alerts go to Slack or email, so a person hears about a gap before a report shows it.

Yes, as sources we build connectors for: the Amazon Selling Partner API for sales and orders, Walmart Connect and TikTok Ads for advertising reports, alongside Amazon Ads, Google Ads and Meta Ads. Each connector is built against the access you have. See what we do on each platform, or ask about a source that is not listed.

No. Advertising data pipelines are a service: we build connectors for the sources you need, deploy them with infrastructure as code and hand them over. A source on our list means we build a connector for it in your engagement, not that a ready-made connector can be switched on.

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