Instacart Ads → BigQuery → Looker Studio Automated Reporting Pipeline Built a fully automated dat...Instacart Ads → BigQuery → Looker Studio Automated Reporting Pipeline Built a fully automated dat...
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Shijith Pulikkal
• Sep 18
Instacart Ads → BigQuery → Looker Studio Automated Reporting Pipeline
Built a fully automated data pipeline that pulls Instacart retail media performance into BigQuery daily — zero manual reporting, live client dashboard.
A US retail media client needed their Instacart Ads performance data flowing into a proper warehouse for reporting — but Instacart doesn't offer a native BigQuery export, so this meant building the integration from scratch.
I designed and deployed three independent Cloud Run jobs, each pulling a different level of Instacart's ad performance data: campaign, keyword, and product. Every job authenticates against Instacart's OAuth2 API, triggers an async report request, polls until it's ready, and loads the results straight into BigQuery — all on an automated daily schedule via Cloud Scheduler.
A few things I had to solve along the way that made this more than a simple API pull:
Token lifecycle management — Instacart's refresh tokens rotate on every use, so I built the auth layer around Secret Manager instead of local storage, keeping all three jobs synced off one shared, self-rotating token.
Idempotent loading — every run deletes and re-inserts its own date range before writing, so re-runs, retries, and historical backfills never create duplicate rows.
Automated backfill chunking — pulling 7+ months of history in one go isn't reliable against Instacart's API, so the pipeline automatically splits large date ranges into monthly batches.
The result: a fully hands-off pipeline feeding a live Looker Studio dashboard, running end to end at effectively $0/month on GCP's free tier.
Link to the Looker Studio Dashboard
https://datastudio.google.com/u/0/reporting/9be2dc18-0c04-46bb-a69f-970482acbd74/page/mSy7F/appview?hl=en
Google BigQueryLooker StudioPython
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