Connect Facebook Ads to BigQuery — Enterprise Ad Analytics at Scale

Log into the Supermetrics Hub, create a new data transfer, select Facebook Ads as the source and BigQuery as the destination. Authorize your Facebook Ads account, choose your BigQuery project and dataset, pick the fields you want, set a schedule, and start the transfer. No code or SQL knowledge required — Supermetrics creates and manages the tables automatically.

Supermetrics is SOC 2 Type II certified and fully GDPR compliant. All Facebook Ads credentials are encrypted at rest and in transit. Data flows directly from the Facebook Ads API to your own BigQuery project — Supermetrics does not store your marketing data on its servers. Your BigQuery IAM policies control who can access the tables.

That is one of the biggest advantages of warehousing your data. Once Facebook Ads lands in BigQuery, you can JOIN it with any other table — GA4 sessions, CRM deals, revenue data, other ad platforms — using standard SQL. Supermetrics supports 170+ data sources, and they all land in the same BigQuery project for easy cross-source analysis.

All standard Facebook Ads reporting fields are available, including Impressions, Clicks, Spend, ROAS, CPA, CTR, and many more. You select exactly which metrics and dimensions to transfer during setup, and you can add or remove fields at any time without losing historical data.

Data freshness depends on your transfer schedule. You can run transfers hourly, daily, or weekly. Most users schedule daily transfers so yesterday's complete data is ready each morning. Supermetrics uses incremental loading — only new and updated records are transferred — so even large accounts refresh quickly.

Supermetrics creates clean, flat tables with properly typed columns. Dates are stored as DATE, text fields like campaign_name as STRING, and numeric metrics like impressions and spend as FLOAT64 or INT64. You choose which fields to include during setup, and each transfer creates or updates tables in your chosen dataset. The schema is fully queryable with standard SQL from day one.

Supermetrics supports backfilling up to 37 months of historical data from the Facebook Ads API, which is the maximum retention window Meta provides. You can trigger a backfill during initial setup or at any time afterward. The historical data uses the same table schema as ongoing transfers, so all your queries work across the full date range.

Supermetrics uses incremental loading by default. Each scheduled transfer fetches only new and updated records since the last run, then appends or upserts them into your BigQuery tables. This keeps transfer times short and BigQuery costs low. You can also trigger a full refresh for a specific date range if you need to re-sync data after changes in Facebook attribution windows.

BigQuery storage costs roughly $0.02 per GB per month, and most Facebook Ads datasets are small — even large accounts with years of history rarely exceed a few GB. Query costs depend on how much data you scan, but columnar storage means a query on just spend and conversions only scans those two columns. For most teams, the total BigQuery bill for Facebook Ads data is under $5 per month.

Yes, and this is one of the primary reasons teams warehouse their Facebook Ads data. Use UTM parameters to join Facebook Ads clicks with GA4 sessions, then extend the chain to CRM tables using email or user ID. A typical query might join facebook_ads_campaign on utm_campaign = ga4_sessions.campaign to calculate true cost-per-acquisition from ad click through to closed deal.

It does. The Facebook Ads API covers all Meta ad placements including Instagram Feed, Stories, Reels, and Explore. You can filter by placement in your SQL queries to isolate Instagram-specific performance, or aggregate across all placements for a unified Meta Ads view.