Supermetrics for BigQuery
Connect Facebook Ads to BigQuery — Enterprise Ad Analytics at Scale
Warehouse your complete Facebook Ads history in BigQuery. Run unlimited SQL queries across campaigns, audiences, and creatives. Join with GA4, CRM, and revenue data for full-funnel attribution — all without writing a single line of ETL code.
Why Connect Facebook Ads to BigQuery?
Stop relying on Ads Manager's limited reporting. Warehouse your Facebook Ads data in BigQuery for unlimited historical analysis, cross-source joins, and custom attribution models.
- Every transfer creates properly typed BigQuery tables with DATE, STRING, FLOAT64, and INT64 columns mapped from the Facebook Ads API. Schema changes — like new metrics Facebook introduces — are handled automatically without breaking existing queries.
- Unlike Ads Manager, which caps exports and slows down on large date ranges, BigQuery processes billions of rows using columnar storage. Run year-over-year comparisons, seasonal trend analysis, and long-term cohort studies without hitting any walls.
- Facebook Ads data in Ads Manager lives in a silo. In BigQuery, you can match ad clicks to website sessions by UTM parameters, then tie those sessions to CRM deals by email or user ID. The result is a full-funnel view from ad impression to closed revenue — something no single-platform report can provide.
- Want profit-adjusted ROAS that factors in COGS? Need a custom efficiency score that weights conversions differently by campaign type? In BigQuery you write a SQL expression once, save it as a view, and every downstream report uses it automatically.
- BigQuery is a native data source in every major BI platform. Point Looker Studio or Tableau at your Facebook Ads tables and build interactive dashboards with drill-downs, filters, and calculated fields — no intermediate exports or data prep steps needed.
- Agencies managing dozens of client accounts and enterprises running thousands of campaigns hit Ads Manager limits fast. BigQuery is built for exactly this scale — partition your tables by date, cluster by campaign, and even the heaviest queries complete in under a minute.
How to Connect Facebook Ads to BigQuery
Three steps. Under two minutes. Zero code.
- 1
Create a data transfer
Log into Supermetrics, select your data source and BigQuery as your destination.
- 2
Authorize and configure
Connect your data source account, choose your BigQuery project and dataset, and select the data you want to transfer.
- 3
Set schedule and start transfer
Choose your refresh frequency (hourly, daily, or weekly) and click Start. Your data begins flowing into BigQuery automatically.
Facebook Ads Data Schema in BigQuery
Supermetrics creates and maintains clean, typed tables automatically. Here's what your Facebook Ads data looks like in BigQuery.
Data Freshness & Scheduling
Facebook Ads data is typically available in BigQuery within 3-6 hours of the reporting period end. Schedule daily transfers to have yesterday's complete data ready each morning. Historical backfill is available for up to 37 months.
What Facebook Ads Data Can You Pull into BigQuery?
Supermetrics gives BigQuery access to your full Facebook Ads reporting data — metrics and dimensions you already know from the Facebook Ads interface.
Key Metrics
- Impressions
- Clicks
- Spend
- ROAS
- CPA
- CTR
- CPM
- Conversions
- Reach
- Frequency
- Video views
- Link clicks
- Cost per result
- Purchase value
- Add to cart
Key Dimensions
- Campaign name
- Ad set name
- Ad name
- Age
- Gender
- Placement
- Device
- Country
- Region
- Platform
- Objective
- Delivery status
- Creative ID
Resources & Guides
Marketing data warehousing 101
Build vs. buy: data pipeline decisions
Supermetrics is Google Cloud Ready — BigQuery
How to optimize your Facebook ad campaigns
Facebook Ads report templates
PPC reporting: extract insights from paid campaigns
Why Supermetrics for BigQuery?
Purpose-built for marketing data since 2009. 200,000+ companies trust Supermetrics to move 15% of global ad spend into reporting and analytics destinations.
No Vendor Lock-In
Your data lands in BigQuery — infrastructure you own and control. Use any BI tool, any transformation layer, any ML platform. If you ever switch providers, your data and dashboards stay with you.
170+ Marketing Data Sources
Purpose-built for marketing data — not a generic ETL tool. Supermetrics covers 99% of metrics and dimensions from each source, with pre-structured tables ready for analysis. No transformation layer required.
Managed Schema
Supermetrics creates and maintains your Facebook Ads tables automatically. Schema changes from the source API are handled for you — no broken pipelines, no manual migrations.
Your Data, Your Infrastructure
Supermetrics moves data directly to your destination — nothing is stored on our servers. SOC 2 Type II certified, GDPR and CCPA compliant. Your data stays in infrastructure you control, simplifying privacy and compliance reviews.
Flat-Rate, Predictable Pricing
Fixed annual pricing regardless of data volume — no per-row charges, no surprise bills during peak campaign seasons. Transfer as much Facebook Ads data as you need without worrying about cost spikes.
No Data Limits
Query any date range, any number of campaigns, any level of granularity. No row limits, no sampling, no restrictions on the data you can pull.
Frequently Asked Questions
- 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.
Also Connect to BigQuery
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