Connect Facebook Ads to Databricks — Lakehouse-Powered Ad Intelligence

Log into the Supermetrics Hub, create a new data transfer, select Facebook Ads as the source and Databricks as the destination. Authorize your Facebook Ads account, provide your Databricks workspace URL and access token, choose your catalog, schema, and Unity Catalog settings, select the fields you need, set a schedule, and start the transfer. No custom notebooks, Spark jobs, or Delta Lake plumbing required — Supermetrics writes directly to Delta tables and registers them in Unity Catalog so your data is governed, versioned, and queryable with both SQL and PySpark from the moment it lands.

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 into your Databricks workspace — Supermetrics never stores your marketing data on its own servers. Unity Catalog provides centralized governance: fine-grained row-level and column-level security, attribute-based access control, and a full audit log of who queried what. Delta Lake's transaction log makes every write atomic and traceable, so you always have a verifiable lineage of your Facebook Ads data from ingestion to insight.

That is one of the defining advantages of the Databricks lakehouse architecture. Once Facebook Ads data lands as a Delta table, you can JOIN it with any other table in your lakehouse — raw event streams, CRM exports, product analytics, even ML Feature Store tables used for model training. Query in SQL from Databricks SQL warehouses or switch to PySpark and pandas for data science workflows — same data, no copying. Supermetrics supports 170+ connectors that all land in the same Unity Catalog namespace, and the Photon engine accelerates analytical queries on those Delta tables automatically.

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 already stored in your Delta tables. Delta Lake's time travel lets you query any previous version of your Facebook Ads data — useful for auditing retroactive metric recalculations or reproducing a dashboard state from last quarter. Schema evolution is handled automatically, so new fields appear as columns without breaking existing queries.

Data freshness depends on your transfer schedule. Supermetrics supports hourly, daily, or weekly transfers into Databricks. Most teams schedule daily transfers so yesterday's complete data is available each morning. Delta Lake's MERGE capability ensures only new and changed records are upserted, keeping cluster utilization and storage costs low. For teams that need near-real-time visibility, the Photon engine accelerates incremental queries so dashboards refresh in seconds, and you can set up Databricks SQL alerts to trigger notifications when key Facebook Ads metrics cross your thresholds.

Supermetrics maps Facebook Ads fields to Delta Lake (Spark SQL) native types. Dates become DATE, text fields like campaign_name are stored as STRING, monetary metrics like spend use DOUBLE for floating-point precision, and count metrics like impressions use LONG (BIGINT). The mapping is automatic — select your fields and Supermetrics creates the Delta table with correct schema.

Yes. Every load from Supermetrics creates a new Delta version. You can query any historical version using VERSION AS OF or TIMESTAMP AS OF syntax. This is especially useful when Meta reprocesses attribution data — compare VERSION AS OF 10 with the current version to see exactly which campaign numbers changed and by how much.

Absolutely. Your Facebook Ads Delta table is directly accessible from Databricks notebooks using PySpark, pandas, or SQL. A common workflow is computing rolling features (7-day CPA trend, frequency growth rate, CTR decay) in a Spark DataFrame, registering them in the Databricks Feature Store, and training a model with MLflow to predict creative fatigue or forecast daily spend.

Unity Catalog provides centralized governance across all Databricks workspaces. You can grant SELECT on your facebook_ads table to specific groups, apply column masks to hide spend data from certain roles, and set row-level filters so each business unit sees only their campaigns. All access is audited, and data lineage shows which downstream notebooks and dashboards consume the table.

Databricks pricing has two components: compute (DBU-hours for clusters or SQL Warehouses) and storage (the underlying cloud storage — S3, ADLS, or GCS — typically $0.02-0.03/GB/month). Facebook Ads datasets are small, usually under 10 GB even with years of history. Compute is the main cost driver — a Databricks SQL Warehouse (2X-Small) running daily queries typically costs $20-50/month depending on usage patterns.

Yes. The Facebook Ads API covers all Meta ad placements including Instagram Feed, Stories, Reels, and Explore. Data lands in the same Delta table with a placement column. Filter with WHERE placement LIKE '%instagram%' for Instagram-specific analysis, or query unfiltered for a consolidated Meta Ads view. You can also partition by placement if you frequently filter on it.