Question 1

What Facebook Ads data can I load into Databricks?

Accepted Answer

Structure and numbers. Structure gives ads, ad sets, campaigns, ad creatives, images and videos. Numbers arrive as ten daily ad level insight tables: a base table plus breakdowns by action type, country, platform and device, impression device, age, gender, age with gender, and publisher platform. Metrics run from spend, impressions, reach, frequency and clicks through CPC, CPM and CTR to conversions, conversion values and video watch measures.

Question 2

What actually moves through my storage bucket?

Accepted Answer

Load files, briefly. Rows are written out as staged files, bulk syncs using Avro, then pulled into Databricks with COPY INTO against a temporary or destination table, and the files are removed after a successful load. So the bucket is working space rather than a second copy of your ad data. It does need to be storage you own and permission, either an S3 bucket on AWS or an ADLS Gen2 container on Azure, and with IAM role staging Databricks itself must be able to read it.

Question 3

How are late Facebook conversions corrected?

Accepted Answer

By reading recent days again and merging over the old rows. Insight reports save their position on the report date, read day by day up to yesterday, then reread the last seven days by default. That can be raised, and up to 28 days is supported to match how long Meta keeps revising. Anything restated outside the window will not refresh by itself, so set the lookback against your longest attribution window rather than the default.

Question 4

How do I stop breakdown tables getting out of hand?

Accepted Answer

Be selective and partition. A single day can produce a row per ad per country per device, so taking all ten breakdowns when you use two is the usual cause of a table nobody wants to query. Set the report date as the partition key on the ones you keep. Z-order and liquid clustering are not applied automatically, so on the largest breakdown tables it is worth adding one yourself after the table exists.

Question 5

Why load ad platform data into a lakehouse?

Accepted Answer

Because spend only means something beside what it produced, and that comparison is a modelling job. Meta reports on Meta, so it cannot say which campaigns brought customers who stayed or which audiences earned the best margin. ETL into a data warehouse makes that an ordinary join and puts the same tables within reach of whatever attribution or mix modelling your team runs.

Sync Facebook Ads to Databricks

Sync Facebook Ads to Databricks with Polytomic

Ten insight breakdowns

Staged then cleaned up

Throttles under Meta limits

Or sync Databricks to Facebook Ads

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Connect Facebook Ads in Polytomic

Connect Databricks in Polytomic

Create a sync between Facebook Ads and Databricks

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