Question 1

What LinkedIn Ads data can I load into Databricks?

Accepted Answer

On the structural side, accounts, campaign groups, campaigns and creatives. On the reporting side, analytics pivoted by campaign, creative, company and other dimensions, with impressions, clicks, spend, conversions and the video and engagement measures LinkedIn exposes. Take the structural tables even if you only want numbers, because analytics rows carry identifiers that mean nothing until joined to campaign and creative names.

Question 2

Where do the tables land in my workspace?

Accepted Answer

In the catalog and schema you select on the sync, so ad data can sit beside the rest of your model rather than in a default location somebody has to go looking for. Getting this right early matters because the connected identity needs USE CATALOG and USE SCHEMA on the target, plus CREATE SCHEMA, CREATE TABLE and MODIFY to build and maintain tables. A wrong or default catalog is one of the more common reasons a first sync fails.

Question 3

How long do LinkedIn numbers keep changing?

Accepted Answer

Longer than a day, which is why analytics are fetched in daily windows, checkpointed by date, then read again on a lookback and merged over what is already there. The default window is two days, and member company reporting forces a minimum of four because those figures settle more slowly. Anything restated outside your window will not refresh on its own. Resource entities like campaigns and creatives are read in full each run, since they are small.

Question 4

Can I match LinkedIn Ads data to target accounts?

Accepted Answer

That is usually the reason to do this at all. Member company reporting adds a company dimension to the analytics, which you can join to your own account list once both sit in the same catalog. Pivots by industry, seniority, job function and company size are available too. LinkedIn applies its own privacy thresholds, so small segments may be suppressed at source and will be absent rather than showing as zero.

Question 5

Why load LinkedIn Ads into a lakehouse?

Accepted Answer

Because business to business advertising is judged on pipeline, and pipeline lives in the CRM. Joining campaign spend to opportunities needs both in one place, and the account level pivots only pay off when matched against your own account list. ETL into a data warehouse gives you that, and puts the data within reach of whatever attribution modelling your team runs rather than leaving it in a reporting view.

Sync LinkedIn Ads to Databricks

Sync LinkedIn Ads to Databricks with Polytomic

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Connect LinkedIn Ads in Polytomic

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Create a sync between LinkedIn Ads and Databricks

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