Question 1

Which entities and metrics are available?

Accepted Answer

Accounts, campaign groups, campaigns and creatives on the structural side, and analytics pivoted by campaign, creative, company and other dimensions on the reporting side. Metrics cover impressions, clicks, spend, conversions and the video and engagement measures LinkedIn exposes. The structural entities matter more than people expect, because analytics rows reference identifiers that mean nothing until joined to the campaign and creative tables.

Question 2

How does incremental reading work, and are restatements picked up?

Accepted Answer

Analytics are fetched in daily windows and checkpointed by date, then recent days are reread on a configurable lookback. The default is two days, and member company reporting forces a minimum of four because those figures settle more slowly. Restatements landing outside the lookback are not refreshed automatically, so if your attribution moves later than the window, widen it. Resource entities such as campaigns and creatives are read in full each run.

Question 3

Can we get demographic breakdowns?

Accepted Answer

Yes, within what LinkedIn permits. Pivots by company, industry, seniority, job function and company size are available, and member company reporting is the one people care about most because it turns advertising into something you can match against target accounts. LinkedIn applies its own privacy thresholds, so small segments may be suppressed at the source and will simply be absent rather than zero.

Question 4

If we need to reload history, what are the resync options?

Accepted Answer

Three, and they behave differently. An ordinary resync alters and merges in place, keeping the existing table and its tuning. Resync and truncate keeps the table schema but replaces all the rows, which is the usual choice when you want a clean reload without disturbing downstream grants. Resync and rebuild drops and recreates the table from scratch, which is the heaviest option and the one that discards any custom sort or distribution keys you had set. Whichever you pick, the reload runs through the same ETL configuration rather than a separate job.

Question 5

Why replicate LinkedIn Ads into a data warehouse?

Accepted Answer

Because B2B advertising is judged on pipeline, not on clicks, and pipeline lives in the CRM. Joining campaign spend to opportunities requires both datasets in one place, and the account level pivots only pay off when you can match them against your own account list. ETL into a data warehouse gives you that, plus a stable daily history rather than a reporting view that keeps adjusting underneath you.

Sync LinkedIn Ads to Redshift

Sync LinkedIn Ads to Redshift with Polytomic

Daily analytics windows

Campaign and company pivots

Backfill from a date cutoff

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