Land LinkedIn Ads data in Databricks
LinkedIn Ads to Databricks with OWOX Data Marts: one table per endpoint in your own catalog and schema, readable field descriptions, a scheduled refresh. Then join ad spend to your web analytics and CRM data to see which ads became deals.
LinkedIn AdsConnectedAd AccountsAd AnalyticsAd Analytics by…Ad Analytics by…
DatabricksDateCostClicksDaily 09:00LinkedIn Ads Endpoints
8 LinkedIn Ads endpoints you can import. Each lands in its own table in your data warehouse.
Ad Accounts
Your ad account settings, billing details, currency, and account status.
14 fieldsAd Analytics
Daily performance metrics for your campaigns — impressions, clicks, spend, and conversions.
104 fieldsTime seriesAd Analytics by Country
Daily campaign performance by the country of the members who saw your ads — impressions, clicks, spend, and conversions per country.
100 fieldsTime seriesAd Analytics by County
Daily campaign performance by the county of the members who saw your ads — impressions, clicks, spend, and conversions per county.
100 fieldsTime seriesAd Analytics by Region
Daily campaign performance by the region of the members who saw your ads — impressions, clicks, spend, and conversions per region.
100 fieldsTime seriesCampaign Groups
Top-level containers that group campaigns with shared objectives, budgets, and scheduling.
14 fieldsCampaigns
Individual campaigns defining your targeting, ad format, bid strategy, and budget.
29 fieldsCreatives
The visual and copy elements of your ads shown to members across LinkedIn.
14 fields
Why bring LinkedIn Ads into Databricks with your CRM data
Run an ad today and the deal may close a year from now, and the ad account will not connect the two. Ad spend, website touch points and CRM deals need to be tables in one warehouse so a single query can follow the path.
- Put LinkedIn spend next to Facebook, Google and Microsoft Ads costs in Databricks
- Tie a click and a form submit to the CRM deal that followed
- Keep the data in your own Databricks tables, ready for a data model
LinkedIn AdsAd spendYour data warehouseAdTouch pointDealJoined by idAd clickedDeal months laterOne query links the ad to the deal.Step 1: Connect LinkedIn Ads and pick the fields
Facebook AdsLinkedIn Ads
Reddit AdsAuthorizeConnectedEndpointAd AnalyticsAd AccountsAd Analytics by C…Ad Analytics by C…Ad AnalyticsdateRangeStartcostInUsdcostInLocalCurrencyclicksexternalWebsiteConversionsqualifiedLeadsStep 2: Load LinkedIn Ads into a Databricks table
Choose the catalog, schema and table. They are pre-filled, starting from the main catalog, and you can change them. Run it and the first load lands in your own Databricks workspace.
- Each endpoint gets its own table, not one wide table of every field
- Every run is logged in Run History
DestinationCatalogmainSchemalinkedin_adsTablead_analyticsRunTablemain.linkedin_ads.ad_analyticsStep 3: Open the output schema in plain words
The first run gives you the output schema: every field with an alias and a business-friendly description. Your quality checks can read it too.
- Descriptions follow the LinkedIn Ads documentation
- Teammates can read the table without the API reference
main.linkedin_ads.ad_analyticsOutput schemaFieldDescriptiondateRangeStartStart date of the report data…costInUsdCost in USD based on the pivot…costInLocalCurrencyCost in the account's local…clicksThe count of chargeable clicks…externalWebsiteConversionsTotal number of times users…qualifiedLeadsThe count of qualified leads…Step 4: Keep the LinkedIn Ads tables linked in Databricks
Campaign groups, campaigns, creatives and ad analytics stay separate tables, joined by ids. In SQL they become one LinkedIn Ads model.
- LinkedIn stores links as URNs such as urn:li:sponsoredAccount:{id}; extract the id to join
- Join the model to web analytics and CRM tables
Campaign GroupsnameaccountidCampaignsidnamecampaignGroupCreativesidcampaignisServingAd AnalyticscostInUsdpivotValuesStep 5: Run on a schedule and backfill the history
Set it once. A daily scheduled refresh keeps the Databricks tables current, and a manual run loads a past period of your choice.
- Incremental load for the current day
- Backfill a custom period for history
- No pipeline to maintain
Step 6: Reuse the Databricks tables in every tool
One set of tables feeds your model, , , assistants like Claude and ChatGPT and AI insights in Slack or email.
- Define the numbers once
- Every report reads the same tables
LinkedIn AdsData MartPublished
Google Sheets
Data Studio dashboards
Claude, ChatGPT
Slack, email
- 0.43.0Compare LinkedIn Ads results across markets: spend by country, region, and county
- 0.43.0The Sheet link of a Google Sheets source opens the imported tab, not the first one
- 0.43.0Connect a Google Sheets plan with one Google sign-in
- 0.41.0Rotating a warehouse key is one change: Storages share reusable Credentials
- 0.41.0Dates from Google Sheets arrive as dates, not text
Why OWOX for LinkedIn Ads in Databricks
Notebooks with API calls and generic ETL tools get the data in. OWOX also keeps it in your tables, readable and ready to model.
- You own the data. It lands in your own Databricks workspace, in tables you control.
- A business-friendly schema. Aliases and plain descriptions instead of API identifiers.
- Ready for a data model. Separate tables per endpoint, joined by ids, built to meet web analytics and CRM.
- Maintained for you. No jobs to repair when LinkedIn changes its API.
- Free to self-host. The open-source OWOX Data Marts runs in your own infrastructure.
You are booked. The invitation is on its way to , with the calendar invite and the meeting link.