Load LinkedIn Pages data into Databricks
Send your company page's follower statistics to Databricks with OWOX Data Marts: followers by seniority, industry, function and location, and daily gains split into organic and paid. Set them beside ad spend, web analytics and CRM to see what the page brings.
LinkedIn PagesConnectedFollower Statist…Time-Bound Follo…
DatabricksOrgan…TimeTimeDaily 09:00LinkedIn Pages Endpoints
2 LinkedIn Pages endpoints you can import. Each lands in its own table in your data warehouse.
Why join LinkedIn Pages with ad spend and your CRM in Databricks
LinkedIn shows how many people followed your page. It does not show whether the right people followed, what paid growth cost, or whether pipeline grew in the same industries. Bring page statistics, ad spend and CRM records into one warehouse to see it.
- Set paid follower gain beside LinkedIn Ads spend
- See who follows you by seniority, industry, function and location
- Set paid follower gain beside LinkedIn Ads spend in Databricks
LinkedIn PagesPage followersYour data warehouseFollowersAd spendPipelineJoined by idFollower gainedPipeline growsOne query links growth to spend.Step 1: Connect LinkedIn Pages and choose what to load
Create a Data Mart, choose LinkedIn Pages as the source, authorize once, open an endpoint and tick only the fields you need.
- Two endpoints and 18 fields to choose from
- Time-Bound Follower Statistics holds the daily organic and paid gains
LinkedIn AdsLinkedIn Pages
Facebook AdsAuthorizeConnectedEndpointTime-Bound Follow…Follower Statisti…Time-Bound Follow…organization_urntime_range_starttime_range_endorganic_follower_gainpaid_follower_gainStep 2: Load LinkedIn Pages into Databricks tables
The catalog defaults to main, and the schema and table are pre-filled; change any of them. Run it and the first load lands in your own Databricks workspace.
- Each endpoint gets its own table: the current follower breakdown and the daily gains stay apart
- Every run is logged in Run History
DestinationCatalogmainSchemalinkedin_pagesTablefollower_statistics_time_boundRunTablemain.linkedin_pages.follower_…Step 3: Open the output schema in plain words
The first run gives you the output schema: every field with an alias and a readable description, not an API identifier. Your quality checks can read it too.
- Descriptions say what a field holds, for example the number of organic followers gained in the period
- Teammates can query the table without the API reference
main.linkedin_pages.follower_statistics…Output schemaFieldDescriptionorganization_urnOrganization URNtime_range_startStart timestamp of the time rangetime_range_endEnd timestamp of the time rangeorganic_follower_gainNumber of organic followers…paid_follower_gainNumber of paid followers gained…Step 4: Keep the page tables linked
Follower Statistics and Time-Bound Follower Statistics are separate tables that share the organization URN. Joined in SQL, they show who follows the page and how fast it grows.
- Join on organization_urn, the page's organization identifier
- Add ad spend and CRM tables to the same model
Follower Statisti…organization_urnsegment_namesegment_valuepaid_follower_co…Time-Bound Follow…organization_urntime_range_startorganic_follower…paid_follower_ga…Step 5: Refresh on a schedule and backfill the history
Set it once. A scheduled refresh keeps the Databricks tables current, and a manual run loads a past period of your choice.
- Daily runs with no pipeline to maintain
- Backfill history the first time you connect
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 table
LinkedIn Pa…Data 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 Pages in Databricks
Notebooks over the LinkedIn API and generic ETL tools get the numbers in. OWOX also keeps them in your tables, readable and ready to model.
- You own the data. It lands in your own Databricks workspace, in tables you control.
- Two tables, two views. The current follower breakdown and the daily gains load apart, so each stays simple.
- Described columns. Fields say what they hold, such as organic followers gained in the period.
- Joined by the organization URN. Ready to set beside ad spend and CRM by date.
- 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.
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