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LinkedIn Ads connector limitations

Beta

This feature is in Beta.

Learn about known limitations when using the managed LinkedIn Ads connector in Lakeflow Connect.

General limitations​

  • When you run a scheduled pipeline, alerts don't trigger immediately. Instead, they trigger when the next update runs.
  • When a source table is deleted, the destination table is not automatically deleted. You must delete the destination table manually. This behavior is not consistent with Spark Declarative Pipelines on Lakeflow behavior.
  • During source maintenance periods, Databricks might not be able to access your data.
  • If a source table name conflicts with an existing destination table name, the pipeline update fails.
  • Multi-destination pipeline support is API-only.
  • You can optionally rename a table that you ingest. If you rename a table in your pipeline, it becomes an API-only pipeline, and you can no longer edit the pipeline in the UI.
  • If you select a column after a pipeline has already started, the connector does not automatically backfill data for the new column. To ingest historical data, manually run a full refresh on the table.
  • Databricks can't ingest two or more tables with the same name in the same pipeline, even if they come from different source schemas.
  • The source system assumes that the cursor columns are monotonically increasing.
  • The connector ingests raw data without transformations. Use downstream Spark Declarative Pipelines on Lakeflow pipelines for transformations.

Supported tables​

The connector supports the twelve predefined tables listed in LinkedIn Ads connector reference. You cannot define custom reports.

Incremental ingestion​

  • Only the seven prebuilt report tables support incremental ingestion. The five entity tables are fully refreshed on each pipeline update.
  • Entity tables do not support history tracking (SCD type 2). Only the current state is stored.
  • A report that has already synced resumes from its own cursor, so lowering sync_start_date after the first update does not backfill it. Only newly appearing accounts or campaigns start from the new date. To re-ingest earlier history, run a full refresh on the table.
  • Deleted campaigns and ad accounts keep their entry in the connector's tracked state and simply stop advancing. Rows already ingested into the destination table remain there; the connector does not delete them.

Data retention​

LinkedIn retains performance data for 10 years and professional-demographic data for 2 years. A sync_start_date earlier than the horizon that applies to the report is rejected, because data before it cannot be retrieved from the API.

Report configuration​

  • Each prebuilt report has a fixed configuration. Its pivot, time granularity, and metric set are set per report and cannot be overridden.
  • The five demographic reports are aggregated per campaign rather than per creative, because LinkedIn's demographic responses do not carry the creative.
  • Monthly reports cannot be narrowed below a calendar month. LinkedIn returns no rows for a sub-month range at monthly granularity, so a sync_start_date that falls mid-month is aligned to the first of that month, and the row returned for that month is the full-month aggregate.

Time zone​

Report date boundaries are evaluated in UTC rather than in the ad account's time zone, and you cannot override this.

Authentication​

  • The connector reads only the ad accounts that the authorizing user has a role on. To ingest an ad account, ensure the user who authorizes the connection has a role on it.
  • LinkedIn issues access tokens with a 60-day lifespan. LinkedIn also invalidates a user's existing tokens for an app when a new authorization requests a different set of scopes.
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