Meta Ads ingestion connector limitations

Beta

The Meta Ads connector is in Beta. Workspace admins can control access to this feature from the Previews page. See Manage Databricks previews.

Learn about limitations and considerations for ingesting data from Meta Ads using Databricks Lakeflow Connect.

General SaaS connector limitations

The limitations in this section apply to all SaaS connectors in Lakeflow Connect.

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 Lakeflow Spark Declarative Pipelines 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.
Column-level selection and deselection are API-only.
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.
With SCD type 1 enabled, deletes don't produce an explicit delete event in the change data feed. For auditable deletions, use SCD type 2 if the connector supports it. For details, see Example: SCD type 1 and SCD type 2 processing with CDF source data.

The following limitations apply during the Beta release:

No UI support: You must create pipelines using the Databricks CLI, APIs, SDKs, or Databricks Asset Bundles. UI-based pipeline creation isn't available.
Instagram Ads not supported: The connector only supports Facebook Ads. Instagram Ads aren't supported.
Fixed attribution windows: Attribution windows are fixed at 7 days for click and 1 day for view. Custom attribution windows aren't configurable.
Daily time increment only: The connector uses daily time increments for ad_insights. Hourly time increments aren't supported.

OAuth tokens are managed automatically by the connector. If a token expires or is revoked, you must reauthorize the connection.

The connector is subject to Insights API rate limits. If you exceed these limits, the connector retries with exponential backoff.
High-frequency pipeline schedules can cause rate limit issues. Databricks recommends scheduling pipelines to run no more frequently than hourly during Beta.

Meta Ads retains performance data for a limited time. The connector can only ingest data that is available through the Insights API.
Historical data older than the Meta retention period cannot be ingested. For retention period details, see Breakdown availability in the Meta documentation.

There is a maximum of 250 objects per pipeline. However, there is no limit on the number of rows or columns within these objects.
The connector doesn't detect deletes. If records are deleted from Meta Ads, they remain in the destination table unless you perform a full refresh.

The ad_insights object requires explicit configuration for breakdowns and action breakdowns. Without configuration, the connector retrieves aggregated data at the specified granularity level.
Some breakdown combinations may not be supported by the Insights API. Check the Meta documentation for valid breakdown combinations.
The connector retrieves ad_insights data with a 3-day attribution window by default. Data for recent dates may be incomplete until the attribution window closes.

The following features aren't supported during Beta: