Outlook connector
This feature is in Beta.
The managed Outlook connector in Lakeflow Connect allows you to ingest email data from Microsoft Outlook into Databricks.
Feature availability
Feature | Availability |
|---|---|
UI-based pipeline authoring |
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API-based pipeline authoring |
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Declarative Automation Bundles |
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Incremental ingestion |
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Unity Catalog governance |
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Orchestration using Databricks Workflows |
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SCD type 2 |
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Column selection and deselection |
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API-based row filtering |
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Automated schema evolution: New and deleted columns |
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Automated schema evolution: Data type changes |
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Automated schema evolution: Column renames |
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Automated schema evolution: New tables | N/A |
Maximum number of tables per pipeline | 1 |
Authentication methods
Authentication method | Availability |
|---|---|
OAuth U2M |
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OAuth M2M |
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OAuth (manual refresh token) |
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Basic authentication (username/password) |
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Basic authentication (API key) |
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Basic authentication (service account JSON key) |
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What to know before you start
Topic | Why it matters |
|---|---|
The workflow depends on your Databricks user persona:
| |
The steps to create a connection depend on the authentication method you choose. | |
The steps to create a pipeline depend on the interface. | |
The pipeline schedule depends on your latency and cost requirements. | |
Depending on your ingestion needs, the pipeline might use configurations like history tracking, column selection, and row filtering. Supported configurations vary by connector. See Feature availability. |
Start ingesting from Microsoft Outlook
The following table provides an overview of the end-to-end Outlook ingestion flow, based on user type:
User | Steps |
|---|---|
Admin |
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Non-admin | Use any supported interface to create a pipeline from an existing connection. See Ingest data from Microsoft Outlook. |