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Important
This feature is in Beta. Workspace admins can control access to this feature from the Previews page. See Manage Azure Databricks previews.
This page shows how to create a managed Outlook ingestion pipeline using Databricks Lakeflow Connect.
Requirements
To create an ingestion pipeline, you must first meet the following requirements:
Your workspace must be enabled for Unity Catalog.
Serverless compute must be enabled for your workspace. See Serverless compute requirements.
If you plan to create a new connection: You must have
CREATE CONNECTIONprivileges on the metastore. See Manage privileges in Unity Catalog.If the connector supports UI-based pipeline authoring, an admin can create the connection and the pipeline at the same time by completing the steps on this page. However, if the users who create pipelines use API-based pipeline authoring or are non-admin users, an admin must first create the connection in Catalog Explorer. See Connect to managed ingestion sources.
If you plan to use an existing connection: You must have
USE CONNECTIONprivileges orALL PRIVILEGESon the connection object.You must have
USE CATALOGprivileges on the target catalog.You must have
USE SCHEMAandCREATE TABLEprivileges on an existing schema orCREATE SCHEMAprivileges on the target catalog.
To ingest from Microsoft Outlook, you must first complete the steps in Configure authentication to Microsoft Outlook and create a connection using Outlook.
Create an ingestion pipeline
The connector supports a single table, email_messages, under the default schema. All mailboxes are merged into this table with a mailbox column distinguishing between them. For details about the destination schema, see Supported data.
Databricks UI
- In the sidebar of the Azure Databricks workspace, click Data Ingestion.
- On the Add data page, under Databricks connectors, click Outlook.
- On the Connection page of the ingestion wizard, select the connection that stores your Microsoft Outlook access credentials. If you have the
CREATE CONNECTIONprivilege on the metastore, you can click
Create connection to create a new connection with the authentication details in Configure authentication to Microsoft Outlook. - Click Next.
- On the Ingestion setup page, enter a unique name for the pipeline.
- Select a catalog and a schema to write event logs to. If you have
USE CATALOGandCREATE SCHEMAprivileges on the catalog, you can click Create schema in the drop-down menu to create a new schema. - Click Create pipeline and continue.
- On the Source page, select the
defaultschema to ingest theemail_messagestable. - Click Save and continue.
- On the Destination page, select a catalog and a schema to load data into. If you have
USE CATALOGandCREATE SCHEMAprivileges on the catalog, you can click Create schema in the drop-down menu to create a new schema. - Click Save and continue.
- (Optional) On the Schedules and notifications page, click Create schedule. Set the frequency to refresh the destination tables.
- (Optional) Click Add notification to set email notifications for pipeline operation success or failure, then click Save and run pipeline.
Declarative Automation Bundles
Use Declarative Automation Bundles to manage Outlook pipelines as code. Bundles can contain YAML definitions of jobs and tasks, are managed using the Databricks CLI, and can be shared and run in different target workspaces (such as development, staging, and production). For more information, see What are Declarative Automation Bundles?.
Create a bundle using the Databricks CLI:
databricks bundle initAdd two new resource files to the bundle:
- A pipeline definition file (for example,
resources/outlook_pipeline.yml). See pipeline.ingestion_definition and Examples. - A job definition file that controls the frequency of data ingestion (for example,
resources/outlook_job.yml).
- A pipeline definition file (for example,
Deploy the pipeline using the Databricks CLI:
databricks bundle deploy
Databricks notebook
Import the following notebook into your Azure Databricks workspace:
Leave cell one as-is.
Modify cell three with your pipeline configuration details. See pipeline.ingestion_definition and Examples.
Click Run all.
Examples
Use these examples to configure your pipeline. See Connector options for the full list of available outlook_options.
Ingest all email messages (default Inbox folder)
This example ingests email messages from the Inbox folder of all accessible mailboxes in the tenant.
Declarative Automation Bundles
variables:
dest_catalog:
default: main
dest_schema:
default: ingest_destination_schema
resources:
pipelines:
pipeline_outlook:
name: outlook_pipeline
catalog: ${var.dest_catalog}
schema: ${var.dest_schema}
ingestion_definition:
connection_name: <outlook-connection>
objects:
- schema:
source_schema: default
destination_catalog: ${var.dest_catalog}
destination_schema: ${var.dest_schema}
connector_options:
outlook_options:
start_date: '2024-01-01'
Databricks notebook
pipeline_spec = """
{
"name": "<pipeline-name>",
"ingestion_definition": {
"connection_name": "<outlook-connection>",
"objects": [
{
"schema": {
"source_schema": "default",
"destination_catalog": "main",
"destination_schema": "ingest_destination_schema",
"connector_options": {
"outlook_options": {
"start_date": "2024-01-01"
}
}
}
}
]
},
"channel": "PREVIEW"
}
"""
create_pipeline(pipeline_spec)
Ingest from specific mailboxes with filters
This example ingests email messages from specific mailboxes, filtered by folder, sender, and subject.
Declarative Automation Bundles
variables:
dest_catalog:
default: main
dest_schema:
default: ingest_destination_schema
resources:
pipelines:
pipeline_outlook:
name: outlook_pipeline
catalog: ${var.dest_catalog}
schema: ${var.dest_schema}
ingestion_definition:
connection_name: <outlook-connection>
objects:
- schema:
source_schema: default
destination_catalog: ${var.dest_catalog}
destination_schema: ${var.dest_schema}
connector_options:
outlook_options:
include_mailboxes:
- user1@contoso.com
- user2@contoso.com
include_folders:
- Inbox
- Sent Items
include_senders:
- alerts@vendor.com
- noreply@system.io
include_subjects:
- SubjectExactMatch
- SubjectPrefixMatch*
start_date: '2024-01-01'
body_format: TEXT_PLAIN
attachment_mode: NON_INLINE_ONLY
Databricks notebook
pipeline_spec = """
{
"name": "<pipeline-name>",
"ingestion_definition": {
"connection_name": "<outlook-connection>",
"objects": [
{
"schema": {
"source_schema": "default",
"destination_catalog": "main",
"destination_schema": "ingest_destination_schema",
"connector_options": {
"outlook_options": {
"include_mailboxes": ["user1@contoso.com", "user2@contoso.com"],
"include_folders": ["Inbox", "Sent Items"],
"include_senders": ["alerts@vendor.com", "noreply@system.io"],
"include_subjects": ["SubjectExactMatch", "SubjectPrefixMatch*"],
"start_date": "2024-01-01",
"body_format": "TEXT_PLAIN",
"attachment_mode": "NON_INLINE_ONLY"
}
}
}
}
]
},
"channel": "PREVIEW"
}
"""
create_pipeline(pipeline_spec)
Ingest the email_messages table explicitly
This example selects the email_messages table directly instead of targeting the schema.
Declarative Automation Bundles
variables:
dest_catalog:
default: main
dest_schema:
default: ingest_destination_schema
resources:
pipelines:
pipeline_outlook:
name: outlook_pipeline
catalog: ${var.dest_catalog}
schema: ${var.dest_schema}
ingestion_definition:
connection_name: <outlook-connection>
objects:
- table:
source_schema: default
source_table: email_messages
destination_catalog: ${var.dest_catalog}
destination_schema: ${var.dest_schema}
destination_table: my_email_messages
connector_options:
outlook_options:
start_date: '2024-01-01'
Databricks notebook
pipeline_spec = """
{
"name": "<pipeline-name>",
"ingestion_definition": {
"connection_name": "<outlook-connection>",
"objects": [
{
"table": {
"source_schema": "default",
"source_table": "email_messages",
"destination_catalog": "main",
"destination_schema": "ingest_destination_schema",
"destination_table": "my_email_messages",
"connector_options": {
"outlook_options": {
"start_date": "2024-01-01"
}
}
}
}
]
},
"channel": "PREVIEW"
}
"""
create_pipeline(pipeline_spec)
Bundle job definition file
The following is an example job definition file to use with Declarative Automation Bundles. The job runs every day, exactly one day from the last run.
resources:
jobs:
outlook_dab_job:
name: outlook_dab_job
trigger:
periodic:
interval: 1
unit: DAYS
email_notifications:
on_failure:
- <email-address>
tasks:
- task_key: refresh_pipeline
pipeline_task:
pipeline_id: ${resources.pipelines.pipeline_outlook.id}
Common patterns
For advanced pipeline configurations, see Common patterns for managed ingestion pipelines.
Next steps
Start, schedule, and set alerts on your pipeline. See Common pipeline maintenance tasks.