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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.
Learn how to create a managed Smartsheet 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 Smartsheet, you must first complete the steps in Create a Smartsheet connection.
Create an ingestion pipeline
Each source object (sheet or report) is ingested into a streaming table. For a list of supported source object types, see Supported source object types.
Databricks UI
- In the sidebar of the Azure Databricks workspace, click Data Ingestion.
- On the Add data page, under Databricks connectors, click Smartsheet.
- On the Connection page of the ingestion wizard, select the connection that stores your Smartsheet 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 Create a Smartsheet connection. - Click Next.
- On the Ingestion setup page, enter a 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 sheets or reports to ingest.
- 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 Smartsheet 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/smartsheet_pipeline.yml). See pipeline.ingestion_definition and Examples. - A job definition file that controls the frequency of data ingestion (for example,
resources/smartsheet_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.
Ingest a single source object
The source_schema is always "default" for Smartsheet. The source_table is the 16-digit sheet or report ID, which you can find in the Smartsheet URL or sheet properties.
Declarative Automation Bundles
The following pipeline definition file ingests a single Smartsheet sheet:
variables:
dest_catalog:
default: main
dest_schema:
default: ingest_destination_schema
resources:
pipelines:
pipeline_smartsheet:
name: smartsheet_pipeline
catalog: ${var.dest_catalog}
schema: ${var.dest_schema}
ingestion_definition:
connection_name: <smartsheet-connection>
objects:
- table:
source_schema: default
source_table: '7483920174635241'
destination_catalog: ${var.dest_catalog}
destination_schema: ${var.dest_schema}
Databricks notebook
The following is an example pipeline specification that ingests a single source object:
pipeline_spec = """
{
"name": "<pipeline-name>",
"catalog": "<catalog-name-for-event-logs>",
"schema": "<schema-name-for-event-logs>",
"ingestion_definition": {
"connection_name": "<smartsheet-connection>",
"objects": [
{
"table": {
"source_schema": "default",
"source_table": "7483920174635241",
"destination_catalog": "main",
"destination_schema": "ingest_destination_schema"
}
}
]
},
"channel": "PREVIEW"
}
"""
create_pipeline(pipeline_spec)
Ingest multiple source objects
Declarative Automation Bundles
The following pipeline definition file ingests multiple sheets or reports:
variables:
dest_catalog:
default: main
dest_schema:
default: ingest_destination_schema
resources:
pipelines:
pipeline_smartsheet:
name: smartsheet_pipeline
catalog: ${var.dest_catalog}
schema: ${var.dest_schema}
ingestion_definition:
connection_name: <smartsheet-connection>
objects:
- table:
source_schema: default
source_table: '7483920174635241'
destination_catalog: ${var.dest_catalog}
destination_schema: ${var.dest_schema}
- table:
source_schema: default
source_table: '3219847562038416'
destination_catalog: ${var.dest_catalog}
destination_schema: ${var.dest_schema}
Databricks notebook
The following is an example pipeline specification that ingests multiple source objects:
pipeline_spec = """
{
"name": "<pipeline-name>",
"catalog": "<catalog-name-for-event-logs>",
"schema": "<schema-name-for-event-logs>",
"ingestion_definition": {
"connection_name": "<smartsheet-connection>",
"objects": [
{
"table": {
"source_schema": "default",
"source_table": "7483920174635241",
"destination_catalog": "main",
"destination_schema": "ingest_destination_schema"
}
},
{
"table": {
"source_schema": "default",
"source_table": "3219847562038416",
"destination_catalog": "main",
"destination_schema": "ingest_destination_schema"
}
}
]
},
"channel": "PREVIEW"
}
"""
create_pipeline(pipeline_spec)
Use connector and table options
Use connector_options and table_configuration to control schema enforcement, column selection, and row filtering.
Declarative Automation Bundles
The following pipeline definition file ingests a sheet with column exclusion and row filtering:
variables:
dest_catalog:
default: main
dest_schema:
default: hr_data
resources:
pipelines:
pipeline_smartsheet:
name: smartsheet_pipeline
catalog: ${var.dest_catalog}
schema: ${var.dest_schema}
ingestion_definition:
connection_name: <smartsheet-connection>
connector_options:
enforce_schema: false
objects:
- table:
source_schema: default
source_table: '7483920174635241'
destination_catalog: ${var.dest_catalog}
destination_schema: ${var.dest_schema}
destination_table: employee_roster
table_configuration:
exclude_columns:
- Employee Name
- Status
row_filter: "row_number <= 10 OR Department != 'Engineering'"
Databricks notebook
The following is an example pipeline specification that uses connector options and table configuration:
pipeline_spec = """
{
"name": "my_smartsheet_pipeline",
"catalog": "your_pipeline_event_log_catalog_name",
"schema": "your_pipeline_event_log_schema_name",
"ingestion_definition": {
"connection_name": "my_smartsheet_connection",
"connector_options": {
"enforce_schema": false
},
"objects": [
{
"table": {
"source_schema": "default",
"source_table": "7483920174635241",
"destination_catalog": "main",
"destination_schema": "hr_data",
"destination_table": "employee_roster",
"table_configuration": {
"exclude_columns": ["Employee Name", "Status"],
"row_filter": "row_number <= 10 OR Department != 'Engineering'"
}
}
}
]
},
"channel": "PREVIEW"
}
"""
create_pipeline(pipeline_spec)
Bundle job definition file
The following is an example job definition file for use with Declarative Automation Bundles. The job runs every day, exactly one day from the last run.
resources:
jobs:
smartsheet_dab_job:
name: smartsheet_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_smartsheet.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.