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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 Workiva ingestion pipeline using Lakeflow Connect.
Requirements
To create an ingestion pipeline, 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.
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.
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 Workiva, first configure authentication from Azure Databricks and create a connection. See Configure authentication to Workiva and Create a Workiva connection.
Create an ingestion pipeline
The Workiva connector makes available three source tables — activities, users, and roles — under the default source schema. For details, see Supported source tables.
Databricks UI
- In the sidebar of the Azure Databricks workspace, click Data Ingestion.
- On the Add data page, under Databricks connectors, click Workiva.
- On the Connection page of the ingestion wizard, select the connection that stores your Workiva credentials. If you have the
CREATE CONNECTIONprivilege on the metastore, click
Create connection to create a connection with the credentials from Configure authentication to Workiva. - 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, click Create schema in the drop-down menu to create a schema. - Click Create pipeline and continue.
- On the Source page, select the tables to ingest:
activities,users, androles. - 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, click Create schema in the drop-down menu to create a 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.
- Click Save and run pipeline.
Declarative Automation Bundles
Use Declarative Automation Bundles to manage Workiva 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/workiva_pipeline.yml). See pipeline.ingestion_definition and Examples. - A job definition file that controls the frequency of data ingestion (for example,
resources/workiva_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:
Create a Workiva ingestion pipeline
Leave cells one and two as they are. Do not modify.
Modify cell three with your pipeline configuration details. See pipeline.ingestion_definition and Examples.
Optionally configure advanced pipeline settings. See Common patterns for managed ingestion pipelines.
Click Run all.
Examples
The Workiva connector makes available the activities, users, and roles tables in the default source schema. For details, see Supported source tables.
Ingest the Workiva tables
Declarative Automation Bundles
The following pipeline definition file ingests all three Workiva tables:
resources:
pipelines:
workiva_pipeline:
name: workiva_pipeline
catalog: 'main'
target: 'workiva_data'
ingestion_definition:
connection_name: workiva_connection
objects:
- table:
source_schema: 'default'
source_table: 'activities'
destination_catalog: 'main'
destination_schema: 'workiva_data'
destination_table: 'activities'
- table:
source_schema: 'default'
source_table: 'users'
destination_catalog: 'main'
destination_schema: 'workiva_data'
destination_table: 'users'
- table:
source_schema: 'default'
source_table: 'roles'
destination_catalog: 'main'
destination_schema: 'workiva_data'
destination_table: 'roles'
Databricks notebook
The following pipeline specification ingests all three Workiva tables:
pipeline_name = "workiva_pipeline"
connection_name = "<workiva-connection>"
pipeline_spec = {
"name": pipeline_name,
"ingestion_definition": {
"connection_name": connection_name,
"objects": [
{
"table": {
"source_schema": "default",
"source_table": "activities",
"destination_catalog": "main",
"destination_schema": "workiva_data",
"destination_table": "activities"
}
},
{
"table": {
"source_schema": "default",
"source_table": "users",
"destination_catalog": "main",
"destination_schema": "workiva_data",
"destination_table": "users"
}
},
{
"table": {
"source_schema": "default",
"source_table": "roles",
"destination_catalog": "main",
"destination_schema": "workiva_data",
"destination_table": "roles"
}
}
]
}
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)
Ingest activities from a specific start datetime
The activities table accepts the optional start_datetime connector option, a UTC ISO-8601 timestamp that sets the start of the first-sync backfill window. Omit it to backfill the previous 365 days. Only the activities table accepts this option. For details, see Connector options.
Declarative Automation Bundles
resources:
pipelines:
workiva_pipeline:
name: workiva_pipeline
catalog: 'main'
target: 'workiva_data'
ingestion_definition:
connection_name: workiva_connection
objects:
- table:
source_schema: 'default'
source_table: 'activities'
destination_catalog: 'main'
destination_schema: 'workiva_data'
destination_table: 'activities'
connector_options:
api_source_connector_options:
options:
start_datetime: '<start-datetime>'
Databricks notebook
pipeline_spec = {
"name": "workiva_pipeline",
"ingestion_definition": {
"connection_name": "<workiva-connection>",
"objects": [
{
"table": {
"source_schema": "default",
"source_table": "activities",
"destination_catalog": "main",
"destination_schema": "workiva_data",
"destination_table": "activities",
"connector_options": {
"api_source_connector_options": {
"options": {
"start_datetime": "<start-datetime>"
}
}
}
}
}
]
}
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)
Declarative Automation Bundles job definition file
The following is an example job definition file for use with Declarative Automation Bundles. The job runs daily.
resources:
jobs:
workiva_job:
name: workiva_job
schedule:
quartz_cron_expression: '0 0 0 * * ?'
timezone_id: 'UTC'
tasks:
- task_key: workiva_ingestion
pipeline_task:
pipeline_id: ${resources.pipelines.workiva_pipeline.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.