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Ingest data from ServiceNow

Learn how to create a managed ServiceNow ingestion pipeline using 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 CONNECTION privileges 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 CONNECTION privileges or ALL PRIVILEGES on the connection object.

    • You must have USE CATALOG privileges on the target catalog.

    • You must have USE SCHEMA and CREATE TABLE privileges on an existing schema or CREATE SCHEMA privileges on the target catalog.

  • To ingest from ServiceNow, you must first complete the steps in Configure ServiceNow for Databricks ingestion.

Create an ingestion pipeline​

Each ingested table is written to a streaming table.

Beta

You can filter rows during ingestion to improve performance and reduce data duplication. See Select rows to ingest.

  1. In the sidebar of the Databricks workspace, click Data Ingestion.
  2. On the Add data page, under Databricks connectors, click ServiceNow.
  3. On the Connection page of the ingestion wizard, select the connection that stores your ServiceNow access credentials. If you have the CREATE CONNECTION privilege on the metastore, you can click Plus icon. Create connection to create a new connection with the authentication details in Configure ServiceNow for Databricks ingestion.
  4. Click Next.
  5. On the Ingestion setup page, enter a unique name for the pipeline.
  6. Select a catalog and a schema to write event logs to. If you have USE CATALOG and CREATE SCHEMA privileges on the catalog, you can click Plus icon. Create schema in the drop-down menu to create a new schema.
  7. Click Create pipeline and continue.
  8. On the Source page, select the tables to ingest. If you select All tables, the connector writes all existing and future tables in the source schema to the destination schema. There is a maximum of 250 tables per pipeline.
  9. Click Save and continue.
  10. On the Destination page, select a catalog and a schema to load data into. If you have USE CATALOG and CREATE SCHEMA privileges on the catalog, you can click Plus icon. Create schema in the drop-down menu to create a new schema.
  11. Click Save and continue.
  12. (Optional) On the Schedules and notifications page, click Plus icon. Create schedule. Set the frequency to refresh the destination tables.
  13. (Optional) Click Plus icon. Add notification to set email notifications for pipeline operation success or failure, then click Save and run pipeline.

Examples​

Use these examples to configure your pipeline.

Ingest a single source table​

The following pipeline definition file ingests a single source table:

YAML
variables:
  dest_catalog:
    default: main
  dest_schema:
    default: ingest_destination_schema

# The main pipeline for servicenow_dab
resources:
  pipelines:
    pipeline_servicenow:
      name: servicenow_pipeline
      catalog: ${var.dest_catalog}
      schema: ${var.dest_schema}
      ingestion_definition:
        connection_name: <servicenow-connection>
        objects:
          # An array of objects to ingest from ServiceNow. This example ingests the incident table.
          - table:
              source_schema: default
              source_table: incident
              destination_catalog: ${var.dest_catalog}
              destination_schema: ${var.dest_schema}

Ingest multiple source tables​

The following pipeline definition file ingests multiple source tables:

YAML
variables:
  dest_catalog:
    default: main
  dest_schema:
    default: ingest_destination_schema

# The main pipeline for servicenow_dab
resources:
  pipelines:
    pipeline_servicenow:
      name: servicenow_pipeline
      catalog: ${var.dest_catalog}
      schema: ${var.dest_schema}
      ingestion_definition:
        connection_name: <servicenow-connection>
        objects:
          # An array of objects to ingest from ServiceNow. This example ingests the incident and problem tables.
          - table:
              source_schema: default
              source_table: incident
              destination_catalog: ${var.dest_catalog}
              destination_schema: ${var.dest_schema}
          - table:
              source_schema: default
              source_table: problem
              destination_catalog: ${var.dest_catalog}
              destination_schema: ${var.dest_schema}

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.

YAML
resources:
  jobs:
    servicenow_dab_job:
      name: servicenow_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_servicenow.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.

Additional resources​