Ingest data from SurveyMonkey

Important

This feature is in Beta. To use it, a workspace admin must turn on Lakeflow Connect for SurveyMonkey from the Previews page. See Manage Azure Databricks previews.

Create a managed Lakeflow Connect pipeline that ingests SurveyMonkey data into Unity Catalog tables. Use the Azure Databricks UI, Declarative Automation Bundles, or a Azure Databricks notebook.

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 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.

  • 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 SurveyMonkey, first configure authentication from Azure Databricks and create a connection. See Configure authentication to SurveyMonkey and Create a SurveyMonkey connection.

Connector options

For the options the SurveyMonkey connector accepts, their scope, and the tables they apply to, see Connector options. Set pipeline-scoped options in source_configurations. See Examples for usage.

Create an ingestion pipeline

For the list of supported source tables, see Supported source tables.

Databricks UI

1. In the sidebar of the Azure Databricks workspace, click Data Ingestion.
2. On the Add data page, under Databricks connectors, click SurveyMonkey.
3. On the Connection page of the ingestion wizard, select the connection that stores your SurveyMonkey credentials. If you have the CREATE CONNECTION privilege on the metastore, click Create connection to create a connection with the credentials from Configure authentication to SurveyMonkey.
4. Click Next.
5. On the Ingestion setup page, enter a 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, click Create schema in the drop-down menu to create a schema.
7. Click Create pipeline and continue.
8. On the Source page, select the tables to ingest.
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, click Create schema in the drop-down menu to create a schema.
11. Click Save and continue.
12. (Optional) On the Schedules and notifications page, click Create schedule. Set the frequency to refresh the destination tables.
13. (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 SurveyMonkey 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?.

1. Create a bundle using the Databricks CLI:

   databricks bundle init
   

2. Add two new resource files to the bundle:

   • A pipeline definition file (for example, resources/surveymonkey_pipeline.yml). See pipeline.ingestion_definition and Examples.
   • A job definition file that controls the frequency of data ingestion (for example, resources/surveymonkey_job.yml).

3. Deploy the pipeline using the Databricks CLI:

   databricks bundle deploy
   

Databricks notebook

1. Import the following notebook into your Azure Databricks workspace:

   Get notebook

2. Leave cells one and two as they are.

3. Modify cell three with your pipeline configuration details. See pipeline.ingestion_definition and Examples.

4. Optionally configure advanced pipeline settings. See Common patterns for managed ingestion pipelines.

5. Click Run all.

Examples

The SurveyMonkey connector makes available 11 source tables in the default source schema. Ingest individual tables or the entire schema.

Ingest specific tables

Use this option to ingest a specific subset of tables, or to customize destination naming per table.

Declarative Automation Bundles

The following pipeline definition file ingests individual SurveyMonkey tables:

resources:
  pipelines:
    surveymonkey_pipeline:
      name: surveymonkey_pipeline
      catalog: 'main'
      target: 'surveymonkey_data'
      ingestion_definition:
        connection_name: surveymonkey_connection
        objects:
          - table:
              source_schema: 'default'
              source_table: 'surveys'
              destination_catalog: 'main'
              destination_schema: 'surveymonkey_data'
              destination_table: 'surveys'
          - table:
              source_schema: 'default'
              source_table: 'survey_responses'
              destination_catalog: 'main'
              destination_schema: 'surveymonkey_data'
              destination_table: 'survey_responses'
          - table:
              source_schema: 'default'
              source_table: 'contacts'
              destination_catalog: 'main'
              destination_schema: 'surveymonkey_data'
              destination_table: 'contacts'

Databricks notebook

The following pipeline specification ingests individual SurveyMonkey tables:

pipeline_name = "surveymonkey_pipeline"
connection_name = "<surveymonkey-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "surveys",
          "destination_catalog": "main",
          "destination_schema": "surveymonkey_data",
          "destination_table": "surveys"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "survey_responses",
          "destination_catalog": "main",
          "destination_schema": "surveymonkey_data",
          "destination_table": "survey_responses"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "contacts",
          "destination_catalog": "main",
          "destination_schema": "surveymonkey_data",
          "destination_table": "contacts"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest from a specific start date

Use the start_datetime option to control how far back the incremental tables read on their first sync.

Declarative Automation Bundles

The following pipeline definition file ingests SurveyMonkey tables from a specific start date:

resources:
  pipelines:
    surveymonkey_pipeline:
      name: surveymonkey_pipeline
      catalog: 'main'
      target: 'surveymonkey_data'
      ingestion_definition:
        connection_name: surveymonkey_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                start_datetime: '<start-datetime>'
        objects:
          - table:
              source_schema: 'default'
              source_table: 'surveys'
              destination_catalog: 'main'
              destination_schema: 'surveymonkey_data'
              destination_table: 'surveys'
          - table:
              source_schema: 'default'
              source_table: 'survey_responses'
              destination_catalog: 'main'
              destination_schema: 'surveymonkey_data'
              destination_table: 'survey_responses'

Databricks notebook

The following pipeline specification ingests SurveyMonkey tables from a specific start date:

pipeline_name = "surveymonkey_pipeline"
connection_name = "<surveymonkey-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "source_configurations": [
      {
        "api_source_connector_config": {
          "configs": {
            "start_datetime": "<start-datetime>"
          }
        }
      }
    ],
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "surveys",
          "destination_catalog": "main",
          "destination_schema": "surveymonkey_data",
          "destination_table": "surveys"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "survey_responses",
          "destination_catalog": "main",
          "destination_schema": "surveymonkey_data",
          "destination_table": "survey_responses"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest the entire schema

Use this option to ingest all SurveyMonkey source tables into a single destination schema with one declaration.

Declarative Automation Bundles

The following pipeline definition file ingests all supported SurveyMonkey tables into a destination schema:

resources:
  pipelines:
    surveymonkey_pipeline:
      name: surveymonkey_pipeline
      catalog: 'main'
      target: 'surveymonkey_data'
      ingestion_definition:
        connection_name: surveymonkey_connection
        objects:
          - schema:
              source_schema: 'default'
              destination_catalog: 'main'
              destination_schema: 'surveymonkey_data'

Databricks notebook

The following pipeline specification ingests all supported SurveyMonkey tables into a destination schema:

pipeline_name = "surveymonkey_pipeline"
connection_name = "<surveymonkey-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "schema": {
          "source_schema": "default",
          "destination_catalog": "main",
          "destination_schema": "surveymonkey_data"
        }
      }
    ]
  }
}
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.

Declarative Automation Bundles

resources:
  jobs:
    surveymonkey_job:
      name: surveymonkey_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: surveymonkey_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.surveymonkey_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.

Additional resources

• SurveyMonkey connector FAQ
• SurveyMonkey connector limitations
• SurveyMonkey connector reference
• Troubleshoot the SurveyMonkey connector