Ingest data from Monday.com

Beta

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

This page shows how to create a managed Monday.com 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 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 Monday.com, first configure authentication from Databricks and create a connection. See Configure authentication to Monday.com and Create a Monday.com connection.

Connector options

The Monday.com connector supports pipeline-scoped and table-scoped options. Set pipeline-scoped options in the source_configurations block and table-scoped options in the connector_options block of each table in your pipeline definition. See Examples for usage.

Option	Scope	Required	Applies to	Description
start_datetime	Pipeline	No	activity_logs table	Earliest timestamp from which to ingest activity logs, in ISO 8601 UTC format (for example, 2026-01-01T00:00:00Z). Defaults to the Unix epoch, so the first sync ingests all available history. Has no effect on the full-refresh batch tables.
board_ids	Table	No	boards and activity_logs tables	Board IDs to ingest, specified as a JSON array of ID strings (for example, ["1234567890","1234567891"]). When set, the connector ingests only the specified boards and the activity logs for those boards. When omitted, the connector ingests all boards in the account. Set this option on both tables so that each covers the same boards.

Option	Scope	Required	Applies to	Description
start_datetime	Pipeline	No	activity_logs table	Earliest timestamp from which to ingest activity logs, in ISO 8601 UTC format (for example, 2026-01-01T00:00:00Z). Defaults to the Unix epoch, so the first sync ingests all available history. Has no effect on the full-refresh batch tables.
board_ids	Table	No	boards and activity_logs tables	Board IDs to ingest, specified as a JSON array of ID strings (for example, ["1234567890","1234567891"]). When set, the connector ingests only the specified boards and the activity logs for those boards. When omitted, the connector ingests all boards in the account. Set this option on both tables so that each covers the same boards.

Create an ingestion pipeline

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

Databricks UI

1. In the sidebar of the Databricks workspace, click Data Ingestion.
2. On the Add data page, under Databricks connectors, click Monday.com.
3. On the Connection page of the ingestion wizard, select the connection that stores your Monday.com 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 Monday.com.
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 Monday.com 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:

   Bash

   databricks bundle init

2. Add two new resource files to the bundle:

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

3. Deploy the pipeline using the Databricks CLI:

   Bash

   databricks bundle deploy

Databricks notebook

1. Import the following notebook into your Databricks workspace:

   Open notebook in new tabOpen in Databricks

2. Leave cells one and two as they are. Do not modify.

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 Monday.com connector makes available 7 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 Monday.com tables:

YAML

resources:
  pipelines:
    monday_com_pipeline:
      name: monday_com_pipeline
      catalog: 'main'
      target: 'monday_com_data'
      ingestion_definition:
        connection_name: monday_com_connection
        objects:
          - table:
              source_schema: 'default'
              source_table: 'boards'
              destination_catalog: 'main'
              destination_schema: 'monday_com_data'
              destination_table: 'boards'
          - table:
              source_schema: 'default'
              source_table: 'activity_logs'
              destination_catalog: 'main'
              destination_schema: 'monday_com_data'
              destination_table: 'activity_logs'
          - table:
              source_schema: 'default'
              source_table: 'users'
              destination_catalog: 'main'
              destination_schema: 'monday_com_data'
              destination_table: 'users'

Databricks notebook

The following pipeline specification ingests individual Monday.com tables:

Python

pipeline_name = "monday_com_pipeline"
connection_name = "<monday-com-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "boards",
          "destination_catalog": "main",
          "destination_schema": "monday_com_data",
          "destination_table": "boards"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "activity_logs",
          "destination_catalog": "main",
          "destination_schema": "monday_com_data",
          "destination_table": "activity_logs"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "users",
          "destination_catalog": "main",
          "destination_schema": "monday_com_data",
          "destination_table": "users"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest specific boards

Use the board_ids connector option to scope ingestion to a subset of boards. Set it on both the boards table and the activity_logs table so that each table covers the same boards.

Declarative Automation Bundles

The following pipeline definition file ingests only the specified Monday.com boards:

YAML

resources:
  pipelines:
    monday_com_pipeline:
      name: monday_com_pipeline
      catalog: 'main'
      target: 'monday_com_data'
      ingestion_definition:
        connection_name: monday_com_connection
        objects:
          - table:
              source_schema: 'default'
              source_table: 'boards'
              destination_catalog: 'main'
              destination_schema: 'monday_com_data'
              destination_table: 'boards'
              connector_options:
                api_source_connector_options:
                  options:
                    board_ids: '["<board-id-1>","<board-id-2>"]'
          - table:
              source_schema: 'default'
              source_table: 'activity_logs'
              destination_catalog: 'main'
              destination_schema: 'monday_com_data'
              destination_table: 'activity_logs'
              connector_options:
                api_source_connector_options:
                  options:
                    board_ids: '["<board-id-1>","<board-id-2>"]'

Databricks notebook

The following pipeline specification ingests only the specified Monday.com boards:

Python

pipeline_name = "monday_com_pipeline"
connection_name = "<monday-com-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "boards",
          "destination_catalog": "main",
          "destination_schema": "monday_com_data",
          "destination_table": "boards",
          "connector_options": {
            "api_source_connector_options": {
              "options": {
                "board_ids": "[\"<board-id-1>\",\"<board-id-2>\"]"
              }
            }
          }
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "activity_logs",
          "destination_catalog": "main",
          "destination_schema": "monday_com_data",
          "destination_table": "activity_logs",
          "connector_options": {
            "api_source_connector_options": {
              "options": {
                "board_ids": "[\"<board-id-1>\",\"<board-id-2>\"]"
              }
            }
          }
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Set an activity log start date

Use the start_datetime connector config to set the earliest timestamp from which the activity_logs table ingests. It is pipeline-scoped, so set it in source_configurations rather than on an individual table. When omitted, the first sync ingests all available activity log history.

Declarative Automation Bundles

The following pipeline definition file ingests Monday.com activity logs from the specified start time:

YAML

resources:
  pipelines:
    monday_com_pipeline:
      name: monday_com_pipeline
      catalog: 'main'
      target: 'monday_com_data'
      ingestion_definition:
        connection_name: monday_com_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                start_datetime: '2026-01-01T00:00:00Z'
        objects:
          - table:
              source_schema: 'default'
              source_table: 'activity_logs'
              destination_catalog: 'main'
              destination_schema: 'monday_com_data'
              destination_table: 'activity_logs'

Databricks notebook

The following pipeline specification ingests Monday.com activity logs from the specified start time:

Python

pipeline_name = "monday_com_pipeline"
connection_name = "<monday-com-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "source_configurations": [
      {
        "api_source_connector_config": {
          "configs": {
            "start_datetime": "2026-01-01T00:00:00Z"
          }
        }
      }
    ],
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "activity_logs",
          "destination_catalog": "main",
          "destination_schema": "monday_com_data",
          "destination_table": "activity_logs"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest the entire schema

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

Declarative Automation Bundles

The following pipeline definition file ingests all supported Monday.com tables into a destination schema:

YAML

resources:
  pipelines:
    monday_com_pipeline:
      name: monday_com_pipeline
      catalog: 'main'
      target: 'monday_com_data'
      ingestion_definition:
        connection_name: monday_com_connection
        objects:
          - schema:
              source_schema: 'default'
              destination_catalog: 'main'
              destination_schema: 'monday_com_data'

Databricks notebook

The following pipeline specification ingests all supported Monday.com tables into a destination schema:

Python

pipeline_name = "monday_com_pipeline"
connection_name = "<monday-com-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "schema": {
          "source_schema": "default",
          "destination_catalog": "main",
          "destination_schema": "monday_com_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

YAML

resources:
  jobs:
    monday_com_job:
      name: monday_com_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: monday_com_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.monday_com_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

• Monday.com connector FAQ
• Monday.com connector limitations
• Monday.com connector reference
• Troubleshoot the Monday.com connector