Ingest data from Xero

Beta

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

Create a managed Lakeflow Connect pipeline that ingests Xero data into Unity Catalog tables. Use the Databricks UI, Declarative Automation Bundles, or a 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 Xero, first configure authentication from Databricks and create a connection. See Configure authentication to Xero and Create a Xero connection.

• Note the tenant ID of the Xero organization to ingest. See Find the tenant ID.

Connector options

For the options the Xero connector accepts, their scope, and the tables they apply to, see Connector options. Set pipeline-scoped options in source_configurations. Every pipeline must set tenant_id. 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 Databricks workspace, click Data Ingestion.
2. On the Add data page, under Databricks connectors, click Xero.
3. On the Connection page of the ingestion wizard, select the connection that stores your Xero 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 Xero.
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. For Tenant ID, enter the tenant ID of the Xero organization to ingest. Optionally, set a Start date and time. See Connector options.
8. Click Create pipeline and continue.
9. On the Source page, select the tables to ingest.
10. Click Save and continue.
11. 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.
12. Click Save and continue.
13. (Optional) On the Schedules and notifications page, click Create schedule. Set the frequency to refresh the destination tables.
14. (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 Xero 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). 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/xero_pipeline.yml). See pipeline.ingestion_definition and Examples.
   • A job definition file that controls the frequency of data ingestion (for example, resources/xero_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 the first three cells as they are.

3. Modify the fourth cell, which defines pipeline_spec, 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 Xero connector makes available 23 source tables in the default source schema. Specify each table that you want to ingest, and set tenant_id to the organization to ingest.

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 Xero tables:

YAML

resources:
  pipelines:
    xero_pipeline:
      name: xero_pipeline
      catalog: 'main'
      target: 'xero_data'
      ingestion_definition:
        connection_name: xero_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                tenant_id: '<tenant-id>'
        objects:
          - table:
              source_schema: 'default'
              source_table: 'invoices'
              destination_catalog: 'main'
              destination_schema: 'xero_data'
              destination_table: 'invoices'
          - table:
              source_schema: 'default'
              source_table: 'contacts'
              destination_catalog: 'main'
              destination_schema: 'xero_data'
              destination_table: 'contacts'
          - table:
              source_schema: 'default'
              source_table: 'payments'
              destination_catalog: 'main'
              destination_schema: 'xero_data'
              destination_table: 'payments'

Databricks notebook

The following pipeline specification ingests individual Xero tables:

Python

pipeline_name = "xero_pipeline"
connection_name = "<xero-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "source_configurations": [
      {
        "api_source_connector_config": {
          "configs": {
            "tenant_id": "<tenant-id>"
          }
        }
      }
    ],
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "invoices",
          "destination_catalog": "main",
          "destination_schema": "xero_data",
          "destination_table": "invoices"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "contacts",
          "destination_catalog": "main",
          "destination_schema": "xero_data",
          "destination_table": "contacts"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "payments",
          "destination_catalog": "main",
          "destination_schema": "xero_data",
          "destination_table": "payments"
        }
      }
    ]
  }
}
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 Xero tables from a specific start date:

YAML

resources:
  pipelines:
    xero_pipeline:
      name: xero_pipeline
      catalog: 'main'
      target: 'xero_data'
      ingestion_definition:
        connection_name: xero_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                tenant_id: '<tenant-id>'
                start_datetime: '<start-datetime>'
        objects:
          - table:
              source_schema: 'default'
              source_table: 'invoices'
              destination_catalog: 'main'
              destination_schema: 'xero_data'
              destination_table: 'invoices'
          - table:
              source_schema: 'default'
              source_table: 'contacts'
              destination_catalog: 'main'
              destination_schema: 'xero_data'
              destination_table: 'contacts'
          - table:
              source_schema: 'default'
              source_table: 'payments'
              destination_catalog: 'main'
              destination_schema: 'xero_data'
              destination_table: 'payments'

Databricks notebook

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

Python

pipeline_name = "xero_pipeline"
connection_name = "<xero-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "source_configurations": [
      {
        "api_source_connector_config": {
          "configs": {
            "tenant_id": "<tenant-id>",
            "start_datetime": "<start-datetime>"
          }
        }
      }
    ],
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "invoices",
          "destination_catalog": "main",
          "destination_schema": "xero_data",
          "destination_table": "invoices"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "contacts",
          "destination_catalog": "main",
          "destination_schema": "xero_data",
          "destination_table": "contacts"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "payments",
          "destination_catalog": "main",
          "destination_schema": "xero_data",
          "destination_table": "payments"
        }
      }
    ]
  }
}
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:
    xero_job:
      name: xero_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: xero_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.xero_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

• Xero connector FAQ
• Xero connector limitations
• Xero connector reference
• Troubleshoot the Xero connector