Ingest data from Celigo

Beta

This feature is in Beta. Workspace admins can control access to this feature from the Previews page. See Manage Databricks previews.

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

Connector options

Set pipeline-scoped options in source_configurations and table-scoped options on the individual object. See Examples for usage.

Option	Scope	Required	Applies to	Description
`start_datetime`	Table	No	`audit_logs`	ISO-8601 UTC datetime for the first sync's backfill start. Defaults to 365 days before the first sync.

Create an ingestion pipeline

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

Databricks UI
Declarative Automation Bundles
Databricks notebook

In the sidebar of the Databricks workspace, click Data Ingestion.
On the Add data page, under Databricks connectors, click Celigo.
On the Connection page of the ingestion wizard, select the connection that stores your Celigo 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 Celigo.
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 CATALOG and CREATE SCHEMA privileges 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.
Click Save and continue.
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.
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, then click Save and run pipeline.

Use Declarative Automation Bundles to manage Celigo 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:
Bash
```
databricks bundle init
```
Add two new resource files to the bundle:
- A pipeline definition file (for example, resources/celigo_pipeline.yml). See pipeline.ingestion_definition and Examples.
- A job definition file that controls the frequency of data ingestion (for example, resources/celigo_job.yml).
Deploy the pipeline using the Databricks CLI:
Bash
```
databricks bundle deploy
```

Examples

The Celigo connector makes available the audit_logs source table in the default source schema. Ingest the table directly, or ingest the entire schema.

Ingest specific tables

Use this option to ingest a specific subset of tables, or to customize destination naming per table. Set the optional start_datetime backfill start on the audit_logs object.

Declarative Automation Bundles
Databricks notebook

The following pipeline definition file ingests individual Celigo tables:

YAML
resources:
  pipelines:
    celigo_pipeline:
      name: celigo_pipeline
      catalog: 'main'
      target: 'celigo_data'
      ingestion_definition:
        connection_name: celigo_connection
        objects:
          - table:
              source_schema: 'default'
              source_table: 'audit_logs'
              destination_catalog: 'main'
              destination_schema: 'celigo_data'
              destination_table: 'audit_logs'
              connector_options:
                api_source_connector_options:
                  options:
                    start_datetime: '<start-datetime>'

The following pipeline specification ingests individual Celigo tables:

Python
pipeline_name = "celigo_pipeline"
connection_name = "<celigo-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "audit_logs",
          "destination_catalog": "main",
          "destination_schema": "celigo_data",
          "destination_table": "audit_logs",
          "connector_options": {
            "api_source_connector_options": {
              "options": {
                "start_datetime": "<start-datetime>"
              }
            }
          }
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest the entire schema

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

Declarative Automation Bundles
Databricks notebook

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

YAML
resources:
  pipelines:
    celigo_pipeline:
      name: celigo_pipeline
      catalog: 'main'
      target: 'celigo_data'
      ingestion_definition:
        connection_name: celigo_connection
        objects:
          - schema:
              source_schema: 'default'
              destination_catalog: 'main'
              destination_schema: 'celigo_data'

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

Python
pipeline_name = "celigo_pipeline"
connection_name = "<celigo-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "schema": {
          "source_schema": "default",
          "destination_catalog": "main",
          "destination_schema": "celigo_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:
    celigo_job:
      name: celigo_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: celigo_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.celigo_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.

Requirements​

Connector options​

Create an ingestion pipeline​

Examples​

Ingest specific tables​

Ingest the entire schema​

Declarative Automation Bundles job definition file​

Common patterns​

Next steps​

Additional resources​