Ingest data from Atlassian

Beta

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

Create a managed Atlassian Audit Logs ingestion pipeline in Lakeflow Connect to ingest organization audit log events into Databricks.

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 Atlassian, first configure authentication from Databricks and create a connection. See Configure authentication to Atlassian and Create an Atlassian Audit Logs connection.

Connector options

The Atlassian Audit Logs connector option is pipeline-level. Set it in the source_configurations block of your pipeline definition. See Examples for usage.

Option	Required	Applies to	Description
`start_datetime`	No	All tables	The start of the initial backfill window, as an ISO 8601 timestamp (for example, `2026-01-01T00:00:00Z`). When omitted, the first sync starts 180 days before the pipeline runs.

Option	Required	Applies to	Description
`start_datetime`	No	All tables	The start of the initial backfill window, as an ISO 8601 timestamp (for example, `2026-01-01T00:00:00Z`). When omitted, the first sync starts 180 days before the pipeline runs.

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 Atlassian Audit Logs.
On the Connection page of the ingestion wizard, select the connection that stores your Atlassian 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 Atlassian.
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 Atlassian Audit Logs 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/atlassian_audit_logs_pipeline.yml). See pipeline.ingestion_definition and Examples.
- A job definition file that controls the frequency of data ingestion (for example, resources/atlassian_audit_logs_job.yml).
Deploy the pipeline using the Databricks CLI:
Bash
```
databricks bundle deploy
```

Examples

The Atlassian Audit Logs connector makes available 1 source table in the default source schema. Ingest the table or the entire schema.

Ingest specific tables

Use this option to customize destination naming for the table.

Declarative Automation Bundles
Databricks notebook

The following pipeline definition file ingests the Atlassian Audit Logs table:

YAML
resources:
  pipelines:
    atlassian_audit_logs_pipeline:
      name: atlassian_audit_logs_pipeline
      catalog: 'main'
      target: 'atlassian_audit_logs_data'
      ingestion_definition:
        connection_name: atlassian_audit_logs_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                start_datetime: '<start-datetime>'
        objects:
          - table:
              source_schema: 'default'
              source_table: 'audit_logs'
              destination_catalog: 'main'
              destination_schema: 'atlassian_audit_logs_data'
              destination_table: 'audit_logs'

The following pipeline specification ingests the Atlassian Audit Logs table:

Python
pipeline_name = "atlassian_audit_logs_pipeline"
connection_name = "<atlassian-audit-logs-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": "audit_logs",
          "destination_catalog": "main",
          "destination_schema": "atlassian_audit_logs_data",
          "destination_table": "audit_logs"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest the entire schema

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

Declarative Automation Bundles
Databricks notebook

The following pipeline definition file ingests all supported Atlassian Audit Logs tables into a destination schema:

YAML
resources:
  pipelines:
    atlassian_audit_logs_pipeline:
      name: atlassian_audit_logs_pipeline
      catalog: 'main'
      target: 'atlassian_audit_logs_data'
      ingestion_definition:
        connection_name: atlassian_audit_logs_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                start_datetime: '<start-datetime>'
        objects:
          - schema:
              source_schema: 'default'
              destination_catalog: 'main'
              destination_schema: 'atlassian_audit_logs_data'

The following pipeline specification ingests all supported Atlassian Audit Logs tables into a destination schema:

Python
pipeline_name = "atlassian_audit_logs_pipeline"
connection_name = "<atlassian-audit-logs-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "source_configurations": [
      {
        "api_source_connector_config": {
          "configs": {
            "start_datetime": "<start-datetime>"
          }
        }
      }
    ],
    "objects": [
      {
        "schema": {
          "source_schema": "default",
          "destination_catalog": "main",
          "destination_schema": "atlassian_audit_logs_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:
    atlassian_audit_logs_job:
      name: atlassian_audit_logs_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: atlassian_audit_logs_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.atlassian_audit_logs_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​