Ingest data from Amplitude

beta

This feature is in Beta. Workspace admins can control access to this feature from the Previews page by opting into the Lakeflow Connect for Amplitude preview. See Manage Databricks previews.

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

Connector options

Set table-scoped options on the individual object in the connector_options block. See Examples for usage.

Option	Scope	Required	Applies to	Description
start_datetime	Table	No	events	Earliest event timestamp to ingest on the first sync, in yyyy-MM-dd'T'HH:mm:ss'Z' format. Defaults to 1970-01-01T00:00:00Z.
request_time_range	Table	No	events	Size of each export window, as an ISO-8601 duration. Defaults to PT24H. The minimum is PT2H.

Option	Scope	Required	Applies to	Description
start_datetime	Table	No	events	Earliest event timestamp to ingest on the first sync, in yyyy-MM-dd'T'HH:mm:ss'Z' format. Defaults to 1970-01-01T00:00:00Z.
request_time_range	Table	No	events	Size of each export window, as an ISO-8601 duration. Defaults to PT24H. The minimum is PT2H.

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 Amplitude.
3. On the Connection page of the ingestion wizard, select the connection that stores your Amplitude 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 Amplitude.
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 Amplitude 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/amplitude_pipeline.yml). See pipeline.ingestion_definition and Examples.
   • A job definition file that controls the frequency of data ingestion (for example, resources/amplitude_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 Amplitude connector makes available four source tables in the default source schema. Ingest individual tables or the entire schema.

note

While the Amplitude connector is in Beta, pin the pipeline to the PREVIEW channel, as shown in the examples. Keep the channel field set to PREVIEW until the connector is generally available.

Ingest specific tables

Use this option to ingest a specific subset of tables, or to customize destination naming per table. The events table accepts the optional connector_options shown below; the other tables declare no per-table options.

Declarative Automation Bundles

The following pipeline definition file ingests individual Amplitude tables:

YAML

resources:
  pipelines:
    amplitude_pipeline:
      name: amplitude_pipeline
      catalog: 'main'
      target: 'amplitude_data'
      channel: PREVIEW
      ingestion_definition:
        connection_name: amplitude_connection
        objects:
          - table:
              source_schema: 'default'
              source_table: 'events'
              destination_catalog: 'main'
              destination_schema: 'amplitude_data'
              destination_table: 'events'
              connector_options:
                api_source_connector_options:
                  options:
                    start_datetime: '<start-datetime>'
                    request_time_range: '<request-time-range>'
          - table:
              source_schema: 'default'
              source_table: 'events_list'
              destination_catalog: 'main'
              destination_schema: 'amplitude_data'
              destination_table: 'events_list'
          - table:
              source_schema: 'default'
              source_table: 'cohorts'
              destination_catalog: 'main'
              destination_schema: 'amplitude_data'
              destination_table: 'cohorts'
          - table:
              source_schema: 'default'
              source_table: 'annotations'
              destination_catalog: 'main'
              destination_schema: 'amplitude_data'
              destination_table: 'annotations'

Databricks notebook

The following pipeline specification ingests individual Amplitude tables:

Python

pipeline_name = "amplitude_pipeline"
connection_name = "<amplitude-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "catalog": "main",
  "schema": "amplitude_data",
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "events",
          "destination_catalog": "main",
          "destination_schema": "amplitude_data",
          "destination_table": "events",
          "connector_options": {
            "api_source_connector_options": {
              "options": {
                "start_datetime": "<start-datetime>",
                "request_time_range": "<request-time-range>"
              }
            }
          }
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "events_list",
          "destination_catalog": "main",
          "destination_schema": "amplitude_data",
          "destination_table": "events_list"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "cohorts",
          "destination_catalog": "main",
          "destination_schema": "amplitude_data",
          "destination_table": "cohorts"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "annotations",
          "destination_catalog": "main",
          "destination_schema": "amplitude_data",
          "destination_table": "annotations"
        }
      }
    ]
  },
  "channel": "PREVIEW"
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest the entire schema

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

Declarative Automation Bundles

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

YAML

resources:
  pipelines:
    amplitude_pipeline:
      name: amplitude_pipeline
      catalog: 'main'
      target: 'amplitude_data'
      channel: PREVIEW
      ingestion_definition:
        connection_name: amplitude_connection
        objects:
          - schema:
              source_schema: 'default'
              destination_catalog: 'main'
              destination_schema: 'amplitude_data'

Databricks notebook

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

Python

pipeline_name = "amplitude_pipeline"
connection_name = "<amplitude-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "catalog": "main",
  "schema": "amplitude_data",
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "schema": {
          "source_schema": "default",
          "destination_catalog": "main",
          "destination_schema": "amplitude_data"
        }
      }
    ]
  },
  "channel": "PREVIEW"
}
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:
    amplitude_job:
      name: amplitude_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: amplitude_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.amplitude_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

• Amplitude connector FAQ
• Amplitude connector limitations
• Amplitude connector reference
• Troubleshoot the Amplitude connector