Ingest data from HubSpot

Beta

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

Learn how to create a managed HubSpot ingestion pipeline using Databricks Lakeflow Connect.

Requirements

To create an ingestion pipeline, you must 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.
- If you plan to create a new connection: You must have CREATE CONNECTION privileges on the metastore.
  
  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.
- If you plan 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 HubSpot, you must first complete the steps in Configure OAuth for HubSpot ingestion.

Create an ingestion pipeline

Each source table is ingested into a streaming table.

Databricks UI
Databricks Asset Bundles
Databricks notebook

In the sidebar of the Databricks workspace, click Data Ingestion.
On the Add data page, under Databricks connectors, click HubSpot.
On the Connection page of the ingestion wizard, select the connection that stores your HubSpot access credentials. If you have the CREATE CONNECTION privilege on the metastore, you can click Create connection to create a new connection with the authentication details in Configure OAuth for HubSpot ingestion.
Click Next.
On the Ingestion setup page, enter a unique 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, you can click Create schema in the drop-down menu to create a new 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, you can click Create schema in the drop-down menu to create a new 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 Databricks Asset Bundles to manage HubSpot 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 Databricks Asset Bundles?.

Create a new bundle using the Databricks CLI:
Bash
```
databricks bundle init
```
Add two new resource files to the bundle:
- A pipeline definition file (for example, resources/hubspot_pipeline.yml). See pipeline.ingestion_definition and Examples.
- A job definition file that controls the frequency of data ingestion (for example, resources/hubspot_job.yml).
Deploy the pipeline using the Databricks CLI:
Bash
```
databricks bundle deploy
```

Examples

Use these examples to configure your pipeline.

Ingest a single source table

Databricks Asset Bundles
Databricks notebook

The following pipeline definition file ingests a single source table:

YAML
variables:
  dest_catalog:
    default: main
  dest_schema:
    default: ingest_destination_schema

# The main pipeline for hubspot_dab
resources:
  pipelines:
    pipeline_hubspot:
      name: hubspot_pipeline
      catalog: ${var.dest_catalog}
      schema: ${var.dest_schema}
      ingestion_definition:
        connection_name: <hubspot-connection>
        objects:
          # An array of objects to ingest from HubSpot. This example ingests the contacts table.
          - table:
              source_schema: default
              source_table: contacts
              destination_catalog: ${var.dest_catalog}
              destination_schema: ${var.dest_schema}

The following is an example pipeline specification that ingests a single source table:

Python
pipeline_name = "hubspot_pipeline"
connection_name = "<hubspot-connection>"

pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "contacts",
          "destination_catalog": "main",
          "destination_schema": "ingest_destination_schema"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest multiple source tables

Databricks Asset Bundles
Databricks notebook

The following pipeline definition file ingests multiple source tables:

YAML
variables:
  dest_catalog:
    default: main
  dest_schema:
    default: ingest_destination_schema

# The main pipeline for hubspot_dab
resources:
  pipelines:
    pipeline_hubspot:
      name: hubspot_pipeline
      catalog: ${var.dest_catalog}
      schema: ${var.dest_schema}
      ingestion_definition:
        connection_name: <hubspot-connection>
        objects:
          # An array of objects to ingest from HubSpot. This example ingests the contacts and companies tables.
          - table:
              source_schema: default
              source_table: contacts
              destination_catalog: ${var.dest_catalog}
              destination_schema: ${var.dest_schema}
          - table:
              source_schema: default
              source_table: companies
              destination_catalog: ${var.dest_catalog}
              destination_schema: ${var.dest_schema}

The following is an example pipeline specification that ingests multiple source tables:

Python
pipeline_name = "hubspot_pipeline"
connection_name = "<hubspot-connection>"

pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "contacts",
          "destination_catalog": "main",
          "destination_schema": "ingest_destination_schema"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "companies",
          "destination_catalog": "main",
          "destination_schema": "ingest_destination_schema"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Bundle job definition file

The following is an example job definition file to use with Databricks Asset Bundles. The job runs every day, exactly one day from the last run.

YAML
resources:
  jobs:
    hubspot_dab_job:
      name: hubspot_dab_job

      trigger:
        periodic:
          interval: 1
          unit: DAYS

      email_notifications:
        on_failure:
          - <email-address>

      tasks:
        - task_key: refresh_pipeline
          pipeline_task:
            pipeline_id: ${resources.pipelines.pipeline_hubspot.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​

Create an ingestion pipeline​

Examples​

Ingest a single source table​

Ingest multiple source tables​

Bundle job definition file​

Common patterns​

Next steps​

Additional resources​