Obtain an OAuth token or API key per GHL documentation and store credentials securely in Databricks secret scopes for safe access during runs.
Use Databricks secrets and a secure credentials vault to pass tokens to notebooks without hard coding, and rotate credentials regularly.
GET /contacts/:contactId GET /contacts/:contactId/tasks GET /contacts/:contactId/tasks/:taskId GET /contacts/:contactId/notes GET /contacts/:contactId/notes/:id GET /contacts/:contactId/appointments GET /contacts/ GET /contacts/business/:businessId contacts.write POST /contacts/ PUT /contacts/:contactId DELETE /contacts/:contactId POST /contacts/:contactId/tasks PUT /contacts/:contactId/tasks/:taskId PUT /contacts/:contactId/tasks/:taskId/completed DELETE /contacts/:contactId/tasks/:taskId POST /contacts/:contactId/tags
Trigger a Databricks job when a contact is updated in GHL via webhook or scheduled checks.
Actions include fetch contact data (GET /contacts/:contactId), retrieve related tasks and notes, and write the result to Delta Lake.
Primary method uses GET /contacts/:contactId and related endpoints to assemble a full contact record.
Key fields include id, email, firstName, lastName, phone, tags, notes, tasks.
Schedule Databricks notebooks to pull a full contact list and business data with GET /contacts/ and GET /contacts/business/:businessId.
Transform and load into Delta Lake, then enrich with business data for reporting.
Use a batch style approach pulling GET /contacts/ and related endpoints for incremental or full loads.
Fields like id, businessId, createdAt, updatedAt are used to track changes.
Poll for changes or subscribe to events if available to pull only updated records.
Regularly fetch GET /contacts/:contactId/tasks and GET /contacts/:contactId/notes to capture updates and write to Delta Lake.
Use endpoints such as GET /contacts/:contactId, GET /contacts/:contactId/tasks, GET /contacts/:contactId/notes to detect changes.
Key fields include contactId, taskId, noteId, updatedAt.
Build dashboards and reports in Databricks using data exported from GHL for insights.
Automate data refresh and reporting with minimal coding by using prebuilt notebooks and Databricks workflows.
Scale data processing across teams with centralized access control and reusable pipelines.
Key elements include authentication, endpoints, data mapping, transformation, and secure storage; processes cover data extraction, normalization, and loading into Delta Lake.
An application programming interface used to access GHL resources like contacts, tasks, and notes.
A specific URL path for accessing a resource in the API (for example, /contacts/:contactId).
An authorization framework used to securely access GHL resources.
A storage layer that enables ACID transactions and scalable data lakes in Databricks.
Export contact attributes and scores to Databricks for scoring and visualization.
Analyze task completion times to improve service levels and reporting.
Aggregate notes for sentiment analysis to gauge engagement and follow up needs.
Create an OAuth client or API key in GHL and store securely in Databricks secrets.
Use the provided endpoints and the contacts.readonly scope to limit access.
Create notebooks that call the GET endpoints, transform data, and load into Delta Lake; schedule with Databricks jobs.
The GHL Contacts API is a REST API that lets you access contacts, tasks, notes, and related data. It uses standard HTTP methods and requires authentication to protect data. Databricks can read this data to populate dashboards and models. For writes, ensure your API token has the appropriate scope and always adhere to rate limits.
Available endpoints include retrieving a single contact and their related tasks notes and appointments and retrieving all contacts or those tied to a business. You can also create, update or delete records where your credentials permit. The list covers read operations for contacts as well as management of tasks notes and tags.
Authentication typically uses OAuth 2.0 tokens or API keys provided by GHL. You will exchange credentials for an access token and securely pass it to Databricks notebooks via secret scopes. Refresh tokens when needed and rotate credentials regularly.
Yes basic scripting helps but many Databricks workflows can be built with minimal code by leveraging notebooks and prebuilt connectors. You can start with read only data pulls and progressively add transforms.
The sync frequency depends on your needs. Real time hooks are ideal but often nightly batch runs are sufficient. Use delta updates to minimize data transfer and avoid duplications.
Required permissions come from the GHL API scope. For read only workflows use contacts.readonly. If you need to write data back you may need additional scopes and approval from GHL.
Writing data back to GHL is possible for supported endpoints if your token has write permissions. Carefully manage write operations and ensure data validations to avoid errors.
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Complete Operations Catalog - 126 Actions & Triggers
