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Ingest data from Verkada

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 Verkada 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 Verkada, first configure authentication from Databricks and create a connection. See Configure authentication to Verkada and Create a Verkada connection.

Connector options​

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

Option

Scope

Required

Applies to

Description

start_datetime

Table

No

audit_log

Earliest audit event to ingest on the first sync, as an ISO 8601 UTC timestamp (for example, 2026-07-09T12:00:00Z). Defaults to 365 days before the first sync, and no earlier than 2025-08-12.

Option

Scope

Required

Applies to

Description

start_datetime

Table

No

audit_log

Earliest audit event to ingest on the first sync, as an ISO 8601 UTC timestamp (for example, 2026-07-09T12:00:00Z). Defaults to 365 days before the first sync, and no earlier than 2025-08-12.

Create an ingestion pipeline​

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

  1. In the sidebar of the Databricks workspace, click Data Ingestion.
  2. On the Add data page, under Databricks connectors, click Verkada.
  3. On the Connection page of the ingestion wizard, select the connection that stores your Verkada credentials. If you have the CREATE CONNECTION privilege on the metastore, click Plus icon. Create connection to create a connection with the credentials from Configure authentication to Verkada.
  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 Plus icon. 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 Plus icon. 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 Plus icon. Create schedule. Set the frequency to refresh the destination tables.
  13. (Optional) Click Plus icon. Add notification to set email notifications for pipeline operation success or failure, then click Save and run pipeline.

Examples​

The Verkada connector makes available 2 source tables in the default source schema. Ingest individual tables or the entire schema.

Ingest specific tables​

Use this option to ingest a specific subset of tables, or to customize destination naming per table.

The following pipeline definition file ingests individual Verkada tables:

YAML
resources:
  pipelines:
    verkada_pipeline:
      name: verkada_pipeline
      catalog: 'main'
      target: 'verkada_data'
      ingestion_definition:
        connection_name: verkada_connection
        objects:
          - table:
              source_schema: 'default'
              source_table: 'audit_log'
              destination_catalog: 'main'
              destination_schema: 'verkada_data'
              destination_table: 'audit_log'
              connector_options:
                api_source_connector_options:
                  options:
                    start_datetime: '<start-datetime>'
          - table:
              source_schema: 'default'
              source_table: 'users'
              destination_catalog: 'main'
              destination_schema: 'verkada_data'
              destination_table: 'users'

Ingest the entire schema​

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

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

YAML
resources:
  pipelines:
    verkada_pipeline:
      name: verkada_pipeline
      catalog: 'main'
      target: 'verkada_data'
      ingestion_definition:
        connection_name: verkada_connection
        objects:
          - schema:
              source_schema: 'default'
              destination_catalog: 'main'
              destination_schema: 'verkada_data'

Declarative Automation Bundles job definition file​

The following is an example job definition file for use with Declarative Automation Bundles. The job runs daily.

YAML
resources:
  jobs:
    verkada_job:
      name: verkada_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: verkada_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.verkada_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​

On this page