Getting started with the Dapr Databricks Python SDK extension

Mon, 01 Jan 0001 00:00:00 +0000

Let’s run a fraud-remediation example that turns simulated Databricks Lakeflow records into durable Dapr Workflow executions. With the provided example, you will:

Run a Dapr Workflow (fraud_remediation) that freezes a card, notifies the customer, opens an investigation, waits for an analyst decision, and resolves the case
Feed it two simulated Lakeflow micro-batches through dapr.ext.databricks’s batch handler — no Databricks workspace or pyspark required for this part
See a retried micro-batch schedule zero duplicate workflow executions, because every instance ID is derived deterministically from the record’s own business key

Databricks Lakeflow identifies a suspicious transaction
 |
 dapr.ext.databricks sink
 |
 FraudRemediationWorkflow
 |
 freeze card -> notify customer -> open investigation
 |
 wait for analyst decision
 |
 resolve case

This example uses the default configuration from dapr init in self-hosted mode.

Dapr Python SDK integration with Databricks Lakeflow on Dapr Docs

Getting started with the Dapr Databricks Python SDK extension