Configure Databricks job notifications, webhooks, and event handling.
Use when setting up Slack/Teams notifications, configuring alerts,
or integrating Databricks events with external systems.
Trigger with phrases like "databricks webhook", "databricks notifications",
"databricks alerts", "job failure notification", "databricks slack".
This v1 skill is being cut in the v2 rebuild — no direct replacement. No corresponding pain in the v2 catalog.
See the pack README → Migration: v1 → v2 for the full map and rationale.
Databricks Webhooks & Events
Overview
Configure notifications and event-driven workflows for Databricks jobs. Covers notification destinations (Slack, Teams, PagerDuty, email, generic webhooks), job lifecycle events, SQL alerts with automated triggers, and system table queries for event auditing.
Query system.access.audit for event monitoring without webhooks.
-- Recent job events (last 6 hours)
SELECT event_time, user_identity.email AS actor,
action_name, request_params.job_id, request_params.run_id,
response.status_code, response.error_message
FROM system.access.audit
WHERE service_name = 'jobs'
AND action_name IN ('runNow', 'submitRun', 'cancelRun', 'repairRun')
AND event_date >= current_date()
AND event_time > current_timestamp() - INTERVAL 6 HOURS
ORDER BY event_time DESC;
-- Permission changes (security audit)
SELECT event_time, user_identity.email, action_name, request_params
FROM system.access.audit
WHERE action_name IN ('changeJobPermissions', 'changeClusterPermissions',
'updatePermissions', 'grantPermission')
AND event_date >= current_date() - 7
ORDER BY event_time DESC;
Step 5: SQL Alerts with Automated Triggers
Create alerts that fire when query conditions are met.
-- Alert query: detect excessive failures
-- Create in SQL Editor > Alerts > New Alert
-- Trigger: failure_count > 3
-- Schedule: every 15 minutes
-- Destination: Slack notification destination
SELECT COUNT(*) AS failure_count,
COLLECT_LIST(DISTINCT job_name) AS failed_jobs
FROM (
SELECT j.name AS job_name
FROM system.lakeflow.job_run_timeline r
JOIN system.lakeflow.jobs j ON r.job_id = j.job_id
WHERE r.result_state = 'FAILED'
AND r.start_time > current_timestamp() - INTERVAL 1 HOUR
);
# Create alert programmatically
alert = w.alerts.create(
name="High Job Failure Rate",
query_id="<saved-query-id>",
options={"column": "failure_count", "op": ">", "value": "3"},
rearm=900, # Re-alert after 15 min if still triggered
)
