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Databricks Monitoring Dashboard — Usage, Cost & Metrics

🎬 Story Time — “Where Did Our Cloud Budget Go?”​

Ankit, a cloud engineer, receives a surprise:

“Our monthly Databricks bill doubled last month.”

He has no visibility:

  • Which jobs consumed the most compute?
  • Which clusters were idle yet running?
  • Which teams overspent?

Ankit realizes he needs a Databricks Monitoring Dashboard.

🔥 1. Why Monitoring Dashboards Matter​

A monitoring dashboard helps:

  • Track cluster usage and idle time
  • Monitor job performance and failures
  • Understand cost allocation per team/project
  • Detect anomalous spikes in compute usage
  • Optimize pipelines and reduce waste

Without monitoring, teams risk overspending and inefficient pipelines.

🧱 2. Key Metrics to Track​

Cluster Metrics​

  • Active vs. idle time
  • Number of clusters per workspace
  • Cluster type distribution
  • Auto-termination compliance

Job Metrics​

  • Run durations
  • Success vs. failure rates
  • Task-level execution time
  • Triggered vs. scheduled jobs

Cost Metrics​

  • Compute costs per cluster
  • Cost per department/project
  • Cost trends over time
  • Idle cluster costs

Usage Metrics​

  • User activity
  • Notebook execution frequency
  • API usage statistics

⚙️ 3. Databricks Native Tools for Monitoring​

Databricks provides:

  • Account Console → Overall usage & cost
  • Admin Console → Cluster-level metrics
  • Jobs UI → Run history, success/failure rates
  • REST API → Programmatic access to metrics
  • SQL Analytics / Dashboards → Custom dashboards for cost & usage

These can be combined into a single observability view.

🔄 4. Example: SQL Dashboard for Cost Tracking​

Create a Databricks SQL query:

SELECT
    cluster_id,
    cluster_name,
    SUM(cpu_hours * price_per_hour) AS cost,
    SUM(run_time_minutes) AS runtime_minutes,
    SUM(idle_time_minutes) AS idle_minutes
FROM databricks_usage_logs
WHERE date >= current_date - 30
GROUP BY cluster_id, cluster_name
ORDER BY cost DESC;

Visualize:

  • Top 10 clusters by cost
  • Idle time percentage per cluster
  • Usage trends over 30 days

🛠️ 5. Job Performance Dashboard​

Track:

  • Success vs. failure trends
  • Average task execution time
  • Pipeline bottlenecks

Example SQL query:

SELECT
    job_name,
    COUNT(*) AS total_runs,
    SUM(CASE WHEN status='SUCCESS' THEN 1 ELSE 0 END) AS success_count,
    SUM(CASE WHEN status='FAILED' THEN 1 ELSE 0 END) AS failed_count,
    AVG(duration_minutes) AS avg_runtime
FROM databricks_job_runs
WHERE start_time >= current_date - 30
GROUP BY job_name
ORDER BY failed_count DESC;

Insight:

  • Quickly identify failing jobs
  • Determine jobs consuming excessive compute
  • Optimize resource allocation

🧪 6. Combining Metrics for Executive Dashboard​

Combine cluster, job, and cost metrics into one dashboard:

  • Cluster utilization chart
  • Job success/failure heatmap
  • Cost per team/project bar chart
  • Idle compute alerts

This gives executives and engineering leads full visibility into Databricks usage and spending.

📊 7. Alerts & Notifications​

Databricks Monitoring Dashboards can trigger:

  • Slack or email alerts for cost spikes
  • Job failure alerts
  • Idle cluster alerts
  • SLA breach notifications

Integrating dashboards with alerts enables proactive monitoring, not just reactive.

🧠 Best Practices​

  1. Monitor both usage and cost simultaneously
  2. Track idle vs. active cluster time
  3. Aggregate metrics per team/project for accountability
  4. Set threshold alerts for abnormal usage or cost
  5. Automate dashboard refresh daily or weekly
  6. Use tags in clusters/jobs to simplify cost attribution
  7. Combine SQL dashboards with API-driven automation for observability

🎉 Real-World Story — Ankit’s Savings​

After building the dashboard:

  • Identified idle clusters running overnight
  • Stopped unnecessary GPU clusters
  • Optimized long-running ETL jobs
  • Saved 28% on monthly cloud costs

Ankit presents the dashboard to management:

“Now we can see exactly where our money goes — and take action immediately.”

📘 Summary​

Databricks Monitoring Dashboards allow teams to:

  • ✔ Track cluster usage & idle time

  • ✔ Monitor job performance & failures

  • ✔ Allocate cost per project or team

  • ✔ Detect anomalies & optimize pipelines

  • ✔ Integrate alerts for proactive monitoring

A key tool for cost efficiency, reliability, and enterprise observability.

The next topic is Databricks Model Serving: LLM Inference Made Easy

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