Managing costs in Azure Databricks can be a real challenge. Clusters often stay idle, autoscaling isn’t always tuned properly, and over-provisioned resources can quickly blow up your bill 💸. In this blog, I’ll walk you through how you can analyze, monitor, and optimize costs in your own Databricks environment using Power BI and AI-powered recommendations.

---

Why Focus on Cost Optimization?

Azure Databricks is powerful, but without the right monitoring, it’s easy to:

• Leave clusters running when not in use 🔄
• Oversize driver and worker nodes 🖥️
• Misconfigure autoscaling policies 📈
• Miss out on spot instances or cluster pools

That’s why cost optimization is a must-have practice for anyone running Databricks in production or development.

---

What You’ll Learn in This Tutorial

Here’s the simple 3-step process we’ll follow:

1️⃣ Collect Cluster Configuration Data

In my previous videos, I showed how to use Azure Function Apps to export cluster configuration details.

• Export via Visual Studio Code
• Export via Azure Portal

These configurations will form the raw dataset for analysis.

2️⃣ Analyze with Power BI 📊

We’ll load the exported data into Power BI and use a ready-made Power BI template (download link below) to visualize:

• Cluster usage
• Node sizes
• Autoscaling patterns
• Idle vs active time

This gives you a clear picture of where money is being spent.

3️⃣ AI-Powered Recommendations 🤖

Finally, we’ll feed the Power BI output into an AI agent. The AI will provide actionable recommendations such as:

• Resize underutilized clusters
• Enable auto-termination for idle clusters
• Use job clusters instead of all-purpose clusters
• Consider spot instances to lower costs

---

Download the Power BI Template

To make this even easier, I’ve created a Power BI template file (.pbit) that you can use right away. Just download it, connect it with your exported cluster configuration data, and start analyzing your environment.

Pro Tips for Cost Savings

💡 Enable auto-termination for idle clusters
💡 Use job clusters instead of always-on interactive clusters
💡 Configure autoscaling properly
💡 Try spot instances where workloads allow
💡 Regularly monitor usage with Power BI dashboards

---

Final Thoughts

With the combination of Power BI and AI, cost optimization in Azure Databricks becomes less of a guessing game and more of a data-driven process.

📺 If you prefer a video walkthrough, check out my detailed step-by-step YouTube tutorial here: Azure Databricks Series on YouTube

👉 Don’t forget to like, share, and subscribe to stay updated with more tutorials in this series!

Thank You,
Vivek Janakiraman

Disclaimer:
The views expressed on this blog are mine alone and do not reflect the views of my company or anyone else. All postings on this blog are provided “AS IS” with no warranties, and confers no rights.