Best Practices for Managing Databricks Costs & Performance

 Introduction 

Databricks is a powerful platform used by many companies to work with big data and build machine learning models. It offers tools for storing, cleaning, analyzing, and using data—all in one place. 

But as helpful as Databricks is, it can become expensive or slow if not used the right way. 

That’s why it’s important to understand how to manage both costs and performance. Doing this well means your jobs run faster, your bills stay lower, and your team works more efficiently. 

In this blog, we’ll walk through simple best practices that can help you get the most out of Databricks without wasting money or time. 

Picture 

Agenda 

  • Picking the right cluster size for your work 
  • Using auto-scaling and auto-termination 
  • Organizing jobs to avoid waste 
  • Monitoring your usage with cost dashboards 
  • Keeping your data clean and compact 
  • Real-world tip from a retail company 
  • Conclusion 

1. Picking the Right Cluster Size for Your Work 

Databricks runs jobs using clusters. A cluster is a group of computers that works together to do a task. Bigger clusters cost more, but sometimes they’re not even needed for small jobs. 

Best practice: 

  • Don’t always choose large or powerful clusters. 
  • Match the size of your cluster to the size of your job. 
  • For small data tasks, smaller clusters will do just fine. 

This simple change can save a lot of money over time. 

2. Using Auto-Scaling and Auto-Termination 

Clusters cost money even when no one is using them. So if you forget to shut them down, your budget can take a hit. 

Databricks has two built-in features to help: 

  • Auto-scaling: Adds more machines if the job grows, and removes them when it shrinks. 
  • Auto-termination: Automatically shuts down a cluster after it’s been idle for some time. 

Tip: Set auto-termination time to 10 or 15 minutes for test clusters. That way, they won’t keep running after you leave for lunch! 

3. Organizing Jobs to Avoid Waste 

When multiple people are working on different tasks, it's easy for jobs to overlap, clash, or repeat work. This wastes both time and money. 

Best practice: 

  • Use Databricks Jobs to schedule work efficiently. 
  • Avoid running the same job too often. 
  • Group related tasks together in a single job where possible. 

This helps reduce confusion and avoid unnecessary compute use. 

4. Monitoring Your Usage with Cost Dashboards 

Databricks provides cost and usage dashboards that show where your money is going. 

You can see: 

  • Which jobs or clusters use the most resources 
  • How long each job takes 
  • Which users or teams are using more than expected 

Best practice: 

  • Review dashboards weekly. 
  • Talk with your team if something looks unusual. 
  • Set alerts if costs go over a certain amount. 

This helps you take control before costs grow too much. 

5. Keeping Your Data Clean and Compact 

Working with messy or too-large data slows everything down. It also takes up more space and processing time, which increases costs. 

Best practice: 

  • Remove unused columns from your data 
  • Store data in Delta format, which is fast and optimized for big data 
  • Clean and filter data before running large queries 

The cleaner and smaller your data, the faster your jobs will run and the less you’ll pay. 

6. Real-World Tip from a Retail Company 

A mid-size e-commerce company used Databricks to track customer behavior. They were running hundreds of jobs a day and noticed their monthly cost rising fast. 

After a quick audit, they found: 

  • Many clusters were staying active all night 
  • Teams weren’t sharing jobs they were duplicating them 
  • Some data tables had unnecessary columns 

After they: 

  • Enabled auto-termination 
  • Merged some jobs 
  • Switched to Delta format 

They reduced costs by 30% in just two weeks—without affecting performance. 

Conclusion 

Databricks can do amazing things with data, but it’s important to manage your costs and performance wisely. 

Here’s a quick recap of the best practices: 

  • Choose cluster sizes based on the job—not too big 
  • Turn on auto-scaling and auto-termination 
  • Schedule and organize jobs properly 
  • Watch your usage with dashboards 
  • Keep your data clean and efficient 

Small changes like these can lead to big savings and faster results. Whether you’re working alone or in a team, these habits will help you get the most value from Databricks—without surprise costs. 

What’s Next? Control Costs, Boost Performance with Databricks 

Want to get more out of your Databricks investment—without breaking the budget? Join our live workshops at AccentFuture and learn hands-on strategies to optimize clusters, trim costs, and speed up your data workflows using real use cases. 

Explore best practices for: 

  • Right-sizing clusters for any job 
  • Enabling cost-saving features like auto-scaling & termination 
  • Cleaning data for faster queries and leaner storage 
  • Building cost dashboards to keep teams informed 

✅ Learn it. ✅ Optimize it. ✅ Save smarter. 
 Master efficient data operations on Databricks with AccentFuture. 

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