In today’s data-hungry world, Databricks has become the go-to platform for organizations leveraging analytics, machine learning, and data engineering to drive business outcomes.

At the heart of this platform is the Databricks Runtime — a powerful software layer that bundles Apache Spark, programming languages, and essential libraries to power your compute clusters.

Keeping this runtime updated isn’t just some IT checkbox — it’s a business priority that keeps you secure, boosts performance, and helps you stay compliant with those pesky regulatory standards. 🛡️

This blog post breaks down why runtime maintenance matters, offers practical strategies for managing it effectively, and provides clear steps to update your Databricks environment — all written for business folks and data pros who want to protect their operations and get the most bang for their buck.

Why Runtime Maintenance Should Be On Your Radar 🔍

Letting your Databricks runtime get outdated is like driving with your check engine light on — eventually, something’s going to break! Here’s why you should care:

Security and Compliance 🔒: Outdated runtimes can harbor security vulnerabilities that hackers love to exploit, putting your data at risk and potentially landing you in hot water with regulations like GDPR or HIPAA. Regular updates keep your environment locked down and audit-ready.

Speed Boost ⚡: New runtime versions often come with performance tweaks that can slash processing times and cut your cloud bills. Runtime 13.x, for example, delivers up to 30% faster SQL operations compared to older versions — who doesn’t want that kind of upgrade?

Everything Plays Nice Together 🤝: As cloud services and tools evolve, outdated runtimes can start causing weird compatibility issues. Updates ensure your environment stays in sync with all your other tech. Up-to-date runtimes also unlock features like Unity Catalog, enabling centralized governance and secure data sharing across your organization.

Cool New Toys 🎁: Each runtime release brings shiny new features — improved ML libraries, better visualizations, or new language support — giving your team new capabilities to play with. 

Bottom line: runtime maintenance is like regular oil changes for your data engine — a little preventive care now saves major headaches later!

Know Your Databricks Runtime Flavors 🍦

Before diving into maintenance strategies, here’s a quick rundown of your options:

• Standard Runtime: The vanilla version with core Apache Spark and essential libraries
• Machine Learning Runtime: Loaded with ML frameworks for your data science team
• Long-Term Support (LTS) Versions: The stability champions with extended support periods for at least 3 years

NB: Databricks releases LTS versions every six months and supports them for three full years. See here for more info: Databricks support lifecycles | Databricks Documentation

Keeping Your Runtime Fresh: Practical Strategies 🛠️

Here’s how to keep your Databricks environment running like a well-oiled machine:

1. Stay In The Loop About New Releases 📰

Databricks regularly drops new runtime versions with security patches and cool features. Make checking the Databricks Runtime release notes a quarterly habit to stay on top of updates that matter for your workloads.

Pro Tip: Put someone in charge of tracking release schedules — trust me, you’ll thank yourself later!

2. Test-Drive Updates Before Going All In 🧪

Before rolling out a new runtime to your production environment, take it for a spin in a test cluster. This should include:

• Running your most important notebooks and jobs
• Making sure all your data connections still work
• Checking if performance is as good or better than before
• Testing any custom libraries or special code you’ve written

Think of it as taking a new car for a test drive before buying — you want to make sure it handles well on your specific roads!

Plan Your Upgrades Like A Pro 📅

For big upgrades, like jumping between Long Term Support (LTS) versions, follow a proper game plan:

• Read the migration docs so you know what might need tweaking
• Schedule updates during quiet periods (weekend deployments, anyone?)
• Give your team and stakeholders a heads-up about what’s changing
• Have a backup plan in case things go sideways

Treat major upgrades like mini-projects, not afterthoughts. Your future self will thank you when everything goes smoothly!

1. Keep An Eye On Performance 📊

Databricks’ built-in Cluster Metrics provide detailed insights into cluster performance, helping you identify bottlenecks and optimize resource usage.

NB: For Databricks Runtime versions 13 and above, Ganglia metrics are replaced with Databricks cluster metrics. For Databricks Runtime versions 12 and below, you can continue to use Ganglia metrics. If you have a workflow that depends on Ganglia metrics that cannot be satisfied with cluster metrics, contact your Databricks representative.

These native tools, integrated with the Spark UI and other observability features, offer a modern alternative to legacy solutions like Ganglia.

Slow jobs, resource bottlenecks, or weird errors might be telling you it’s time for an update.

Level Up: Hook into your chosen monitoring platforms, like Datadog or New Relic or Dynatrace, for an even better view of your entire data ecosystem.

2. Automate The Boring Stuff 🤖

If you’re juggling multiple clusters, automation via the Databricks API or Terraform or CLI tools are your best friends. It ensures consistency and saves your team from mind-numbing manual work. Consider:

• Writing scripts to create clusters with standardized runtime versions
• Automating tests when new runtimes drop
• Setting up policies to enforce consistent runtime usage
• Automation isn’t just cool — it saves time and prevents human error. Win-win!

In fact, I have created a proof-of-concept Python program here: sunny-sharma-datalytyx/databricks-maintenance

Just run databricks-maintenance generate-report — output report.html in a terminal and you will get this: (obviously with your own cluster names and not my example ones!)

I would love some help to enhance this further and so have made this fully open sourced!

The Databricks Maintenance Report — Does what it say’s on the tin!

The “Oops” Story: What Happens When You Procrastinate 😬

A financial services company learned this lesson the hard way. They kept putting off a runtime update for six months until they hit a known bug — one that had been fixed in newer versions — that crashed their ETL processes during month-end reporting. The result? Two days of downtime, frantic troubleshooting, and delayed financial reports to stakeholders. Talk about a nightmare scenario!

Updating Your Runtime: The Step-by-Step Guide 📝

Getting your runtime updated is actually pretty straightforward:

For Existing Clusters:

1. Go to Compute > All-Purpose Compute in your workspace
2. Find your cluster and hit Edit
3. Pick the runtime version you want from the dropdown
4. Restart the cluster to apply the changes

For New Clusters:

When creating new clusters, just select the latest appropriate runtime version for what you’re trying to do.

Heads Up: Clusters on soon-to-be-retired runtimes (like 11.3 LTS, which goes away in Oct 2025) will need to be migrated. Keep an eye on those support dates!

I am writing this blog on the 4th April 2025 so keep that in mind!

Bottom Line: Don’t Sleep On Runtime Maintenance 😴 → 🚀

Keeping your Databricks runtimes up to date is like flossing — not the most exciting task, but ignore it at your peril! By staying informed, testing thoroughly, planning carefully, monitoring consistently, and automating where possible, you’ll avoid nasty surprises while unlocking new capabilities.

Take a minute right now to check your runtime version either manually, through the Databricks CLI, or my own Python script. If it’s time for an update, think of it as giving your data platform a tune-up — a small investment that prevents costly problems while setting you up for future success. For more nitty-gritty details, dive into the Databricks documentation.

Remember: in the world of data platforms, an ounce of prevention beats a pound of emergency fixes any day of the week! 💪

If you would like some assistance on maintaining your Databricks platform, then please reach out to me here on Medium or on my Mphasis Datalytyx email address: sunny.sharma@datalytyx.com.

This blog is written by Sunny Sharma 

Disclaimer: Please note the opinions above are the author’s own and not necessarily my employer’s opinion. This blog article is intended to generate discussion and dialogue with the audience. If I have inadvertently hurt your feelings in anyway, then I’m sorry.

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