I teach Databricks to all sorts of folks, coders, managers—everyone. What’s wild is how two companies with the same setup can have totally different experiences. Usually, it’s not the tech itself that’s the issue, but how people see it.
Databricks training really pays off if it clicks with people and changes how they think, not just what buttons they know. This goes for leaders who want it to work and see a return, and also for the regular users who have to use this stuff every day.
I see this all the time in my classes.
There’s usually a point, maybe halfway through a lab, where someone stops and asks, kind of carefully:
“Okay… but how does this work?”
That question is really important. It’s not just about the lab. It’s about whether the whole thing still feels like a mystery, or if they’re starting to get how it works and trust that it makes sense.
When the Platform Doesn’t Click
Databricks isn’t just another gadget you add to what you already have. It changes how you keep data, make pipelines, control who sees what, and how teams share work. If that change doesn’t stick, people go back to what they know. And that’s where things usually mess up.
For bosses, that means things take too long and don’t always work out.
For the people doing the work, it means they’re annoyed, find weird ways around things, and design stuff that won’t grow well - shadow IT.
That’s why training is worth it.
If companies have trouble using Databricks, it’s usually not because it can’t do what it’s supposed to. It’s usually because folks haven’t quite figured out how to use it the right way. If they don’t get that, teams do what they know:
- keep tight control
- use all sorts of random tools
- don’t say who’s in charge
- don’t worry about governing things until it’s a pain to add later
Good training fixes that early on.
What Good Training Actually Changes
It gives everyone – engineers, analysts, the machine learning practitioners, and the governance folks – a way to talk to each other.
It makes it faster to get that first real win, not just a demo, but something that actually works for real.
It helps people see what will grow well and what’s barely alive.
And it gives them confidence, which is super important for getting everyone on board.
From a boss’s view, the biggest mistake I see is thinking training is something you do after problems show up. The teams that do well see it as part of getting started.
From the worker’s side, the real point of training isn’t just seeing all the cool features. It’s changing how you think about things and getting fingers to the keyboard quickly.
The Lakehouse “Wait… What?” Moment
Most folks have been working with traditional data warehouses for years. Those systems are all about tables, managed super tightly, and feel like locked boxes. That experience is great, but it also sets up what people expect.
Then, early in training, the lakehouse idea comes up in a real way.
Data’s in cloud storage.
Just files.
Buckets.
Out in the open.
That’s when someone always asks:
“Wait… you mean we’re just working with files?”
It’s a great question. Once that clicks, a lot of other stuff isn’t so mysterious.
How tables relate to storage.
Why file types and logs are important.
How you can control things without locking everything down.
Why being open but governed is way different than being closed and controlled.
Training lets people ask those questions safely before they make choices that cost a lot to fix later.
When “Magic” Turns Into Engineering
One of the clearest examples I saw was from a DBA. He came from a traditional database background and didn’t know much about distributed systems. He pulled me aside during a break and asked, really wanting to know:
“How can Databricks crunch hundreds of terabytes so fast? What’s really happening behind the scenes?”
So we took a break from the product and talked about how systems like Spark work.
Splitting up data.
Parallelizing the work.
Moving data when you need to, not just when it’s easy.
The idea that speed comes from working together across a bunch of machines, not just one super-powered thing.
Then I made an analogy.
Imagine a huge pile of jelly beans you need to sort and count by color. One person could do it, eventually. Or you could give scoops to a group, have everyone count their handful, and then add up the totals.
You could see when it clicked.
What seemed like magic started to feel like regular engineering. His questions changed. Not so much “which setting do I mess with?” but more “how does automatic parallelism reshape the size and complexity of the problems I can solve?”
That moment sticks with me because that’s exactly what training should do. It gives you the know-how, not just makes you rely on the tool.
Seeing the Whole Platform Come Together
Another thing people often realize later is how fast they can build something real from beginning to end: getting data in, changing it, managing it, governing it, all in one place.
Many teams use a bunch of different tools for each of those steps. After a while, that feels normal.
Then they put together a pipeline faster than they thought, with the ability to see what’s happening and control it built-in, not just slapped on later. You can tell the mood in the room changes.
That might just get a nod during class. But its real impact shows up months later, when teams are trying to grow, work together, and stay compliant without messing everything up. That’s when thinking about the whole platform stops being just an idea and starts being useful.
Training is one of the few chances to get people thinking that way before they get stuck in their old ways with the infrastructure.
Setting Training Up for Success
From the leadership side, one change always makes things better: making sure everyone’s on the same page before they start training.
Too often, people are sent to training without really thinking about their background, what they do, and what the training is supposed to cover.
When that happens, it’s not great.
It feels too fast or too slow.
It’s too much to take in.
People lose confidence.
That’s not their fault. It’s just a mismatch.
When people have the right background and know what they’re supposed to get out of it, things go better. Discussions are more interesting. Labs go faster. People leave feeling like they can do stuff instead of feeling lost.
Getting the Most Out of Training
For regular users, a few things really help them get the most out of it:
- Come with a real project in mind, even if it’s small.
- Be honest about what you don’t know.
- Use one thing you learned right after class, before you forget the details.
- And tell your team what clicked for you. Training gets better when you share.
Why This All Matters
Basically, Databricks working well isn’t just about the tech stuff. It’s about changing how people work. You can have the best platform and still not see results if teams don’t have the right mindset and feel confident using it.
That’s why training is important.
Leaders get results faster and avoid costly mistakes.
Workers get clarity, intuition, and methods that can grow.
And sometimes the best thing is seeing “this feels like magic” turn into “this makes sense.”
Call to Action
If you’re leading a Databricks rollout—or living with one every day—pause and ask yourself a simple question: do your teams understand how the platform works, or are they just getting by?
If you want adoption that sticks, align on training early, invest in the right foundations, and give people space to truly understand the system they’re building on.
That mindset shift pays dividends long after the class ends.
I encourage you to share your thoughts. Do my words here resonate with you? Or, do I miss the mark? Please share.
Best Regards, Louis.
