I teach Databricks to all sorts of folks, coders, managersโeveryone. Whatโs wild is how two companies with the same setup can have totally different experiences. Usually, itโs not the tech itself thatโs 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.
Thereโs usually a point, maybe halfway through a lab, where someone stops and asks, kind of carefully:
โOkayโฆ but how does this work?โ
That question is really important. Itโs not just about the lab. Itโs about whether the whole thing still feels like a mystery, or if theyโre starting to get how it works and trust that it makes sense.
When the Platform Doesnโt Click
Databricks isnโt 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 doesnโt stick, people go back to what they know. And thatโs where things usually mess up.
For bosses, that means things take too long and donโt always work out.
For the people doing the work, it means theyโre annoyed, find weird ways around things, and design stuff that wonโt grow well - shadow IT.
Thatโs why training is worth it.
If companies have trouble using Databricks, itโs usually not because it canโt do what itโs supposed to. Itโs usually because folks havenโt quite figured out how to use it the right way. If they donโt get that, teams do what they know:
- keep tight control
- use all sorts of random tools
- donโt say whoโs in charge
- donโt worry about governing things until itโs a pain to add later
Good training fixes that early on.
What Good Training Actually Changes
It gives everyone โ engineers, analysts, the machine learning practitioners, and the governance folks โ 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 whatโs barely alive.
And it gives them confidence, which is super important for getting everyone on board.
From a bossโs 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 workerโs side, the real point of training isnโt just seeing all the cool features. Itโs changing how you think about things and getting fingers to the keyboard quickly.
The Lakehouse โWaitโฆ What?โ 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.
Dataโs in cloud storage.
Just files.
Buckets.
Out in the open.
Thatโs when someone always asks:
โWaitโฆ you mean weโre just working with files?โ
Itโs a great question. Once that clicks, a lot of other stuff isnโt 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 โMagicโ Turns Into Engineering
One of the clearest examples I saw was from a DBA. He came from a traditional database background and didnโt know much about distributed systems. He pulled me aside during a break and asked, really wanting to know:
โHow can Databricks crunch hundreds of terabytes so fast? Whatโs really happening behind the scenes?โ
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 itโs 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 โwhich setting do I mess with?โ but more โhow does automatic parallelism reshape the size and complexity of the problems I can solve?โ
That moment sticks with me because thatโs 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 whatโs 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. Thatโs 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 everyoneโs 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, itโs not great.
It feels too fast or too slow.
Itโs too much to take in.
People lose confidence.
Thatโs not their fault. Itโs just a mismatch.
When people have the right background and know what theyโre 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 itโs small.
- Be honest about what you donโt 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 isnโt just about the tech stuff. Itโs about changing how people work. You can have the best platform and still not see results if teams donโt have the right mindset and feel confident using it.
Thatโs 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 โthis feels like magicโ turn into โthis makes sense.โ
Call to Action
If youโre leading a Databricks rolloutโor living with one every dayโpause 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 theyโre 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.
