Managed vs. DIY Data Lakehouse: What Are You Really Signing Up For?

So... you need a data lakehouse. Simple enough, right? Except now you have to decide whether to build it yourself or use a managed service. And that brings up a whole new set of questions.

• What will it cost to build and maintain it yourself?
• What does your company take responsibility for once it's running?
• What does building one yourself actually involve?
• What changes when someone else manages the platform?
• What happens to your data if you decide to leave?

I'll break this down into a few pieces. Covering what DIY actually means, what you're taking on with each approach, and what you're really paying for when you choose a managed lakehouse.

Building It Is Only the Beginning

Can you build your own data lakehouse?

Sure.

You can also build your own CRM, payroll system, or email server if you really want to. Given enough money, time, and people who know what they're doing, there aren't a whole lot of technology problems that a company absolutely can't solve itself.

But can we build it isn't really the question you should be asking. It’s what happens after you build the damned thing.

A data lakehouse isn't something you finish, put into production, hand the keys to someone, and then wander off congratulating yourself on a job well done. Now you own it. All of it. End to end. The infrastructure needs to be maintained. Software gets upgraded. Pipelines break. Security requirements change. Someone adds another data source. Someone else wants a capability nobody thought about when the thing was designed. Something that worked perfectly fine yesterday suddenly doesn't, and now somebody has the utter joy of figuring out why.

None of that means building your own platform is a bad idea. For some organizations, it makes perfect sense. If you already have the people, expertise, and a good business reason for controlling the entire stack yourself, building may be exactly the right choice.

But you're not just deciding whether to build something. You're deciding whether you want to be in the business of running it afterward.

That's where the difference between DIY and managed starts getting a lot more interesting. A managed data lakehouse and one you build yourself may ultimately accomplish many of the same things, but they're fundamentally different commitments for the business.

So before comparing products, features, or architecture diagrams, it's probably worth looking at what DIY actually means.

What Does “DIY” Actually Mean?

Calling it a DIY data lakehouse makes it sound a little easier than it actually is. You aren't downloading Data Lakehouse Community Edition, installing it on a server, clicking through a setup wizard, and calling it a day. You're building a platform out of a collection of technologies that all have to work together.

At the bottom, you need somewhere to store the data, usually cloud object storage. Then you need a table format, something like Apache Iceberg, that defines how all those files actually behave as tables. You need a catalog so everything knows what exists and where to find it. You need something like Trino or another query/compute engine to actually do something useful with the data. You need orchestration to get data where it needs to go and when it needs to get there. Then there's security, access control, governance, monitoring, observability, and all the other stuff that keeps the whole thing from becoming the world's most expensive collection of files.

There are plenty of good open-source technologies available to do all of those things. That's one of the reasons building your own lakehouse is a perfectly legitimate option in the first place.

The problem is that six great technologies don't magically become one great platform just because you put them in the same architecture diagram.

Someone has to decide which pieces to use, configure them, and make them talk to each other. Someone has to understand what happens when one gets upgraded and another one doesn't appreciate it. Security has to work across the stack. Monitoring has to tell you something useful when things break, and somebody has to know where to start looking when they do.

And now we see why open source and free mean two very different things.

Doing it properly can require a standing team of three to five people. Not five people who build the thing and then move on. But people you continue needing because the platform needs them to stay alive.

And we're not talking about inexpensive generalists. You need some combination of data architecture, platform engineering, pipeline development, security, governance, and people who understand what the business actually needs from its data.

May 2025 – current mileage may vary

Those aren't perfect matches for every person you'd need, but they give you a pretty good idea what kind of labor market you're shopping in.

Then there's the Gary problem.

Every complicated internal system eventually seems to have a Gary. Gary knows why that pipeline works differently from all the others. Gary remembers the workaround somebody added three years ago because upgrading the catalog broke something in Finance. Gary knows which configuration absolutely cannot be changed even though nobody bothered documenting why.

And eventually Gary gets another job, retires, changes careers, or has a terminal kinetic meeting with a bus. You've not only lost Gary, you’ve lost part of the knowledge base for the platform.

That's part of DIY too.

DIY vs. Managed: What Are You Actually Taking On?

Put the technology aside for a minute, and the difference between DIY and managed gets a lot easier to see.

With DIY, you own the platform. With managed, you're paying someone else to take on most of that responsibility.

That's really the tradeoff. Not free versus expensive, because DIY sure as hell isn't free. And not open source versus proprietary, because those are separate decisions. It's about which parts of running the platform you actually want your company to be responsible for.

One of the differences that's easy to overlook is time to trusted data. Getting a platform running isn't the same thing as getting use out of it. You can have storage working, pipelines moving data, queries running, and dashboards happily displaying numbers while three departments are still arguing about which number is actually right.

Redgate's 2026 State of the Database Landscape found that only 23% of organizations had a formal data governance and quality framework in place, while 47% identified data quality issues as a challenge in data transformation and engineering. Getting more data into one place doesn't automatically make the data better. Sometimes you're just moving problems around.

source: https://www.red-gate.com/solutions/state-of-database-landscape/2026/

There's also the question of what happens when something goes wrong. Maybe a pipeline failed. Maybe somebody changed a field upstream. Maybe permissions are wrong. Maybe two departments have different definitions of the same metric. With DIY, figuring that out is your problem, honestly it's probably several people's problem. Eventually, everyone ends up in the same meeting trying to figure out which part of the chain produced the wrong answer.

A managed service doesn't magically make bad source data good, and it can't decide what your company means by "active customer" or "revenue." The business still has to own its data and define what the numbers actually mean.

What changes is who owns the platform underneath it. That doesn't eliminate problems. It does eliminate a lot of arguments about who owns fixing the problem.

What Are You Actually Buying With a Managed Lakehouse?

So if you aren't building and running the platform yourself, what exactly are you paying for? The obvious answer is the technology, but that's only part of it. It’s three key things: the tool, the platform, and the expert. That's a useful way to look at it because you're going to need all three whether you build the lakehouse yourself or somebody else does it for you.

You need the tools to store, organize, query, move, secure, and govern the data. You need the platform and infrastructure that ties those tools together and keeps them working. And you need people who understand both the technology and the data well enough to make the whole thing useful.

DIY means assembling those three things yourself. A managed service means you're buying them together.

That's what Spitfyre is selling. We provide the platform, infrastructure, and expertise to operate it rather than handing you another collection of software and wishing you luck. We can work with an existing data lake or provide the environment for a company starting from scratch.

But managed doesn't automatically mean giving up control of your data. That's where what the platform is built on matters.

Spitfyre's platform is built around open technologies and formats, including Apache Iceberg and Parquet. We’re managing the platform, but the underlying data isn't being stuffed into a proprietary format that only Spitfyre knows how to read. If you decide someday that you don't want Spitfyre managing it anymore, your data is still in an open format designed to be used outside the platform.

And really, that's where Spitfyre removes one of the questions from the build-versus-managed decision. Who owns your data? You do. Whether you build the platform yourself or use Spitfyre, your data remains yours.

So the decision comes back to what we've been talking about all along. Do you want to build, maintain, and manage the entire platform yourself, or would you rather have someone else take care of that part?