Don't Reinvent the Monolith: Mapping an Open Lakehouse Future
Using a Wardley Map to show why bundling catalog, compute, and storage into one platform traps innovation, and why an agnostic, open Lakehouse wins.
Originally published at https://www.linkedin.com/pulse/dont-reinvent-monolith-mapping-open-lakehouse-future-vakamo-com-gyaie/.
In the world of modern data and AI infrastructure, it’s easy to get seduced by monolithic “all-in-one” platforms. Everything in one place, one vendor, one contract. Sounds efficient—until it’s not.
At Vakamo, we believe that the fastest way to slow down innovation is to bundle everything—from catalog to compute to storage—into one tightly coupled system. That’s why we’re advocating for agnostic architecture.
Let us use Wardley Map to explain why. Wardley Map is a strategic tool that help us understand the evolution and maturity of different components within a system, revealing where value truly lies and where strategic choices need to be made.
Mapping the Lakehouse Landscape
🧪 Genesis → AI/ML Systems
This is where raw invention lives. AI-native workflows, vector stores, retrieval-augmented generation, fine-tuned domain models—these are unpredictable, rapidly evolving, and full of potential. Companies ignoring this space miss out on what will likely define the next decade. But equally, locking AI into legacy platforms is a mistake. These systems demand flexibility, experimentation, and fast iteration—not rigid integration points and vendor delays.
🛠️ Custom Build → Metadata Control Plane
Here lives the strategic differentiation. Every company has unique needs around access control, lineage, and regulatory compliance. But a truly effective Metadata Control Plane goes beyond mere inventory; it transforms metadata into an actionable and contextful layer. This means metadata isn’t just descriptive; it’s the intelligence that drives automated policies, informs data quality initiatives, guides data discovery, and fuels decision-making. It provides crucial context: where data originated, its quality, its purpose, who owns it, and how it’s being used.
A generic checkbox feature bundled into another tool cannot deliver this. Custom build doesn’t mean starting from scratch—it means using open-source tools (like Lakekeeper) as a foundation for your unique needs. Open-source is crucial here because it offers the flexibility, extensibility, and community-driven innovation necessary to meet your bespoke requirements without vendor lock-in. This is where data product thinking and domain ownership emerge—and where you win long-term by making your data truly intelligent and self-governing.
⚙️ Product → Compute Engines
This is the danger zone—not because it’s unstable, but because it’s saturated with noise.
Everyone is trying to differentiate on compute: the fastest queries, the cheapest execution, the smartest optimizer. But from the customer’s perspective, it’s always the same:
READ SOME DATA & WRITE SOME DATA!
The promise of every vendor? “We’re faster. We’re cheaper.” The result? Marginal gains that rarely justify the complexity or the lock-in.
Most of the platforms in this space are building the similar engine with slightly different tuning knobs. While specific, highly specialized scenarios might warrant unique compute optimizations, for 95% of use cases, the relentless focus on incremental compute gains is a zero-sum game dressed up as innovation. Investors are pouring huge amounts of capital into an area that’s already been solved. We’re squeezing a dry lemon, hoping for another drop.
Worse, compute is often bundled tightly with catalog and governance. So you don’t just pick a query engine—you pick an entire platform. Now your innovation is chained to a release cycle, and every improvement means waiting, migrating, or negotiating.
This isn’t forward motion—it’s architectural vendor capture, and it’s slowing the entire industry down. Want real innovation? It’s not in benchmarks. It’s in letting your teams compose the right tools for the job—without being forced into someone else’s box.
🪵 Commodity → Storage
Storage seems boring. But it’s the most strategic commodity in the stack. Object stores like S3 or GCS are universal, mature, and stable—but we’re now seeing a renaissance here.
Tiered storage, data versioning, zero-ETL pipelines, and even open table formats like Apache Iceberg are modernizing storage. We’re watching a feedback loop where the commodity layer re-enters the Genesis phase by enabling new architectures.
Commodity doesn’t mean “done.” It means solid enough to build on—again.
The Problem With All-in-One
Having mapped these components, let’s now consider what happens when they are rigidly bundled together. When these parts are bundled tightly—catalog tied to storage tied to compute—it locks innovation to the pace of the slowest component. Want to plug in a new AI-native compute engine? Tough luck. Your platform doesn’t support it yet. Need to manage cross-cloud governance with open standards? Not possible—your catalog is vendor-specific.
It’s not just a technical issue—it’s a strategic trap. A single-vendor strategy might give short-term convenience, but it kills adaptability when it matters most.
Why Agnostic Matters
An agnostic Lakehouse architecture lets you:
- Swap components as innovation happens (compute, governance, AI tools).
- Retain control over your most strategic asset: your data.
- Align to open standards and the AI-native future.
At Vakamo, we’re building tools like Lakekeeper to keep the Lakehouse open, interoperable, and adaptive—so that your organization can innovate where it matters most, without lock-in. We believe in building with the community, ensuring our tools evolve to truly serve open architectures.
Let’s not reinvent the monolith. Let’s map the path forward.
Try It. Fork It. Shape the Future.
Want to help build a truly open Lakehouse architecture?
- ✅ Try Lakekeeper OSS
- ⭐️ Star us on GitHub
- 🤝 Contribute, fork, or file issues—we’re building this with the community.