Data Lakehouse Architecture in 2025 Why Control and Cost Matter More Than Ever
You’ve heard the sales pitch: “Managed, serverless, infinite scale. Pay only for what you use.”
It’s tempting — spin up a SaaS lakehouse, connect your dashboards, and let the magic happen.
But when you talk to teams who’ve been operating one at scale for a year or two, the tone changes.
It’s not about features they don’t like. It’s about things they didn’t realize they’d given away — until it was too late.
The aha moment: the control plane is the real lock-in
When most people compare Databricks, Snowflake, Dremio, Cloudera, or IOMETE, they focus on engines, pricing, and performance. But the bigger story — and the one few talk about — is where the control plane lives.
In a vendor-hosted SaaS lakehouse, the control plane — the system that holds your catalogs, governance rules, audit logs, and cluster definitions — lives in the vendor’s environment. That choice has long-term consequences:
- Cost leverage disappears: You can’t apply your reserved or spot instance policies, and you’re limited to whatever “optimization” features the vendor decides to expose.
- Governance is outsourced: Your access rules, masking logic, and audit trails live outside your security perimeter, making compliance slower and riskier.
- Incident response slows down: Operational telemetry is gated by their APIs and SLAs, adding latency to every high-severity investigation.
- Multi-cloud flexibility is gone: You can’t easily route workloads to the cheapest or most compliant location because the orchestration logic is hard-wired into the vendor’s environment.
In a self-hosted lakehouse, the control plane is yours. When the control plane is in your environment, the equation flips:
- You choose where logs live, who can read them, and how long to keep them.
- You decide how workloads are placed, scaled, and optimized.
- You maintain full visibility without relying on a vendor’s timeline.
In the end, it’s not just about where the software runs — it’s about who holds the levers. Vendor-hosted SaaS gives you convenience at the cost of control. Self-hosted gives you control at the cost of some operational responsibility. The right choice depends on whether you’re comfortable letting someone else decide when and how you can adjust costs, enforce policy, or change direction.
For large enterprises, that’s not a minor architectural choice — it’s the difference between saving 15% or more every month versus paying a permanent “vendor tax” on compute and compliance.
The format war is over (and that changes your strategy)
For years, choosing a lakehouse felt like choosing sides in a format battle: Delta vs. Iceberg vs. Hudi. That decision shaped everything — your tools, your compatibility, your long-term flexibility.
That’s no longer true. Today, the market has quietly converged:
- Databricks ships Iceberg GA in Unity Catalog alongside Delta.
- Snowflake supports both managed and externally managed Iceberg.
- IOMETE is Iceberg-native across all workloads (SQL, ETL, ML, streaming) on a single Spark engine.
Iceberg brings ACID transactions, schema evolution, and time travel — but the real shift is engine independence. Once your data sits in Iceberg, you can query it with any compatible engine without rewriting a single byte.
That’s a strategic advantage: your data layer is no longer a reason to stay with one vendor.
But that only solves half the lock-in problem. The bigger question is: who decides how and where your compute runs?
That’s where the control plane takes center stage.
The cost insight most teams miss
When teams talk about “cost optimization,” they often think about tuning queries or adding caches. Those tweaks help — but the biggest TCO difference comes from something far more fundamental: where your compute actually runs.
If the control plane lives in your vendor’s cloud account, it’s the vendor — not you — who decides how and where compute is allocated. That choice determines whether you can use reserved capacity, spot discounts, or cross-cloud arbitrage.
Here’s why that matters:
- Snowflake credits are fixed-rate. Even if AWS is offering you a deep reserved instance discount, you can’t attach it.
- Databricks supports running workers on spot instances, but the driver runs on on-demand instances managed by their control plane. That means you can’t fully integrate spot usage — or other cost governance policies — across the entire cluster.
- IOMETE runs entirely in your tenancy, so you decide: reserved, spot, mixed — and the savings go directly to your budget.
This matters because cost control is already a universal pain point. Flexera’s 2025 State of the Cloud report found that 84% of organizations rank managing cloud spend as their top challenge, and cloud waste still averages 32%. If you don’t own the levers that control how your compute runs, you’re starting that fight with one hand tied behind your back.
One engine vs. many: the hidden complexity tax
It’s easy to think that once the format war is over, you can mix and match engines freely for BI, ETL, and ML.
In practice, more engines often mean more complexity — not more flexibility.
- Security overhead: every engine needs its own governance and access rules.
- Tuning fragmentation: each engine has its own performance knobs and quirks.
- Operational drag: upgrades, patches, and incident responses now happen three times instead of once.
Over time, this “multi-engine tax” compounds — slowing teams down and making optimizations harder to share.
IOMETE’s approach: one engine for every workload. Apache Spark handles SQL, ETL, streaming, and ML, all on Iceberg tables.
One security model. One set of performance optimizations. One operational playbook.
Roadmap note: IOMETE will soon add Apache Arrow DataFusion Comet as an optional accelerator for Spark — replacing parts of Spark’s execution engine with native, vectorized operators, bypassing JVM garbage collection, and reducing CPU overhead. The result: faster queries, lower memory pressure, and better hardware utilization, without losing Spark’s API compatibility.
From architecture theory to vendor reality
Owning the control plane decides your real cost leverage, governance agility, and data sovereignty. The format war may be over with Iceberg ensuring table portability, but only control plane ownership frees compute and governance. And while multiple engines sound flexible, one engine means fewer moving parts, simpler tuning, and stronger security.
When you put these principles into a real-world comparison, the picture is clear: most vendors force trade-offs — SaaS platforms give up control, hybrid stacks add complexity — while IOMETE delivers full control, cost flexibility, and architectural simplicity in one package.
Feature Comparison Table
| Feature | Databricks (SaaS) | Snowflake (SaaS) | Dremio Cloud / Software | Cloudera CDP (Hybrid) | IOMETE (Self-Hosted) |
|---|---|---|---|---|---|
| Control Plane Location | Vendor-hosted | Vendor-hosted | Vendor or self-hosted | Customer or vendor-hosted | Fully self-hosted |
| Cost Leverage | Spot for workers only | None | Yes (self-hosted) | Yes | Full - spot, reserved, hybrid |
| Governance Control | Vendor-managed | Vendor-managed | Moderate (self-hosted) | Enterprise-grade | Full in-infra integration |
| Data Sovereignty | Vendor environment | Vendor environment | Better in self-hosted | Full control | Full control |
| Engine | Spark + Photon | Single proprietary engine | Arrow-based engine | Multi-engine (Hive/Impala/Spark) | Single-engine Apache Spark (all workloads) |
| Table Format | Delta + Iceberg (GA) | Native Table Format + Iceberg (GA) | Iceberg | Iceberg | Iceberg |
The Lakehouse Without the Trade-Offs
In regulated sectors like finance, healthcare, government, and telecom, control isn’t optional. The same is true if you’re running hybrid deployments or chasing every ounce of cost efficiency. IOMETE delivers both control and agility — something SaaS lakehouses and heavyweight hybrid stacks can’t do at the same time.
- You keep the control plane: governance, catalogs, metadata, audit logs, and cluster definitions stay in your environment. No vendor lock-in.
- You keep cost leverage: run on-prem where it’s cheaper, or in cloud with your own reserved/spot mix.
- You keep architectural simplicity: one engine (Apache Spark) for SQL, ETL, streaming, and ML, all on Iceberg. No sprawling multi-engine complexity.
- You keep your options open: Iceberg tables mean you’re never tied to one engine or one vendor.
It’s not about replacing Databricks, Snowflake, Cloudera, or Dremio everywhere — it’s about putting yourself in a position where you choose what runs where.
That’s what true architectural control means.