The lakehouse idea springs from a common pain point: warehouses excel at handling structured data and delivering strong analytics performance, but they falter when faced with unstructured data and scalability challenges. On the other hand, lakes shine with unstructured data and flexibility but struggle with governance, consistency, and transactional integrity. Neither system is perfect—data warehouses rely on schema-on-write for better read performance, but this approach can hinder upstream agility and slow down the pace of business. Lakes, with schema-on-read, offer upstream flexibility but often suffer from performance bottlenecks and governance chaos.
The lakehouse aims to merge these systems, combining the structured rigor of warehouses with the adaptability of lakes. However, this fusion is like trying to build a flying submarine, you can make it work but it requires making intrinsic sacrifices to satisfy competing demands. These compromises reflects a broader tension in data management—balancing read performance and scalability against the need for upstream agility and consistent governance. This tradeoff underpins the lakehouse paradigm, but it also highlights the deeper, unresolved issues in the quest for a unified data platform.
Lakehouse require investments in data governance, tooling, and architecture. Distributed storage systems, like those underpinning lakes, struggle to maintain the consistency and ACID guarantees that warehouses provide by design. If you're not careful, this operational overhead can turn what initially seems like a cost-saving solution into a resource sink. Sure, tools like Delta Lake and Apache Iceberg improve data lake performance, but they still don’t reach the optimized query speeds of purpose-built warehouses for structured analytics.
Governance issues only compound the mess. The flexibility of data lakes is both their strength and an Achilles’ heel, often leading to chaotic metadata management, inconsistent data quality, and compliance headaches. Adding to this complexity is the fact that data rarely resides in one place or system. Even within the lakehouse paradigm, data is scattered across disparate environments, making governance a fragile patchwork of rules and enforcement mechanisms that can break under pressure. This disjointedness underscores a critical point: the promise of unified systems like the lakehouse remains hindered by the inherent fragmentation of data across modern ecosystems.
Then there’s the cost. Data lakes are often touted as more economical for storage, but lakehouse implementations reveal a different reality. Without meticulous tuning, compute costs for processing and querying can balloon. The system that was supposed to simplify and economize your architecture becomes an expensive juggling act.
Despite the hype, lakehouses fail to deliver on their promise of versatility. They aren’t well-suited for online transaction processing (OLTP), struggle with real-time streaming, and still can’t replace the speed and simplicity of dedicated warehouses for fast business intelligence (BI) reporting. Moreover, while they attempt to centralize data, they overlook the reality that data resides across a diverse range of systems—vector databases, key-value stores, document databases, graph databases, and search engines—each optimized for specific workloads. Even if data eventually makes it to the lakehouse, issues like latency, freshness, and correctness persist, underscoring the limitations of trying to force a one-size-fits-all solution on inherently diverse data ecosystems. This isn’t just a case of overselling—it’s a fundamental reality we’re ignoring: there is no one system that is optimal for everything.
The industry’s obsession with centralization compounds the problem. Instead of embracing the diversity of data use cases and systems, we keep trying to cram everything into one box, whether it’s a warehouse, a lake, or now a lakehouse.
The path forward isn’t about squeezing more into a single system—it’s about embracing the reality of distributed, multi-model data. Data isn’t static, and its uses are ever-evolving. Instead of building monoliths, we should focus on flow: how data moves through systems, adapts to needs, and serves diverse workloads.
This is where solutions like Matterbeam offer a fresh perspective. Rather than forcing data into one destination, Matterbeam sees data as inherently dynamic. Its approach—focusing on making data available at the moment of need in the right shape—frees data from the constraints of centralized systems, allowing it to move predictably and seamlessly to where it’s needed most.
The lakehouse is an improvement in that it can handle both lake and warehouse workloads moderately well, but this comes with significant tradeoffs. It succeeds only if we assume one-size-fits-all is the ultimate goal, with a system that excels in neither domain. This highlights our fixation on merging paradigms instead of acknowledging the strengths and limitations of each. It’s time to rethink the way we approach data architecture. The future isn’t about forcing lakes and warehouses together; it’s about liberating data from these constraints altogether. Lets use warehouses for what warehouses are good for, lakes for what lakes are good for, and embrace new systems that unlock entirely new capabilities. The only constant is change. Data flows. Let’s build systems that embrace this reality.
Thank you Mark Lyons for reading a draft of this post.
