A data lakehouse can replace a traditional data warehouse for many organizations, but not all. If your workloads are primarily structured, latency-sensitive reporting for business intelligence teams, a warehouse may still be the right fit. But if you need to support mixed workloads, unstructured data, machine learning pipelines, or streaming analytics alongside SQL queries, a data lakehouse is often the more practical and flexible choice. The sections below work through the most common questions teams ask when evaluating this shift.
What does a data lakehouse actually do differently?
A data lakehouse combines the low-cost, flexible storage of a data lake with the structured query performance and ACID transaction support traditionally associated with a data warehouse. It does this by adding a metadata and governance layer, typically built on open table formats like Apache Iceberg, directly on top of object storage. The result is a single architecture that can serve SQL analysts, data scientists, and streaming pipelines without duplicating data across systems.
The practical difference shows up in how data is stored and accessed. A traditional warehouse ingests data into proprietary storage formats controlled by the vendor. A lakehouse keeps data in open formats on storage you control, which means multiple engines can read the same data without copying it. Trino, Apache Spark™, and other query engines can all operate against the same Apache Iceberg tables simultaneously, each optimized for different query patterns.
This also changes the economics. Because storage is decoupled from compute, you scale them independently. You stop paying for warehouse compute when you only need an overnight batch job, and you’re not stuck with a single vendor’s query engine.
What are the main limitations of a traditional data warehouse?
Traditional data warehouses struggle most with data variety, cost at scale, and vendor dependency. They’re designed around structured, relational data and SQL workloads. When organizations need to store semi-structured logs, raw event streams, or model training data alongside their reporting tables, warehouses either reject that data or require expensive ETL pipelines to transform it first.
Cost is the second pressure point. Proprietary warehouse solutions typically bundle storage and compute, meaning you pay for both even when only one is under load. As data volumes grow, this pricing model becomes hard to justify, particularly for cold or archival data that’s rarely queried.
The third limitation is lock-in. Proprietary storage formats tie your data to a specific vendor’s ecosystem. Migrating away requires re-exporting and re-ingesting data, which is time-consuming and risky. Many teams find this dependency becomes a real constraint when evaluating new tools or negotiating contracts.
Which workloads is a data lakehouse better suited for?
A data lakehouse works best for organizations running mixed workloads that span SQL analytics, machine learning, and streaming data on a shared dataset. It performs particularly well when data volumes are large, data types are varied, or when multiple teams with different tools need access to the same underlying data without creating siloed copies.
Specific workloads where the lakehouse architecture has a clear advantage include:
- Machine learning and feature engineering: Data scientists can access raw and processed data in the same storage layer using Apache Spark™ or Python-based frameworks, without waiting for a data engineering team to load it into a warehouse.
- Event streaming and real-time ingestion: Streaming platforms like Apache Kafka® can write directly to open table formats, making fresh data immediately queryable without a separate loading step.
- Multi-engine analytics: Teams using different query engines, for example Trino for interactive SQL and Spark for batch processing, can share the same data without duplication.
- Data archival at scale: Object storage is significantly cheaper than warehouse storage for large volumes of historical data that are queried infrequently.
Where a warehouse still has an edge is in highly optimized, low-latency BI dashboards with well-defined, stable schemas. If that’s primarily what you need, the added complexity of a lakehouse probably isn’t worth it.
What’s the difference between a data lakehouse and a data mesh?
A data lakehouse is an architectural pattern for storing and querying data. A data mesh is an organizational and ownership model for managing data across an enterprise. They address different problems and can coexist, with a lakehouse often serving as the technical foundation on which a data mesh is implemented.
The data mesh model, as defined by Zhamak Dehghani, shifts data ownership from a central platform team to individual domain teams. Each domain owns, publishes, and maintains its own data products. The central platform provides self-serve infrastructure so domain teams can do this without deep platform expertise.
A data lakehouse supports this well because it allows domain teams to write to their own storage partitions using open formats, while still making data discoverable and queryable across domains. The governance and cataloging layer of a lakehouse, particularly when combined with policies-as-code approaches, maps naturally onto the federated governance principle that data mesh requires.
The confusion between the two terms usually comes from vendors using them interchangeably for marketing purposes. They are not the same thing. A data platform solution can support both concepts simultaneously, but understanding which problem each solves helps you design your architecture more deliberately.
Can a data lakehouse meet enterprise compliance and security requirements?
Yes, but the implementation details matter significantly. The architecture itself doesn’t automatically provide compliance. You need to layer access control, audit logging, data lineage tracking, and encryption on top of the storage and query layer, and those capabilities vary depending on the tools and operators you choose.
Key compliance capabilities to evaluate in any lakehouse implementation include:
- Column-level and row-level access control: Ensuring that analysts can only query data they’re authorized to see, enforced at the query engine level rather than relying on application-layer filtering.
- Audit logging: A complete, tamper-evident record of who queried what data and when, which is often required under frameworks like the Digital Operational Resilience Act (DORA) or sector-specific regulations.
- Data lineage: The ability to trace where a value in a report came from, through every transformation step, back to the source system.
- Encryption at rest and in transit: A standard requirement for any regulated data, and it needs to be verified at the storage, network, and query engine layers.
Open-source lakehouse stacks built on Kubernetes can give compliance teams more transparency than proprietary systems, because the full software supply chain is auditable. That traceability is often harder to demonstrate with closed, managed services where the underlying components aren’t visible.
How do you migrate from a data warehouse to a data lakehouse?
Migrating from a traditional data warehouse to a data lakehouse is best approached incrementally rather than as a full cutover. Start by identifying workloads that are poor fits for your current warehouse, typically high-volume historical data, machine learning pipelines, or streaming ingestion, and migrate those first while keeping the warehouse running for existing BI workloads.
A practical migration sequence looks like this:
- Choose your open table format: Apache Iceberg is the most widely supported option across query engines and has strong community momentum. Establish this as your standard before writing any data.
- Set up your storage layer: Configure object storage, whether on-premises or in a cloud provider, with appropriate access policies and encryption.
- Deploy your query engine: Trino is a strong choice for interactive SQL queries against Iceberg tables. Apache Spark™ covers batch and ML workloads. You can run both against the same data.
- Export and convert existing data: Export warehouse tables to Parquet or ORC format and register them as Iceberg tables. Validate query results against the source warehouse before switching any workloads.
- Migrate pipelines incrementally: Move ETL and ELT pipelines one at a time, validating each before decommissioning the warehouse equivalent.
- Establish governance tooling: Set up a data catalog, access control policies, and lineage tracking before the warehouse is fully decommissioned.
Plan for the migration to take longer than expected. Data quality issues that were hidden by warehouse constraints tend to surface when you move to a more flexible storage model. That’s actually useful information, but it takes time to work through.
Should you replace your data warehouse or extend it?
For most organizations, the honest answer is: extend first, replace later if the evidence supports it. A full warehouse replacement carries significant risk to existing reporting and BI workflows. A more practical approach is to run a lakehouse in parallel for new workloads, prove out the stack, and migrate existing workloads incrementally as confidence grows.
Replace your warehouse outright when the cost of maintaining two systems exceeds the risk of migration, or when the warehouse is actively blocking workloads you need to support. Extend rather than replace when your core BI and reporting workflows are stable, well-tested, and heavily used by non-technical stakeholders who depend on consistent behavior.
The decision also depends on your team’s operational capacity. A lakehouse stack, particularly a self-managed one, requires expertise in Kubernetes, distributed query engines, and open table format management. If that expertise isn’t already present, the operational overhead of a full replacement can outweigh the architectural benefits in the short term. Building that capability alongside a parallel lakehouse deployment is a lower-risk path.
You can explore working demos of lakehouse-style architectures to get a concrete sense of what the operational reality looks like before committing to a migration plan.
How Stackable supports data lakehouse adoption
The Stackable Data Platform (SDP) is a modular, Kubernetes-native data platform that provides the building blocks for a production-grade data lakehouse without locking you into proprietary storage formats or a single cloud provider.
Concretely, the SDP gives you:
- Kubernetes-native operators for Apache Kafka®, Apache Spark™, Trino, and other open-source data tools, all managed through a consistent infrastructure-as-code approach
- Open table format support via Apache Iceberg, enabling multiple engines to share the same data without duplication
- Data sovereignty by design: run on your own infrastructure, in any cloud, or in a hybrid environment, with no dependency on a single vendor’s managed service
- A fully traceable software supply chain, which matters when you need to demonstrate compliance to auditors or regulators
- Modular composition: add or remove components as your architecture evolves, without rebuilding from scratch
If you’re evaluating whether a data lakehouse is the right direction for your organization, or working out how to get there from where you are today, talk to our team about your specific setup.