A data lakehouse is easy to define and harder to build well, because the definition skips the decision that shapes a team’s tooling for years afterward: which open table format the lakehouse actually runs on. Most explanations of the architecture stop at the concept and leave that choice, along with the tradeoffs behind it, for later. This blog covers both: what a data lakehouse actually is, the layers that make it work, and how to think through the open table format decision on its own merits rather than defaulting to whichever one is easiest to reach for.
What Is a Data Lakehouse?
A data lakehouse is a data architecture that combines the low-cost, flexible storage of a data lake with the reliability, structure, and governance features of a data warehouse, on a single platform instead of two separate systems. It stores structured, semi-structured, and unstructured data together in open formats, while still supporting the ACID transactions, schema enforcement, and query performance that business intelligence has always required.
The practical effect is that a data science team pulling in raw sensor data and a finance team running a quarterly revenue report can work against the same underlying data, instead of two copies of it maintained in two different systems that inevitably drift out of sync.
The Three Layers of a Lakehouse Architecture
A lakehouse is built from three layers stacked on top of each other, and understanding what each one does makes the rest of this guide, and most vendor documentation, much easier to parse.
The storage layer is low-cost cloud object storage, the same kind a data lake uses, holding data in open file formats like Parquet. This layer is largely commoditized: AWS S3, Azure Data Lake Storage, and Google Cloud Storage all serve the same basic function, and switching between them is rarely the hard part of a lakehouse decision.
The table format layer sits on top of raw storage and creates most of the architecture’s real value. This metadata layer tracks which files belong to which table version and adds capabilities a data lake lacks on its own: ACID transactions, schema enforcement, schema evolution, and time travel to query historical versions of a dataset. Delta Lake, Apache Iceberg, and Apache Hudi are the three open table formats that operate at this layer, and which one a team picks shapes tooling choices for years afterward.
The semantic or serving layer is where business intelligence tools, data science notebooks, and increasingly AI agents actually query the data. This is also the layer Gartner has flagged as the next competitive front: in a March 2026 prediction, Gartner stated that by 2030, universal semantic layers will be treated as critical infrastructure alongside data platforms and cybersecurity, since without one, an AI agent has no reliable way to know what a “customer” or an “order” means consistently across systems.
Data Lakehouse vs. Data Lake vs. Data Warehouse
| Data warehouse | Data lake | Data lakehouse | |
|---|---|---|---|
| Data types | Structured only | Structured, semi-structured, unstructured | Structured, semi-structured, unstructured |
| Primary use | BI and reporting | Data science, machine learning | BI, data science, and AI on one platform |
| Schema approach | Schema-on-write | Schema-on-read | Schema enforcement with evolution support |
| Transaction support | Yes | No | Yes, via the table format layer |
| Typical cost profile | Higher, proprietary formats | Lower, but requires separate BI layer | Lower cost storage with warehouse-grade reliability |
A data warehouse has powered business intelligence for decades, but its proprietary formats and rigid schema-on-write approach make it expensive and poorly suited to the unstructured data that machine learning and AI workloads depend on. A data lake solved the cost and flexibility problem but created a new one: without transaction support or enforced schema, a data lake accumulates poor data quality and inconsistent access controls, generally described as the data swamp problem. A lakehouse is the attempt to keep the lake’s cost and flexibility while adding back the reliability a warehouse has always provided.
The Open Table Format Decision: Delta Lake vs. Iceberg vs. Hudi
This is the decision most first-time lakehouse explainers skip, largely because the company writing the explainer usually has a stake in the answer.
Delta Lake, originally built by Databricks and now open source, is the most mature of the three and the default choice inside the Databricks ecosystem. It has the deepest tooling integration for teams already standardized on Databricks or Azure Databricks.
Apache Iceberg, originally developed at Netflix, has become the format most associated with vendor-neutral, multi-engine access. It is the format Google Cloud, Snowflake, and AWS have all invested in supporting, which makes it a reasonable default for a team that expects to query the same data from more than one engine over time.
Apache Hudi, originally built at Uber, is optimized for high-frequency incremental updates and streaming ingestion, and remains the strongest fit for workloads with heavy upsert and delete volume, such as change-data-capture pipelines from operational databases.
All three now support some degree of interoperability; Delta Lake’s UniForm feature and Iceberg’s REST catalog both aim to reduce lock-in, but the real switching cost is rarely the file format itself. It is the catalog, governance tooling, and query engine integrations a team builds around that format over time. The honest answer for most enterprises is to pick based on the query engines and governance tools already in use rather than on which vendor’s marketing page is being read that week, and to treat interoperability claims as a hedge against future lock-in rather than a reason the choice does not matter today.
Why the Lakehouse Matters More Now
The lakehouse architecture predates the current wave of enterprise AI adoption by several years, but AI workloads are what have made the architecture close to unavoidable rather than merely convenient. An AI agent that needs to reason over customer data, operational data, and unstructured documents in the same query cannot do that cleanly across a warehouse and a separate lake connected by nightly batch jobs. It needs one governed platform with a consistent view of the data, which is exactly what a lakehouse is built to provide.
The market reflects that shift. The data lakehouse market reached $12.58 billion in 2026 and is projected to grow to $27.28 billion by 2030, a 21.4% compound annual growth rate, according to The Business Research Company’s 2026 market report. That growth is not just more data moving into a lakehouse. It reflects the platform doing double duty as both the analytics layer teams have used it for and the data foundation newer AI and agentic systems are being built on top of.
Common Data Lakehouse Mistakes
Treating table format selection as a technical footnote.
It determines catalog choice, query engine compatibility, and how painful a future migration will be. It deserves the same scrutiny as any other multi-year infrastructure commitment, not a default to whatever the primary cloud vendor recommends.
Migrating everything at once instead of by workload.
A lakehouse migration that tries to move every data source and every downstream consumer simultaneously tends to stall. Migrating the highest-value analytics or AI use case first builds the governance and pipeline patterns the rest of the organization can then reuse.
Skipping the semantic layer.
A lakehouse without a semantic layer still leaves every consuming application, dashboard, or AI agent to independently interpret what the underlying data means, which reintroduces the inconsistency the lakehouse was supposed to solve in the first place.
Underinvesting in governance until something breaks.
Centralized storage without centralized access control, lineage tracking, and audit trails creates a single large asset with weaker oversight than the fragmented systems it replaced, which is a worse security posture, not a better one.
FAQ
1) What is a data lakehouse in simple terms?
A data lakehouse is a data platform that stores all of an organization’s data, structured and unstructured, in low-cost storage while still providing the transaction support, schema enforcement, and query reliability that a traditional data warehouse offers, so teams do not need to maintain both systems separately.
2) Is a data lakehouse the same as a data lake?
No. A data lake stores raw data cheaply but lacks transaction support and schema enforcement. A data lakehouse adds a table format layer, such as Delta Lake, Iceberg, or Hudi, on top of that same low-cost storage to provide the reliability and structure a data lake does not have on its own.
3) Which open table format should we use, Delta Lake, Iceberg, or Hudi?
It depends on the query engines and governance tools already in use. Delta Lake fits teams standardized on Databricks. Iceberg fits teams that expect multiple engines to query the same data. Hudi fits workloads dominated by high-frequency updates and change-data-capture streams. All three now offer some interoperability, but the deeper cost is in the catalog and tooling built around the format, not the format itself.
4) Do we need a data lakehouse if we already have a data warehouse?
Not necessarily, if the workload is purely structured BI reporting with no machine learning or unstructured data requirements. A lakehouse becomes valuable once an organization needs to support AI, data science, or unstructured data alongside traditional BI without maintaining two separate systems.
5) How does a data lakehouse support AI and agentic AI workloads?
By providing a single governed source of data that AI agents and models can query without pulling from disconnected systems. Many lakehouse platforms have also added vector indexing support for retrieval-augmented generation, and a well-built semantic layer helps an AI agent interpret business terms and data relationships consistently rather than guessing at them per system.
How [x]cube LABS Can Help
[x]cube LABS builds AI-ready data foundations on top of lakehouse architecture, spanning real-time change data capture, OLTP and OLAP convergence, semantic layer design, and vector search integration, so a lakehouse serves both traditional analytics and the AI and agentic systems being built on top of it. The team works across the major open table formats and cloud data platforms, recommending a format and catalog strategy based on the query engines and governance model an organization actually runs, rather than defaulting to whichever platform’s lakehouse is easiest to sell.
That approach has underpinned a national diagnostics platform’s shift to a hybrid Azure and AWS data architecture, unifying real-time operational data with historical records to scale daily processing volume roughly one hundred times over while keeping governance and access control centralized rather than fragmented across systems. For more on how the data and AI practice approaches lakehouse design, governance, and AI-readiness, see the team’s full data capability.
Talk to someone who has actually built this.