The problem lakehouse architecture solves
For years, companies with advanced analytics needs have had to choose between two architectures with opposite trade-offs.
The traditional data warehouse offers excellent SQL query performance, robust governance, guaranteed data quality, and mature BI tools. But it’s expensive at scale, hard to adapt to unstructured data, and not designed for training machine learning models.
The data lake offers cheap storage for any kind of data (structured, semi-structured, unstructured), flexibility for ML workloads, and the ability to scale to massive volumes. But without careful management, it turns into a “data swamp”: data of unknown quality, with no schema and no governance, that’s hard to use productively.
Lakehouse architecture was created to solve exactly this dilemma, combining the best of both worlds in a single platform.
What a lakehouse is and how it works
A lakehouse stores data in a low-cost data lake (typically cloud object storage such as S3, Azure Data Lake Storage, or GCS) but adds a metadata and governance layer that delivers data warehouse capabilities: ACID transactions, schema enforcement, high-performance SQL queries, versioning, and data lineage.
The open table formats that make this possible are:
Delta Lake: developed by Databricks and now open source. It’s the standard format in Microsoft Fabric, Databricks, and Azure. It supports ACID transactions, time travel (querying data as it was at any point in the past), schema changes, and automatic file optimization.
Apache Iceberg: developed by Netflix and widely adopted in the AWS ecosystem (Athena, EMR, Glue). It offers features similar to Delta Lake, with a particular focus on interoperability between query engines.
Apache Hudi: developed by Uber, with a focus on incremental updates and Spark integration. Especially useful for CDC (change data capture) use cases.
Lakehouse vs. traditional data warehouse
| Dimension | Data warehouse | Lakehouse |
|---|---|---|
| Storage cost | High | Low (object storage) |
| Data types | Mainly structured | Structured, semi-structured, unstructured |
| ML and data science | Limited | Native |
| SQL performance | Very high | High (with optimization) |
| Governance | Mature | Maturing |
| Schema flexibility | Low | High |
For many companies, lakehouse architecture is the natural evolution of a data warehouse that can no longer handle today’s data variety and volume — without maintaining two separate platforms and their associated costs and complexity.
Lakehouse implementations on the market
The main vendors offering lakehouse architecture are:
- Databricks Lakehouse Platform: the reference implementation, with Delta Lake as its native format.
- Microsoft Fabric: OneLake with the Delta Parquet format and unified access from every service.
- Snowflake: has evolved from a pure data warehouse toward lakehouse capabilities, with support for unstructured data and ML.
- AWS Lake Formation + Apache Iceberg/Hudi: an open-source stack on AWS.
The right choice depends on your existing cloud ecosystem, your team’s skills, and the specific requirements of each use case.
When to adopt a lakehouse
Signs that it’s time to evaluate a lakehouse architecture: your data team needs to combine analytical SQL with Python/ML on the same data, your current data warehouse costs are a problem, you need to store and analyze unstructured data (images, text, logs), or you want to stop maintaining two separate platforms (a lake for ML and a warehouse for BI).
Frequently asked questions
Does a lakehouse fully replace the data warehouse? Often, but not always. For organizations with very heavy SQL workloads, a traditional data warehouse is still faster for complex queries on structured data. That said, the performance gap has narrowed significantly thanks to Delta Lake and Iceberg optimizations. The decision should be based on each company’s specific workload mix.
Which open table format is best: Delta Lake, Iceberg, or Hudi? It depends on your ecosystem. Delta Lake is the natural choice if you use Databricks, Azure, or Microsoft Fabric. Iceberg is preferable on AWS. Hudi has advantages in use cases with many incremental updates. The good news is that the three formats are converging in features, and interoperability between them keeps improving.
How do you migrate an existing data warehouse to a lakehouse without disrupting operations? The safest migration is incremental: keep the existing data warehouse running, build the lakehouse in parallel with new data flows, and gradually move workloads from the warehouse to the lakehouse by domain or team. This lets you validate each step before taking the next one and reduces operational risk.