What is Data Lakehouse Architecture?
Data Lakehouse Architecture is the convergence of the two dominant data architectures of the last twenty years: the Data Warehouse and the Data Lake. It is an open storage and compute platform designed to provide the structured analytical performance, ACID transactions, and governance of a traditional Cloud Data Warehouse, while keeping the scalability, machine learning flexibility, and low storage costs of a Data Lake.
Historically, organizations were pushed into a complex, dual-system architecture. They dumped large volumes of raw, unstructured data (images, audio, logs) into a Data Lake (like Amazon S3) for data scientists to use. Concurrently, they built expensive ETL pipelines to copy a fraction of that data into a rigid Data Warehouse (like Snowflake) for business analysts to query. This dual-system model created data silos, duplicated storage costs, and often left the business dashboards out of sync with the data science models. The Data Lakehouse removes much of this physical divide.
The Decoupled Architecture
The fundamental principle of the Data Lakehouse is the decoupling of Storage from Compute, mediated by Open Table Formats.
1. The Storage Layer (Object Storage)
The foundation of the Lakehouse is inexpensive, elastically scalable cloud object storage (Amazon S3, Google Cloud Storage, Azure Data Lake Storage). Data is written here using open columnar file formats (primarily Apache Parquet). By using open formats rather than proprietary vendor formats, the organization keeps ownership of its physical data.
2. The Metadata Layer (Open Table Formats)
This is the key architectural innovation that makes a Lakehouse possible. In a raw data lake, Parquet files are just files in folders. The Lakehouse introduces a structured metadata layer using Apache Iceberg, Delta Lake, or Apache Hudi. This layer tracks exactly which files belong to which tables. It provides ACID transactions (Atomicity, Consistency, Isolation, Durability), Time Travel for recovery, and metadata-only Schema Evolution, turning the lake into a structured, database-like system.
3. The Compute Layer (Multi-Engine Execution)
Because the data and the metadata are open, the organization is not locked into a single query engine.
- Business analysts can use Dremio to execute fast BI dashboard queries.
- Data engineers can spin up Apache Spark clusters to run large batch ETL transformations.
- Data scientists can use Python to read the exact same data to train machine learning models. All three engines read the same physical Parquet files without creating data silos or copying data between systems.
Summary of Technical Value
The Data Lakehouse Architecture unifies the enterprise data stack. By applying the structural governance, ACID transactions, and query optimizations of a warehouse on top of the low-cost, scalable storage of a data lake, it reduces the need to duplicate data across the enterprise. It lets analysts, engineers, and scientists work at the same time on a single, governed source of truth.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.