Compare data lake vs cloud data warehouse architectures across storage, cost, governance, and ML performance — with a framework for choosing the right system for your workload.
by Databricks Staff
A data lake is a centralized repository that stores raw data in its native format — structured, semi-structured, and unstructured — using low-cost cloud object storage. Unlike a cloud data warehouse, which enforces a predefined schema before data can be loaded, a data lake applies structure only at read time, giving data scientists and data engineers maximum flexibility to work with diverse data types without upfront transformation. Both architectures live on cloud infrastructure, but they answer fundamentally different questions about how to collect data, process data, and retrieve data at scale.
This guide is written for data scientists, data engineers, and analytics leaders who need a practical decision framework — not a vendor pitch. By the end, you will understand the key differences between a data lake and a cloud data warehouse, when a data lakehouse closes the gap, and how to choose the right data storage architecture for your specific workloads.
Before diving into the mechanics, here is the practical guidance most teams need up front.
