Snowflake's key innovation is its multi-cluster, shared-data architecture that completely separates storage, compute, and cloud services. Data is stored centrally in a customer's cloud account (AWS, GCP, Azure), while stateless compute clusters, called "Virtual Warehouses," can be independently scaled up, down, or even suspended. This design provides exceptional concurrency, as different workloads (e.g., data loading, BI queries) can run on isolated compute resources without competing.
Databricks is built on the "data lakehouse" architecture, which unifies data lakes and data warehouses. It operates directly on data stored in open formats (like Delta Lake) within a customer's cloud storage. It uses the powerful Apache Spark engine for compute, allowing it to handle a wide range of workloads—from SQL analytics and BI to large-scale data engineering and machine learning—on a single, unified platform and a single copy of the data.