Should You Replace Your Cloud Data Warehouse?

Cloud data warehouses (CDWs) like Snowflake, BigQuery, and Redshift have been central to modern analytics due to their ability to handle structured BI tasks efficiently. However, when data exceeds traditional clean table formats and shifts towards raw, semi-structured, or real-time applications, these warehouses can become costly and less effective. The rise of open lakehouse architectures, combining object storage with open table formats like Apache Iceberg, offers a flexible alternative by allowing diverse compute engines to operate on the same data. This architecture supports various workloads, including interactive SQL, batch processing, and machine learning, often at a lower cost due to its scalable object storage model. As organizations face rising costs, concurrency issues, and workload mismatches in their existing CDWs, many are considering a hybrid approach that retains CDWs for polished BI tasks while transitioning other workloads to a lakehouse setup. This strategic shift helps optimize data performance and cost-efficiency without an abrupt overhaul of existing systems.