I thought it would be fun to ask Copilot what the differences were. Here was Copilots response. I also used Copilot to create an image of a data warehouse and data lakehouse and I am not a fan of what it created, but I’ll use it anyways.

A data warehouse and a data lakehouse serve different purposes and have distinct characteristics:

Data Warehouse

• Purpose: Designed to store and manage structured data from various sources, primarily for reporting and analysis.
• Data Type: Stores processed and cleaned data, typically structured and organized into tables.
• Schema: Uses a predefined schema (schema-on-write), meaning the data structure is defined before storing the data.
• Use Cases: Ideal for business intelligence, reporting, and querying historical data.
• Users: Business analysts and decision-makers who need to generate reports and insights from structured data.
• Performance: Optimized for fast query performance and data retrieval.

Data Lakehouse

• Purpose: Combines the features of data lakes and data warehouses, allowing for both structured and unstructured data storage and processing.
• Data Type: Can handle raw, semi-structured, and structured data, providing more flexibility.
• Schema: Uses a schema-on-read approach, meaning the data structure is defined when the data is read, not when it is stored.
• Use Cases: Suitable for advanced analytics, machine learning, and real-time data processing, in addition to traditional BI tasks.
• Users: Data scientists, data engineers, and business analysts who need to work with diverse data types and perform complex analyses.

In summary, while a data warehouse is optimized for structured data and traditional BI tasks, a data lakehouse offers greater flexibility by supporting a wider range of data types and advanced analytics capabilities.