How data and schema interact with a data lake and data warehouse

Data lakes and data warehouses serve as repositories for data storage but differ fundamentally in their architecture and functionality, particularly in how they utilize data catalogs. Data warehouses are characterized by their structured, predefined schemas that dictate how data is loaded and managed, offering speed and optimized query performance, but at the cost of flexibility. Conversely, data lakes are known for their flexibility, accepting data in any format and using catalogs to help users identify and manage data types, though traditionally, they lagged in query performance compared to data warehouses. However, advancements in data lake technology have enhanced their query capabilities, making them comparable to data warehouses while maintaining flexibility and cost efficiency. The complexity of managing data for insights and governance remains a challenge when using both systems, leading to the consideration of solutions like Starburst, which provides fast data lake query engines that optimize data access and reduce management costs, enhancing time-to-insight for business decisions.