Data Platform Evolution

Initially, data warehouses served as first-generation platforms primarily focused on processing structured data. However, as the demand for analyzing large volumes of semi-structured and unstructured data grew, second-generation platforms shifted their attention toward leveraging data lakes. This resulted in two-tier architectures with problematic side effects: complexity of maintaining and synchronizing the two tiers, data duplication, increased risks of failure due to data movement between warehouses and data lakes, and so on.

Data lakehouses are third-generation platforms created to address the above limitations. Lakehouses are open, cost-efficient architectures combining key benefits of data lakes and data warehouses. They do their magic by implementing a metadata layer on top of data lakes. 

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