For a very long time, data warehouses were the best option available for reporting and data analysis. The data warehouse concept, a holdover from the 1980s, was first presented as an architectural model for data flows between operational systems and decision support systems.
Despite their continued popularity—especially in light of cloud services like Snowflake—data warehouses are coming under growing pressure from the data lakehouse, a new paradigm in data management. The main advantages of data lakes and data warehouses are combined in contemporary lakehouse architecture. And it's becoming increasingly common: 65% of organizations now conduct most of their analytics on lakehouses, with 42% having switched in the past year from cloud data warehouses to data lakes. This is according to a recent survey conducted in 2024.
Don't worry if you have experience with data warehouses and find the idea of the "lakehouse" foreign or confusing; we'll go into both ideas in detail below and see how they complement one another.
What is a Data Lakehouse?
A data lakehouse is a new kind of open data management architecture that combines the scalability, flexibility, and cost-effectiveness of data lakes with the principles of data warehouses.
- Data warehouse – A relational database that stores integrated data from one or more sources.
- Data Lake — A centralized repository that can store both structured and unstructured data at any scale, using distributed storage
- Data lakehouse – An open data management architecture that provides a structured transactional layer over low-cost cloud object storage, enabling fast reporting and analytics directly on the data lake.
