It is best suited for data sources that can be extracted using a batch process and reports that deliver high value to the business.Īnother way to think about it is that data lakes are schema-less and more flexible to store relational data from business applications as well as non-relational logs from servers, and places like social media. As a follow-up to my blog Data Lakehouse & Synapse, I wanted to talk about the various definitions I am seeing about what a data lakehouse is, including a recent paper by Databricks. The requirements for reporting is known ahead of time during the planning and design of a data warehouse and the ETL process. It is highly attractive to data scientists and applications that leverage data for AI/ML where new ways of using the data are possible.Ī data warehouse is a centralized place for structured data to be analyzed for specific purposes related to business insights. All data is allowed, no matter if it is structured or unstructured and no processing is done to the data until after it is in the data lake. Reduced maintenance Unlike the time spent maintaining a data lake or transforming data for a data warehouse, data lakehouses require comparatively little. Think of a data lake as where streams of data from various sources meet.

Data lakes and data warehouses are used in organizations to aggregate multiple sources of data, but vary in its users and optimizations.

Data warehouse vs data lakehouse