data warehouses are both widely used to store data for analytics, but they are not interchangeable terms. A data lake tends to include large amounts of raw data, the purpose for which may not yet be defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose.
Data Lake is a storage repository that stores huge structured, semi-structured, and unstructured data, while Data Warehouse is a blending of technologies and components which allows the strategic use of data. Data Lake defines the schema after data is stored, whereas Data Warehouse defines the schema before data is stored.
Data in a data lake is stored in its native format, whereas data in a data warehouse is transformed into a uniform format. Data lakes are designed for data discovery and exploration as well as raw data storage, while data warehouses are optimized for data analysis and reporting.A data warehouse is often considered a step "above" a database, in that it's a larger store for data that could come from a variety of sources. Both databases and data warehouses usually contain data that's either structured or semi-structured. In contrast, a
data lake is a large store for data in its original, raw format.A data warehouse can only store data that has been processed and refined. Data lakes, on the other hand, store raw data that has not been processed for a purpose yet. Therefore,
data lakes require a much larger storage capacity than data warehouses; the data is flexible, quickly analyzed, and perfect for machine learning.
