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- The main differences between a data warehouse and a data lake are12:
- A data warehouse is a larger store for data that could come from a variety of sources, while 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, while data lakes store raw data that has not been processed for a purpose yet.
- Data lakes require a much larger storage capacity than data warehouses.
- Data in a data lake is flexible, quickly analyzed, and perfect for machine learning.
Learn more:✕This summary was generated using AI based on multiple online sources. To view the original source information, use the "Learn more" links.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.redpanda.com/blog/database-data-lake-data-wareh…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.
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