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The difference between a data warehouse and a data mart can be understood in terms of scope, size, and purpose:
- Scope: A data warehouse is designed to consolidate data from across the entire organization, making it a centralized system. In contrast, a data mart is focused on a specific group or department within the organization, making it a decentralized system1.
- Size: Data warehouses are typically much larger in size, often exceeding 100 gigabytes and potentially reaching into terabytes. Data marts are smaller, usually less than 100 gigabytes2.
- Purpose: The data warehouse serves as a big repository of data from various departments and is used for comprehensive analysis. A data mart, being a subset of a data warehouse, is tailored to the needs of a particular business unit and is used for more specific, project-oriented tasks4.
- Design: The design process for a data warehouse is more complicated due to its large scope and the variety of data sources it integrates. Data marts have a simpler design process because they are focused on a single area5.
- Data Normalization: In a data warehouse, lightly denormalization takes place to optimize performance for analytical queries, while in a data mart, highly denormalization is common to provide quick results for specific queries1.
- Flexibility: Data warehouses are generally more flexible in terms of accommodating different types of data from various sources. Data marts are less flexible as they are designed for specific purposes1.
In summary, data warehouses are large-scale, flexible systems that integrate data from across an organization for comprehensive analysis, while data marts are smaller, focused repositories designed for specific projects or departments.
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WebNov 29, 2023 · Written by Coursera Staff • Updated on Nov 29, 2023. Data marts and data warehouses are repositories that help organizations manage their data. Here are the key differences between the two tools. …
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