The marketing department at a large retail company complains that running analytics on the enterprise data warehouse is too slow. Their queries for campaign performance analysis do not require access to finance or HR data, but the current system forces them to scan through these unrelated datasets. To improve query performance and provide the marketing team with a tailored dataset for their specific analytical needs, which type of data repository should a data analyst recommend building?
A data mart is the correct solution because it is a subset of a data warehouse that is focused on a specific line of business or department, such as marketing. By creating a data mart, the marketing team gets a smaller, tailored repository with only the data they need, which significantly improves query performance and simplifies analysis.
A data warehouse is incorrect because the scenario states the team is already using the enterprise data warehouse, and it is the source of the performance problem due to its broad scope.
A data lake is incorrect as it is a centralized repository for vast amounts of raw, often unstructured, data. The marketing team needs optimized, structured data for analysis, not a raw data store.
A data lakehouse is a modern architecture that combines features of data lakes and data warehouses, but it is a broader solution. The most specific and appropriate solution for creating a department-focused analytical store from an existing warehouse is a data mart.
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CompTIA Data+ DA0-002 (V2)
Data Concepts and Environments
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