A marketing department needs to create reports and dashboards focused exclusively on customer demographics and campaign performance. The data required is a pre-processed, structured subset of the main corporate data warehouse. To ensure high-speed query performance for their business intelligence tools, they need to build a repository tailored to their specific analytical needs. Which type of data repository best fits this scenario?
The correct answer is a data mart. A data mart is a subject-oriented repository of data that is a subset of a larger data warehouse and is designed for the specific needs of a particular department or line of business. In this scenario, the marketing department requires a focused, pre-processed subset of data for high-performance analytics, which is the exact use case for a data mart.
A data lake is incorrect because it is a centralized repository that stores vast amounts of raw, unprocessed data in its native format. The scenario specifies that the data is already pre-processed and structured.
A data lakehouse is an architectural approach that combines the features of a data lake and a data warehouse. While it supports analytics, it is a broader solution for managing all types of data at scale, not a smaller, departmental subset created from an existing warehouse.
A relational database is a general term for a database that stores data in a structured format of rows and columns. While a data mart is often built using relational database technology , "data mart" is the more specific and accurate term that describes the repository's purpose and role within the overall data architecture.
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CompTIA Data+ DA0-002 (V2)
Data Concepts and Environments
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