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Questions and Answers
In computing, what is a data warehouse used for?
In computing, what is a data warehouse used for?
A data warehouse is used for reporting and data analysis and is considered a core component of business intelligence.
What is the benefit of using a data warehouse for companies?
What is the benefit of using a data warehouse for companies?
It enables companies to interrogate and draw insights from their data and make decisions.
Where does the data stored in a data warehouse come from?
Where does the data stored in a data warehouse come from?
The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales).
What are the two main approaches used to build a data warehouse system?
What are the two main approaches used to build a data warehouse system?
What additional operations may be required for the data stored in a data warehouse?
What additional operations may be required for the data stored in a data warehouse?
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Study Notes
Data Warehouse Overview
- A data warehouse is a central repository that stores data from various sources in a single location, making it easily accessible for analysis and reporting.
Benefits of Data Warehouses
- Data warehouses help companies make informed business decisions by providing a single, unified view of their operations.
- Data warehouses enable companies to analyze their data to identify trends, patterns, and correlations.
Data Sources for Data Warehouses
- Data stored in a data warehouse comes from various sources, including operational databases, external sources, and existing data systems.
Approaches to Building a Data Warehouse System
- The top-down approach involves designing a data warehouse from a business perspective, focusing on the overall architecture and then implementing it.
- The bottom-up approach involves building a data warehouse incrementally, starting with a specific business problem or opportunity.
Additional Operations for Data Warehouse Data
- Data in a data warehouse may require additional operations, such as data cleansing, data transformation, and data aggregation, to ensure data quality and make it analysis-ready.
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