Data Warehousing Essentials Quiz

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Questions and Answers

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?

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?

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?

<p>Extract, transform, load (ETL) and extract, load, transform (ELT).</p> Signup and view all the answers

What additional operations may be required for the data stored in a data warehouse?

<p>Data cleansing may be required for additional operations to ensure data quality before it is used in the data warehouse for reporting.</p> Signup and view all the answers

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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