The data in Data Warehouse is generally:
-
Clean Data
-
Dirty Data
-
Clean and Dirty Data
-
None of above
A core purpose of data warehousing is the ETL (Extract, Transform, Load) process that cleanses source data by removing errors, handling missing values, standardizing formats, and applying business rules. The data warehouse stores this cleaned, consistent, integrated data - this is a fundamental differentiator from operational systems that may contain dirty, inconsistent data.
To answer this question, you need to understand the concept of a Data Warehouse and the quality of data stored in it.
A Data Warehouse is a large, centralized repository that stores data from various sources for reporting and analysis purposes. The primary goal of a Data Warehouse is to provide accurate and reliable data for decision-making.
Option A) Clean Data - This option is correct because the data in a Data Warehouse is generally clean. Before data is loaded into a Data Warehouse, it goes through a process called Extract, Transform, and Load (ETL). During the transformation phase of ETL, data is cleaned, standardized, and validated to remove any inconsistencies or errors.
Option B) Dirty Data - This option is incorrect because the data in a Data Warehouse is not generally dirty. The ETL process ensures that data is cleaned and validated before being loaded into the Data Warehouse.
Option C) Clean and Dirty Data - This option is incorrect because the data in a Data Warehouse is primarily clean. While it is possible for some dirty data to exist in a Data Warehouse, it is not the norm. The ETL process aims to eliminate dirty data as much as possible.
Option D) None of the above - This option is incorrect because the correct answer is option A, which states that the data in a Data Warehouse is generally clean.
The correct answer is option A. The data in a Data Warehouse is generally clean because it goes through a rigorous ETL process to ensure its accuracy and reliability.