The correct answer is: Data Transformation.
Data transformation is the process of converting data from one format to another, or from one system to another. It is often used to prepare data for analysis or storage. Data transformation can be a complex process, and it is important to choose the right tools and techniques for the job.
There are many different ways to transform data. Some common methods include:
- Data cleansing: This is the process of removing errors and inconsistencies from data.
- Data deduplication: This is the process of identifying and removing duplicate data.
- Data standardization: This is the process of converting data into a standard format.
- Data integration: This is the process of combining data from multiple sources into a single data set.
Data transformation is an essential part of data management. It is used to prepare data for analysis, storage, and reporting. Data transformation can also be used to improve the quality of data.
A. Data Integration is the process of combining data from multiple sources into a single data set. This can be done manually or using data integration tools. Data integration is often used to create a data warehouse or data mart.
B. Data Wrangling is the process of cleaning and organizing data so that it can be used for analysis. This can be a complex process, and it is often necessary to use data wrangling tools. Data wrangling is often used to prepare data for data mining or machine learning.
C. Data Cleaning is the process of identifying and removing errors from data. This can be a complex process, and it is often necessary to use data cleaning tools. Data cleaning is often used to prepare data for analysis or storage.
I hope this helps!