Which term refers to the process of converting raw data into a structured format?

Which term refers to the process of converting raw data into a structured format?

• A. Integration
• B. Normalization
• C. Wrangling
• D. Aggregation

Answer: (C)

The term that refers to the process of converting raw data into a structured format is known as wrangling. This process involves cleaning, reorganizing, and preparing the data so that it is structured and formatted for analysis. Data wrangling typically includes tasks such as removing inconsistencies, handling missing values, and transforming data types, which helps in making the data more usable and accessible for analytics purposes. The goal of data wrangling is to take unrefined data and refine it into a structured format that can be easily analyzed or fed into machine learning models. This step is crucial in the data engineering process since the quality and structure of input data significantly influence the outcomes of data analyses and models. Integration typically refers to the combination of data from different sources into a unified view, whereas normalization involves adjusting values in a dataset to a common scale, and aggregation involves compiling and summarizing the data for reporting or analysis. While these processes are important, they serve different purposes compared to the specific focus of wrangling on formatting raw data.

The term that refers to the process of converting raw data into a structured format is known as wrangling. This process involves cleaning, reorganizing, and preparing the data so that it is structured and formatted for analysis. Data wrangling typically includes tasks such as removing inconsistencies, handling missing values, and transforming data types, which helps in making the data more usable and accessible for analytics purposes.

The goal of data wrangling is to take unrefined data and refine it into a structured format that can be easily analyzed or fed into machine learning models. This step is crucial in the data engineering process since the quality and structure of input data significantly influence the outcomes of data analyses and models.

Integration typically refers to the combination of data from different sources into a unified view, whereas normalization involves adjusting values in a dataset to a common scale, and aggregation involves compiling and summarizing the data for reporting or analysis. While these processes are important, they serve different purposes compared to the specific focus of wrangling on formatting raw data.