Designing and Implementing a Data Science Solution on Azure (DP-100) 2026 – 400 Free Practice Questions to Pass the Exam

ETL stands for Extract, Transform, Load, which represents a process used in data integration and data warehousing. This process involves three key steps:

1. **Extract**: Data is collected from various source systems. This can include databases, application software, and other data repositories. The extraction phase is critical because it allows organizations to consolidate data from disparate sources into a single system for analysis.

2. **Transform**: Once the data is extracted, it must be transformed into a suitable format for analysis. This transformation process can involve cleaning the data (removing inaccuracies), converting data types, aggregating data, or enriching it. The goal of this phase is to ensure that the data fits the requirements of the target system or analysis processes.

3. **Load**: After transformation, the data is loaded into the target system, which is typically a data warehouse or a data lake. This step ensures that decision-makers have access to reliable and formatted data for reporting and analysis.

The ETL process is essential for organizations that require comprehensive data analysis, enabling better decision-making and improved operational efficiency. The other options do not reflect the widely recognized definition of ETL used in data processing and were not aligned with the established practices in data management and analytics