ETL (Extract, Transform, Load) processes are fundamental to data engineering, ensuring that data is efficiently and accurately integrated into a data warehouse or other data storage solutions. The ETL pipeline begins with Extract, where data is gathered from various sources such as databases, APIs, or flat files. This is followed by the Transform stage, where raw data is cleaned, filtered, and reshaped to meet the requirements of the target system. Transformation might include tasks like data normalization, aggregation, or the application of business rules. Finally, the Load phase involves moving the transformed data into a destination storage system, such as a data warehouse, where it can be accessed and analyzed. Effective ETL processes are crucial for maintaining data quality, consistency, and accessibility, enabling organizations to make informed decisions based on comprehensive and reliable datasets.