An ETL pipeline is a process that extracts raw, unstructured data from various sources, transforms it into a standardized format suitable for analysis or business needs, and loads it into a target system designed for storing vast amounts of information. The three main processes involved in an ETL pipeline are extract, transform, and load, which work together to bridge the gap between siloed data and actionable insights. By moving valuable data from point A to point B, ETL pipelines enable businesses to make informed decisions based on accurate and unified data, improve data quality, increase efficiency, provide analytics for decision-making, scale to accommodate growing needs, and offer benefits such as data integration, improved data quality, higher efficiency, analytics for decision-making, scalability, creating reference data, extracting and standardizing data, validating data, transforming data, staging data, loading data, scheduling future processing, and activating valuable customer data. ETL pipelines differ from other solutions like data pipelines, ELT, and Reverse ETL in terms of their focus on preparing data for analysis and loading it into a target system, prioritizing data quality, and offering flexibility in the transformation process. Reverse ETL, a subset of ETL, allows businesses to move valuable data back into operational systems, enhancing personalization at scale, boosting operational efficiency, enabling real-time decision-making, and providing a single source of customer truth.