Data engineering has evolved rapidly as organizations increasingly rely on large volumes of structured and unstructured data for analytics and business intelligence. Modern ETL pipelines must handle scalability, automation, reliability, and consistency across multiple environments. Traditional approaches to ETL deployment often create problems related to dependency conflicts, inconsistent configurations, and difficult onboarding processes for developers. Docker and Docker Compose provide a modern solution by enabling teams to package applications, services, and dependencies into lightweight containers. Understanding ETL Pipelines ETL stands for Extract, Transform, and Load. It is the backbone of most data engineering workflows. The extract phase collects data from different sources such as APIs, relational databases, cloud storage systems, or streaming platforms. The transform phase cleans, validates, aggregates, and formats the data into a usable structure.…