Explain the ETL process in data engineering. What do the steps Extract, Transform, and Load entail?

ETL stands for Extract, Transform, Load, the core process of moving data from source systems into a target data store for analysis. In the Extract phase, data is pulled from one or more heterogeneous sources such as relational databases, flat files, APIs, or streaming services. The goal is to gather all relevant data while handling connectivity, authentication, and initial filtering. Transform is the most computationally intensive step; it cleans, enriches, and reshapes the data. Common operations include type conversion, null handling, deduplication, aggregation, joining with reference tables, and applying business rules or calculations. Transform also often involves data quality checks and schema mapping to the target model. Finally, Load writes the transformed data into the destination system, which could be a data warehouse, data lake, or operational database. Load can be incremental (appending new rows) or full refresh, and may involve partitioning, indexing, or compression to optimize query performance. Together, ETL pipelines enable consistent, reliable data integration for reporting, analytics, and machine learning.