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ETL stands for Extract, Transform, Load. Data is first extracted from source systems, then transformed (cleaned, enriched, aggregated) in a staging area, and finally loaded into a target data warehouse. It is the traditional approach for data integration where transformations happen before data reaches the warehouse.
ELT stands for Extract, Load, Transform. Unlike ETL, raw data is loaded directly into the target system (e.g., a cloud data warehouse) and transformations happen inside the warehouse using its compute power. ELT leverages the scalability of modern warehouses like BigQuery, Snowflake, and Redshift to handle transformations at scale.
A data pipeline is an automated series of steps that moves data from one or more sources to a destination system. It typically includes stages for ingestion, transformation, validation, and loading. Pipelines can be batch-based, real-time, or a hybrid of both, and they are often orchestrated by tools like Apache Airflow or Prefect.
A data warehouse is a centralized repository designed for analytical querying and reporting. It stores structured, historical data that has been cleaned and transformed from operational systems. Data warehouses use schema-on-write, meaning data is structured before being stored, and are optimized for read-heavy OLAP workloads.
A star schema is a data modeling technique where a central fact table (containing measurable metrics) is surrounded by dimension tables (containing descriptive attributes). It is called a "star" because the diagram resembles a star shape. Star schemas are denormalized for fast query performance and are the most common schema in data warehouses.