ETL is the foundation of data warehousing and analytics. Extract: pull data from sources (databases, APIs, files). Transform: clean, validate, aggregate, and reshape data. Load: write to destination (data warehouse, data lake). Modern variations: ELT (load raw data first, transform in warehouse - enabled by cheap cloud storage and powerful query engines like BigQuery). Tools include: Apache Airflow (orchestration), dbt (transformation), Fivetran/Airbyte (extraction), and Spark (large-scale processing). Best practices: idempotent operations, incremental loading, data validation, and monitoring. ETL skills are essential for data engineering roles.
📊 Data Engineering intermediate
ETL (Extract, Transform, Load)
Data integration process that extracts data from sources, transforms it, and loads it into a destination system.
6
views