Quick answer: SQL + Python + Spark / Airflow — feed AI systems with clean data.
Data pipelines are automated workflows that collect, clean, transform, and move data from source systems to where it's needed—typically feeding machine learning models and analytics engines. They're the backbone of every AI system: without clean, timely data flowing reliably into your models, even the best AI algorithms fail.
Using SQL for data transformation, Python for orchestration logic, and tools like Apache Spark for processing large datasets or Airflow for scheduling workflows, you build systems that handle millions of records daily. For example, an e-commerce company's data pipeline might extract customer behavior from multiple databases, clean and deduplicate records, calculate features like "purchase frequency," and deliver that data hourly to a recommendation engine.