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Data extraction or injection occurs next, often via tools like Kafka or direct loads into data warehouses such as BigQuery or open-source query engines like Presto. Within this architecture lies a transformation layer, typically orchestrated through workflow managers like Apache Airflow, accompanied by data cataloging and self-service tools enabling users to explore datasets independently.

While this architecture has served well over the years, the advent of AI exposes its limitations and reveals new opportunities for optimization.

Challenges Faced by Traditional Data Engineering Teams

Despite a robust legacy, traditional data pipelines grapple with several persistent issues: