Data pipeline vs ETL: key differences explained

ETL (extract, transform, load) and data pipelines are distinct concepts within data processing, where ETL represents a specific sequence of steps to move and transform data, while data pipelines encompass any automated system that transports data through various stages. Modern workflows have evolved beyond ETL, incorporating patterns such as real-time streaming, ELT (extract, load, transform), and AI-enriched pipelines, which allow for more dynamic and context-dependent data processing. Fivetran's report indicates that 72% of data teams use non-ETL patterns, reflecting a shift towards more versatile data pipelines that can handle tasks like reverse ETL and multi-step reasoning. Tools like Apache Airflow and dbt facilitate the orchestration of these complex pipelines, while platforms like CodeWords offer comprehensive solutions with over 500 integrations and native LLM support, enabling users to build sophisticated workflows without managing infrastructure.

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