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About this paper
Kevin M. Kramer, Valerie Restat, Sebastian Strasser, Uta Störl, Meike Klettke
A three-level hierarchical framework for next-generation data engineering pipelines is proposed, advancing them towards autonomy, continuous data quality optimization, and robust self-adaptation to evolving data characteristics. The framework utilizes abstract profiles and their diffs for monitoring and dynamic adjustments.
Key insights
- Automated composition and optimization of data engineering pipelines can achieve high data quality independently of downstream analysis objectives.
- Continuous generation and comparison of data and error profiles enable pipelines to become "self-aware" of structural and semantic changes in data.
- A MAPE-K inspired self-adaptation process allows pipelines to automatically react to detected changes by adjusting configurations or structure, ensuring continued functionality and data quality.
Summary by alphaXiv.
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