Magpie: Python at Speed and Scale using Cloud Backends
Summary: Magpie exposes the Pandas API but lazily pushes dataframe work into cloud query engines (SQL DW, Spark, SCOPE) through a common data layer, avoiding cross-engine transfer and leveraging DB-grade features. It auto-selects optimal backends to deliver database-scale performance to Python analytics; production traces show ~25% of internal computations could benefit. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Alekh Jindal (Microsoft)
- 2. K. Venkatesh Emani (Microsoft)
- 3. Maureen Daum (University of Washington)
- 4. Olga Poppe (Microsoft)
- 5. Brandon Haynes (Microsoft)
- 6. Anna Pavlenko (Microsoft)
- 7. Ayushi Gupta (Apple)
- 8. Karthik Ramachandra (Microsoft)
- 9. Carlo Curino (Microsoft)
- 10. Andreas Mueller (Microsoft)
- 11. Wentao Wu (Microsoft)
- 12. Hiren Patel (Microsoft)
BibTeX Citation
@inproceedings{jindal_cidr21,
address = {Amsterdam, Netherlands},
series = {{CIDR} '21},
title = {{Magpie: Python at Speed and Scale using Cloud Backends}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Jindal, Alekh and Emani, K. Venkatesh and Daum, Maureen and Poppe, Olga and Haynes, Brandon and Pavlenko, Anna and Gupta, Ayushi and Ramachandra, Karthik and Curino, Carlo and Mueller, Andreas and Wu, Wentao and Patel, Hiren},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
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