When sweet and cute isn't enough anymore: Solving scalability issues in Python Pandas with Grizzly
Summary: Grizzly addresses Pandas' scalability limits by compiling Pandas DataFrame pipelines into SQL/SparkSQL and executing them in a DBMS to leverage optimized storage and query engines. Retains a Pandas-friendly API while dramatically reducing memory and CPU overhead. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Stefan Hagedorn (Technical University of Ilmenau)
BibTeX Citation
@inproceedings{hagedorn_cidr20,
address = {Amsterdam, Netherlands},
series = {{CIDR} '20},
title = {{When sweet and cute isn't enough anymore: Solving scalability issues in Python Pandas with Grizzly}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Hagedorn, Stefan},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,384 | Putting Pandas in a Box | 2021 | CIDR | 7.3550055e-05 |
| 11,570 | SplitDF: Splitting Dataframes for Memory-Efficient Data Analysis | 2024 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,745 | Selecting Subexpressions to Materialize at Datacenter Scale | 2018 | VLDB | 9.7343818e-05 |
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