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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)

Paper ID
373
Venue
CIDR
Year
2020
Pagerank
4.3441378e-05
Overall Rank
9,416 | 34.50%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
4,813 Putting Pandas in a Box 2021 CIDR 5.9049746e-05
11,024 SplitDF: Splitting Dataframes for Memory-Efficient Data Analysis 2024 VLDB 4.1945683e-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
1,922 Selecting Subexpressions to Materialize at Datacenter Scale 2018 VLDB 0.00010082599
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