cedar: Optimized and Unified Machine Learning Input Data Pipelines

Summary: Cedar provides a unified, composable framework for ML input pipelines across arbitrary frameworks, with an extensible optimizer that combines execution optimizations. It orchestrates local/distributed resources and delivers 1.87–10.65× speedups over existing systems. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: h023ca72e87513528
Venue: VLDB
Year: 2025
Pagerank: 5.0979044e-05
Overall Rank: 10,000 | 32.77%
DOI: 10.14778/3705829.3705861

Incoming Non-self Citations Over Time

Authors

1. Mark Zhao (Stanford University)
2. Emanuel Adamiak (Stanford University)
3. Christos Kozyrakis (Stanford University)

BibTeX Citation

@article{zhao_vldb25,
        title = {{cedar: Optimized and Unified Machine Learning Input Data Pipelines}},
        author = {Zhao, Mark and Adamiak, Emanuel and Kozyrakis, Christos},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {2},
        pages = {488--502},
        doi = {10.14778/3705829.3705861},
        url = {https://doi.org/10.14778/3705829.3705861},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,798	NeurIDA: Dynamic Modeling for Effective In-Database Analytics	2026	VLDB	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
23	Spark SQL: Relational Data Processing in Spark	2015	SIGMOD	0.00055406774
52	The Snowflake Elastic Data Warehouse	2016	SIGMOD	0.00041219077
292	LINQ: Reconciling Objects, Relations and XML in the .NET Framework	2006	SIGMOD	0.00021947834
325	The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing	2015	VLDB	0.00020964941
459	Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores	2020	VLDB	0.00017856221
1,475	Analyzing and Mitigating Data Stalls in DNN Training	2021	VLDB	0.00010556672
2,053	tf.data: A Machine Learning Data Processing Framework	2021	VLDB	9.1140407e-05
2,195	Opening the Black Boxes in Data Flow Optimization	2012	VLDB	8.8781177e-05
2,264	An Intermediate Representation for Optimizing Machine Learning Pipelines	2019	VLDB	8.7289107e-05
3,601	Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines	2022	SIGMOD	7.1757877e-05
3,811	FastFlow: Accelerating Deep Learning Model Training with Smart Offloading of Input Data Pipeline	2023	VLDB	7.0087478e-05
5,381	GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning	2023	SIGMOD	6.1550396e-05
7,827	FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation	2024	VLDB	5.4442509e-05

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