Apache Wayang in Action: Enabling Data Systems Integration via a Unified Data Analytics Framework
Summary: Apache Wayang offers a unified analytics layer that decouples apps from data engines and coordinates heterogeneous sources. An optimizer selects cost-based deployment plans across systems, enabling seamless integration and better performance. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kaustubh Beedkar (Indian Institute of Technology Delhi)
- 2. Aurélien Bertrand (IT University of Copenhagen)
- 3. Haralampos Gavriilidis (Technical University of Berlin)
- 4. Augusto Fonseca (National Laboratory for Scientific Computing)
- 5. Zoi Kaoudi (IT University of Copenhagen)
- 6. Mingxi Liu (East China Normal University)
- 7. Volker Markl (Berlin Institute for the Foundations of Learning and Data; German National Research Center for Information Technology; Technical University of Berlin)
- 8. Juri Petersen (IT University of Copenhagen)
- 9. Fabio Porto (National Laboratory for Scientific Computing)
- 10. Víctor Ribeiro (National Laboratory for Scientific Computing)
- 11. Mads Sejer Pedersen (IT University of Copenhagen)
- 12. Lucas Tavares (National Laboratory for Scientific Computing)
- 13. Michalis Vargiamis (Scalytics)
- 14. Chen Xu (East China Normal University)
BibTeX Citation
@inproceedings{beedkar_sigmod25,
title = {{Apache Wayang in Action: Enabling Data Systems Integration via a Unified Data Analytics Framework}},
author = {Beedkar, Kaustubh and Bertrand, Aurélien and Gavriilidis, Haralampos and Fonseca, Augusto and Kaoudi, Zoi and Liu, Mingxi and Markl, Volker and Petersen, Juri and Porto, Fabio and Ribeiro, Víctor and Pedersen, Mads Sejer and Tavares, Lucas and Vargiamis, Michalis and Xu, Chen},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725081},
url = {https://dl.acm.org/doi/10.1145/3722212.3725081},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,551 | APEROL: Adaptive Parallel Edge-to-cloud Runtime Optimization for Layered Workflow Execution | 2026 | VLDB | 5.093636e-05 |
| 11,065 | Learned Cost Models for Query Optimization: From Batch to Streaming Systems | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,351 | RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! - | 2018 | VLDB | 7.4937347e-05 |
| 7,000 | A Cost-based Optimizer for Gradient Descent Optimization | 2017 | SIGMOD | 5.7287645e-05 |
| 9,742 | Unified Data Analytics: State-of-the-art and Open Problems | 2022 | VLDB | 5.227679e-05 |
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