Intelligent Scaling in Amazon Redshift
Summary: AI-powered RAIS enables vertical and horizontal scaling in Redshift with dynamic compute provisioning and automatic warehouse-size tuning for varying workloads. Shows up to 7.6x cost and 14.2x query-time improvements over baselines across ad-hoc, ETL, and data-growth scenarios. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Vikram Nathan (Amazon)
- 2. Vikramank Singh (Amazon)
- 3. Zhengchun Liu (Amazon)
- 4. Mohammad Rahman (Amazon)
- 5. Andreas Kipf (University of Technology Nuremberg)
- 6. Dominik Horn (Amazon)
- 7. Davide Pagano (Amazon)
- 8. Gaurav Saxena (Amazon)
- 9. Balakrishnan Narayanaswamy (Amazon)
- 10. Tim Kraska (Amazon)
BibTeX Citation
@inproceedings{nathan_sigmod24,
title = {{Intelligent Scaling in Amazon Redshift}},
author = {Nathan, Vikram and Singh, Vikramank and Liu, Zhengchun and Rahman, Mohammad and Kipf, Andreas and Horn, Dominik and Pagano, Davide and Saxena, Gaurav and Narayanaswamy, Balakrishnan and Kraska, Tim},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3626246.3653394},
url = {https://dl.acm.org/doi/10.1145/3626246.3653394},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 86 | Automatic Database Management System Tuning Through Large-scale Machine Learning | 2017 | SIGMOD | 0.00035316107 |
| 347 | Tuning Database Configuration Parameters with iTuned | 2009 | VLDB | 0.00020651582 |
| 378 | Bao: Making Learned Query Optimization Practical | 2021 | SIGMOD | 0.00019638121 |
| 818 | Amazon Redshift Re-invented | 2022 | SIGMOD | 0.00013822916 |
| 2,091 | iCBS: Incremental Cost-based Scheduling under Piecewise Linear SLAs | 2011 | VLDB | 9.1867579e-05 |
| 2,958 | WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases | 2016 | VLDB | 7.9197796e-05 |
| 3,071 | Choosing A Cloud DBMS: Architectures and Tradeoffs | 2019 | VLDB | 7.7885156e-05 |
| 3,809 | Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift | 2023 | SIGMOD | 7.1074195e-05 |
| 5,148 | LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems | 2022 | SIGMOD | 6.3465986e-05 |
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|---|---|---|---|---|
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| 9 | 818 | Amazon Redshift Re-invented | 2022 | SIGMOD |
| 10 | 3,585 | The evolution of Amazon Redshift (extended abstract) | 2021 | VLDB |