The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward
Summary: Cosmos' exabyte-scale evolution at Microsoft spans reliability, scale, efficiency, and usability, with next steps toward security, compliance, and heterogeneous analytics. The paper links Cosmos workload evolution to broad big-data trends, offering platform-driven design insights for researchers. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Conor Power (Microsoft)
- 2. Hiren Patel (Microsoft)
- 3. Alekh Jindal (Microsoft)
- 4. Jyoti Leeka (Microsoft)
- 5. Bob Jenkins (Microsoft)
- 6. Michael Rys (Microsoft)
- 7. Ed Triou (Microsoft)
- 8. Dexin Zhu (Microsoft)
- 9. Lucky Katahanas (Microsoft)
- 10. Chakrapani Bhat Talapady (Microsoft)
- 11. Joshua Rowe (Microsoft)
- 12. Fan Zhang (Microsoft)
- 13. Rich Draves (Microsoft)
- 14. Marc Friedman (Microsoft)
- 15. Ivan Santa Maria Filho (Microsoft)
- 16. Amrish Kumar (Microsoft)
BibTeX Citation
@article{power_vldb21,
title = {{The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward}},
author = {Power, Conor and Patel, Hiren and Jindal, Alekh and Leeka, Jyoti and Jenkins, Bob and Rys, Michael and Triou, Ed and Zhu, Dexin and Katahanas, Lucky and Talapady, Chakrapani Bhat and Rowe, Joshua and Zhang, Fan and Draves, Rich and Friedman, Marc and Filho, Ivan Santa Maria and Kumar, Amrish},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {3148--3161},
doi = {10.14778/3476311.3476390},
url = {https://doi.org/10.14778/3476311.3476390},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,998 | Deploying a Steered Query Optimizer in Production at Microsoft | 2022 | SIGMOD | 6.9676473e-05 |
| 7,762 | Runtime Variation in Big Data Analytics | 2023 | SIGMOD | 5.5501898e-05 |
| 8,193 | Towards Building Autonomous Data Services on Azure | 2023 | SIGMOD | 5.4696038e-05 |
| 8,861 | Optimizing the cloud? Don't train models. Build oracles! | 2024 | CIDR | 5.355716e-05 |
| 8,864 | Pipemizer: An Optimizer for Analytics Data Pipelines | 2022 | VLDB | 5.355022e-05 |
| 10,687 | Asynchronous Replication Strategies for a Real-Time DBMS | 2025 | SIGMOD | 5.093636e-05 |
| 10,966 | UniClean: A Scalable Data Cleaning Solution for Mixed Errors based on Unified Cleaners and Optimized Cleaning Workflow | 2025 | VLDB | 5.093636e-05 |
| 10,997 | The HANA Native Query Engine for Lakehouse Systems | 2025 | VLDB | 5.093636e-05 |
| 11,150 | Proactive Resume and Pause of Resources for Microsoft Azure SQL Database Serverless | 2024 | SIGMOD | 5.093636e-05 |
| 13,399 | PikePlace: Generating Intelligence for Marketplace Datasets | 2023 | VLDB | - |
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Outgoing Citations (Sorted by Pagerank)
Showing 32 of 32 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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