Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML

Summary: Predicts enterprise adoption of DBMS-native ML that tightly integrates model lifecycle with rigorous data governance, privacy, and security at scale. Identifies unmet requirements and DB research challenges (provenance, auditing, access control, federated/secure training, deployment/auto-tuning) and sketches early system-building steps. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 386
Venue: CIDR
Year: 2020
Pagerank: 7.2568185e-05
Overall Rank: 3,614 | 75.21%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Ashvin Agrawal (Microsoft)
2. Rony Chatterjee (Microsoft)
3. Carlo Curino (Microsoft)
4. Avrilia Floratou (Microsoft)
5. Neha Gowdal (Microsoft)
6. Matteo Interlandi (Microsoft)
7. Alekh Jindal (Microsoft)
8. Konstantinos Karanasos (Microsoft)
9. Subru Krishnan (Microsoft)
10. Brian Kroth (Microsoft)
11. Jyoti Leeka (Microsoft)
12. Kwanghyun Park (Microsoft)
13. Hiren Patel (Microsoft)
14. Olga Poppe (Microsoft)
15. Fotis Psallidas (Microsoft)
16. Raghu Ramakrishnan (Microsoft)
17. Abhishek Roy (Microsoft)
18. Karla Saur (Microsoft)
19. Rathijit Sen (Microsoft)
20. Markus Weimer (Microsoft)
21. Travis Wright (Microsoft)
22. Yiwen Zhu (Microsoft)

BibTeX Citation

@inproceedings{agrawal_cidr20,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '20},
        title = {{Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Agrawal, Ashvin and Chatterjee, Rony and Curino, Carlo and Floratou, Avrilia and Gowdal, Neha and Interlandi, Matteo and Jindal, Alekh and Karanasos, Konstantinos and Krishnan, Subru and Kroth, Brian and Leeka, Jyoti and Park, Kwanghyun and Patel, Hiren and Poppe, Olga and Psallidas, Fotis and Ramakrishnan, Raghu and Roy, Abhishek and Saur, Karla and Sen, Rathijit and Weimer, Markus and Wright, Travis and Zhu, Yiwen},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 17 of 17 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,293	Extending Relational Query Processing with ML Inference	2020	CIDR	8.7949378e-05
2,347	Vertica-ML: Distributed Machine Learning in Vertica Database	2020	SIGMOD	8.7157552e-05
2,584	Complaint-driven Training Data Debugging for Query 2.0	2020	SIGMOD	8.3783546e-05
2,865	End-to-end Optimization of Machine Learning Prediction Queries	2022	SIGMOD	8.0180243e-05
4,067	Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches	2021	VLDB	6.9293511e-05
4,240	LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems	2021	SIGMOD	6.809685e-05
4,756	Improving Reproducibility of Data Science Pipelines through Transparent Provenance Capture	2020	VLDB	6.5221401e-05
5,704	Optimizing Data Pipelines for Machine Learning in Feature Stores	2023	VLDB	6.1146371e-05
6,121	The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward	2021	VLDB	5.9688569e-05
6,614	Mitigating the Impedance Mismatch between Prediction Query Execution and Database Engine	2025	SIGMOD	5.8216658e-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
9,649	Transforming ML Predictive Pipelines into SQL with MASQ	2021	SIGMOD	5.2430158e-05
9,835	Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem	2022	VLDB	5.2112879e-05
10,130	Does A Fish Need a Bicycle? The Case for On-Chip NPUs in DBMS	2026	CIDR	5.093636e-05
11,355	Git is for Data	2023	CIDR	5.093636e-05
11,512	Towards Observability for Machine Learning Pipelines	2022	CIDR	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
106	The MADlib Analytics Library or MAD Skills, the SQL	2012	VLDB	0.00033539462
894	Froid: Optimization of Imperative Programs in a Relational Database	2018	VLDB	0.00013367658
1,224	Dhalion: Self-Regulating Stream Processing in Heron	2017	VLDB	0.00011596911
1,468	Towards a Learning Optimizer for Shared Clouds	2019	VLDB	0.00010686496
2,239	An Intermediate Representation for Optimizing Machine Learning Pipelines	2019	VLDB	8.8875753e-05
2,293	Extending Relational Query Processing with ML Inference	2020	CIDR	8.7949378e-05
2,822	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	8.0898536e-05

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