Towards Real-Time Cohort Analytics at Petabyte Scale: A Configuration-Driven Architecture for Healthcare Claims Data
Abstract
Healthcare cohort analytics increasingly requires the fusion of enormous longitudinal claims histories with heterogeneous reference data, delivered fast enough to support interactive clinical, actuarial, and pharmaceutical decision-making. Conventional pipelines struggle on two fronts simultaneously: the sheer volume of data—on the order of 100+ billion claim events and tens of terabytes of medical claims and reference records—and the velocity of change as new data modalities and client contracts appear. This paper proposes and evaluates a configuration-driven architecture for a real-time, in-memory analytics platform operating at petabyte-class scale. The central design principle is a generic, configuration-first application layer that onboards diverse healthcare datasets (remittance, EMR, LAAD) without per-source code forks, decoupling data heterogeneity from engineering effort. The platform is implemented on a modern, cloud-agnostic stack (Java, Apache Spark, Kafka, Docker, Kubernetes), enabling portable deployment across environments. Using a mixed evaluation of production telemetry and comparative benchmarking, the study demonstrates substantial batch-runtime reductions (multi-hour jobs compressed to a few hours), improved onboarding lead times for new modalities, and stable near-real-time query latencies under concurrent cohort workloads. Findings indicate that treating dataset onboarding as a configuration problem rather than a coding problem yields compounding gains in maintainability, throughput, and time-to-insight. The paper contributes an architectural reference model, an empirical performance characterisation, and a set of transferable engineering lessons for organisations building large-scale, multi-tenant healthcare analytics. Limitations include the observational nature of production measurement and single-organisation context, which constrain generalisability. The work advances distributed-systems practice by showing how declarative configuration and in-memory compute jointly reconcile scale with agility.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 9 No. 2 (2026): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
467-476 |
Published |
March 12, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Amirtharaj Sathees Chandran (%2026). Towards Real-Time Cohort Analytics at Petabyte Scale: A Configuration-Driven Architecture for Healthcare Claims Data. International Journal of Science, Research and Technology , Vol. 9 No. 2 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 467-476. https://doi.org/10.15662/IJSRAT.2026.0902018 |
References
2. Brewer, E. (2012) 'CAP twelve years later: how the "rules" have changed', Computer, 45(2), pp. 23–29. Available at: https://doi.org/10.1109/MC.2012.37
3. Burns, B., Grant, B., Oppenheimer, D., Brewer, E. & Wilkes, J. (2016) 'Borg, Omega, and Kubernetes', Communications of the ACM, 59(5), pp. 50–57. Available at: https://doi.org/10.1145/2890784
4. Chen, M., Mao, S. & Liu, Y. (2020) 'Big data analytics in healthcare: promise and potential', Health Information Science and Systems, 8(1), pp. 1–15. Available at: https://doi.org/10.1007/s13755-020-00120-w
5. Creswell, J.W. & Creswell, J.D. (2018) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 5th edn. Thousand Oaks: SAGE.
6. Dean, J. & Ghemawat, S. (2008) 'MapReduce: simplified data processing on large clusters', Communications of the ACM, 51(1), pp. 107–113. Available at: https://doi.org/10.1145/1327452.1327492
7. Gannon, D., Barga, R. & Sundaresan, N. (2017) 'Cloud-native applications', IEEE Cloud Computing, 4(5), pp. 16–21. Available at: https://doi.org/10.1109/MCC.2017.4250939
8. Hevner, A.R., March, S.T., Park, J. & Ram, S. (2004) 'Design science in information systems research', MIS Quarterly, 28(1), pp. 75–105. Available at: https://doi.org/10.2307/25148625
9. Hripcsak, G. et al. (2015) 'Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers', Studies in Health Technology and Informatics, 216, pp. 574–578.
10. Kleppmann, M. (2017) Designing Data-Intensive Applications. Sebastopol: O'Reilly Media.
11. Kreps, J., Narkhede, N. & Rao, J. (2011) 'Kafka: a distributed messaging system for log processing', Proceedings of the NetDB Workshop, pp. 1–7.
12. Ousterhout, K., Rasti, R., Ratnasamy, S., Shenker, S. & Chun, B.G. (2015) 'Making sense of performance in data analytics frameworks', Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI), pp. 293–307.
13. Rosenbaum, S. (2021) 'Administrative claims data and health policy research', Journal of Health Politics, Policy and Law, 46(4), pp. 561–575. Available at: https://doi.org/10.1215/03616878-8970829
14. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.F. & Dennison, D. (2015) 'Hidden technical debt in machine learning systems', Advances in Neural Information Processing Systems (NeurIPS), 28, pp. 2503–2511.
15. Shvachko, K., Kuang, H., Radia, S. & Chansler, R. (2010) 'The Hadoop distributed file system', Proceedings of the IEEE Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–10. Available at: https://doi.org/10.1109/MSST.2010.5496972
16. Stonebraker, M. & Çetintemel, U. (2018) '"One size fits all": an idea whose time has come and gone', in Making Databases Work. New York: ACM Books, pp. 441–462.
17. Wang, S.V., Schneeweiss, S. et al. (2022) 'Reporting to improve reproducibility and facilitate validity assessment for healthcare database studies', Value in Health, 25(4), pp. 481–495. Available at: https://doi.org/10.1016/j.jval.2021.10.017
18. Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M.J., Shenker, S. & Stoica, I. (2016) 'Apache Spark: a unified engine for big data processing', Communications of the ACM, 59(11), pp. 56–65. Available at: https://doi.org/10.1145/2934664
19. Marz, N. & Warren, J. (2015) Big Data: Principles and Best Practices of Scalable Real-Time Data Systems. Shelter Island: Manning Publications.
20. Abadi, D., Ailamaki, A. et al. (2019) 'The Seattle report on database research', ACM SIGMOD Record, 48(4), pp. 44–53. Available at: https://doi.org/10.1145/3385658.3385668