Automating Enterprise Storage Provisioning with PowerShell and Python: Effects on Deployment Time and Configuration Errors

Authors

  • Mohammed Nazir Honda North America, Raymond, OH Author

Abstract

Background: Enterprise storage provisioning frequently spans array configuration, host presentation, fabric or network connectivity, multipath validation, and operating-system or hypervisor integration. Manual execution across these layers is slow, difficult to reproduce, and vulnerable to omission, transcription, and sequencing errors. PowerShell and Python provide two widely adopted mechanisms for converting such workflows into testable, repeatable automation.

Objective: To synthesize evidence relevant to automated infrastructure provisioning and evaluate how PowerShell- and Python-based storage automation influence deployment time, configuration errors, reproducibility, and operational control in large enterprise environments.

Methods: A structured integrative review was conducted across peer-reviewed software-engineering and infrastructure-automation literature together with standards and established enterprise-storage automation sources. Evidence was organized around four domains: deployment lead time, configuration quality, automation reliability, and language/platform fit. A storage-specific measurement framework was then derived to support reproducible evaluation of manual and automated provisioning workflows.

Results: The literature consistently supports automation as a mechanism for reducing operator-active time, eliminating repeated hand-offs, and increasing execution consistency. PowerShell is especially effective where storage administration is tightly coupled to Windows, VMware PowerCLI, or vendor cmdlets, whereas Python offers broad cross-platform portability and direct REST/SDK integration. Automation reduces preventable omission, transcription, and ordering errors, but shifts risk toward script defects, hard-coded configuration, inadequate exception handling, privilege misuse, and API-version incompatibility. Idempotence, pre-flight validation, modular design, version control, logging, and post-provision verification are therefore central to realizing quality benefits. Direct head-to-head quantitative trials in enterprise storage are limited, making locally measured deployment-time and error-rate baselines essential.

Conclusions: PowerShell and Python can materially improve enterprise storage provisioning when used as controlled engineering assets rather than ad hoc scripts. The largest benefits arise from removing manual touchpoints and standardizing decision logic, while the principal residual risks are concentrated in automation-code quality and integration governance. A closed-loop, idempotent, observable provisioning model provides the most defensible route to faster deployment with fewer configuration errors.

References

Rahman A, Mahdavi-Hezaveh R, Williams L. A systematic mapping study of infrastructure as code research. Inf Softw Technol. 2019;108:65-77. doi:10.1016/j.infsof.2018.12.004.

Rahman A, Williams L. Source code properties of defective infrastructure as code scripts. Inf Softw Technol. 2019;112:148-163. doi:10.1016/j.infsof.2019.04.013.

Sharma T, Fragkoulis M, Spinellis D. Does your configuration code smell? In: Proceedings of the 13th Working Conference on Mining Software Repositories. New York: ACM; 2016. p. 189-200. doi:10.1145/2901739.2901761.

Jiang Y, Adams B. Co-evolution of infrastructure and source code: an empirical study. In: 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories. IEEE; 2015. p. 45-55. doi:10.1109/MSR.2015.12.

Laukkanen E, Itkonen J, Lassenius C. Problems, causes and solutions when adopting continuous delivery: a systematic literature review. Inf Softw Technol. 2017;82:55-79. doi:10.1016/j.infsof.2016.10.001.

Chen L. Continuous Delivery: overcoming adoption challenges. J Syst Softw. 2017;128:72-86. doi:10.1016/j.jss.2017.02.013.

Shahin M, Babar MA, Zahedi M, Zhu L. Beyond continuous delivery: an empirical investigation of continuous deployment challenges. In: 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. IEEE; 2017. p. 111-120. doi:10.1109/ESEM.2017.18.

Lwakatare LE, Kilamo T, Karvonen T, Sauvola T, Heikkilä V, Itkonen J, et al. DevOps in practice: a multiple case study of five companies. Inf Softw Technol. 2019;114:217-230. doi:10.1016/j.infsof.2019.06.010.

Huijgens H, Greuter E, Brons J, van Doorn EA, Papadopoulos I, Martinez FM, et al. Factors affecting cloud infra-service development lead times: a case study at ING. In: Proceedings of the IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice. IEEE; 2019. p. 233-242. doi:10.1109/ICSE-SEIP.2019.00033.

Ebert C, Gallardo G, Hernantes J, Serrano N. DevOps. IEEE Softw. 2016;33(3):94-100. doi:10.1109/MS.2016.68.

Forsgren N, Humble J, Kim G. Accelerate: the science of lean software and DevOps. Portland (OR): IT Revolution Press; 2018.

Klein J, Reynolds D. Infrastructure as Code: Final Report. Pittsburgh (PA): Software Engineering Institute, Carnegie Mellon University; 2019.

Storage Networking Industry Association. Swordfish Scalable Storage Management API Specification. Version 1.2.0. Colorado Springs (CO): SNIA; 2020.

Pure Storage. FlashArray REST API 1.9 Reference Guide. Mountain View (CA): Pure Storage; 2016.

NetApp. NetApp PowerShell Toolkit 9.6 released. Sunnyvale (CA): NetApp; 2019.

Stamen D. Using Python to provision your Pure Storage infrastructure. 2020.

Stamen D. Using PowerCLI to provision your Pure Storage infrastructure. 2020.

Hanappi O, Hummer W, Dustdar S. Asserting reliable convergence for configuration management scripts. In: Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications. New York: ACM; 2016. p. 328-343. doi:10.1145/2983990.2984000.

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2021-07-01

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Vol. 1 No. 1 (2021): Inaugural Issue

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