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Multidisciplinary Research in Computing Information Systems
Article . 2023 . Peer-reviewed
License: CC BY NC ND
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License: CC BY
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Other literature type . 2023
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2023
License: CC BY
Data sources: Datacite
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Automating AEM Content Lifecycle Management Using Python and Adobe APIs

Authors: Dayasagar Vangala;

Automating AEM Content Lifecycle Management Using Python and Adobe APIs

Abstract

The explosive nature of digital content among enterprise organizations has posed unprecedented challenges in the management of content lifecycles in an efficient manner within Adobe Experience Manager (AEM). The current research paper is a detailed study of automation of the AEM content lifecycle management via Python scripting and Adobe APIs, with the focus on patterns of implementation, automation system models, and business results. This paper explores the ways that organizations can use programmatic solutions to automate content operations without sacrificing the standards of governance or quality, through the systematic analysis of API integration strategies, Python automation libraries, and content workflow optimization strategies. The study is based on a multi-methodology that includes the technical implementation analysis, performance benchmarking, and case study analysis to discover the best patterns of automating AEM content. Results indicate that organizations using Python-based automation of Adobe APIs have experienced 55-75% less manual content management effort, 40-60% improved content lifecycle processing and 35-50% content governance compliance. The paper has shown that Python has a large library ecosystem and AEM has full REST APIs which can be used to automate complex content operations such as version control, publishing processes, archiving processes, and content auditing. Moreover, the study notes that the most flexible solution to automating enterprise-scale content operations without compromising security and performance levels is to use custom Python frameworks that connect to the Content Services APIs of AEM. This paper offers an organized approach to designing, developing and optimizing python-based automation systems to support the entire content lifecycle, encompassing creation and archival. The conclusions provide a practical advice to the AEM administrators, content operations specialists, and automated engineers operating in the contents management environments of enterprises to improve the efficiency of operations at the enterprises and minimize the number of manual errors and risk of compliance in the content management settings.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
hybrid