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bjoern__koth
Community Advisor and Adobe Champion
bjoern__kothCommunity Advisor and Adobe Champion

Sources Data Prep for Calculated Fields - Extend functionalities in Sources data prep to process hierarchical data structures like in DestinationsNew

Description These days, the AEP sources data prep UI for calculated fields is lacking basic functions to (easily) transform incoming data structures like arrays through a computed field into a target structure of any kind which makes it really cumbersome to work with. Also, asterisk syntax is only supported in standard mapping fields.   Why is this feature important to you Well, the source data often does not match the XDM schema data and has to be brought into the right format. The provided functions are insufficient. The destinations data prep, however, seems to contain more functions to transform hierarchies.   How would you like the feature to work Let's start with something trivial like looping over arrays to transform their data.   Destinations Data Prep: transformArray([5, 6, 7], x -> x + 1) Sources Data Prep: ???   Current Behaviour - It seems impossible or at least extremely cumbersome to do even minimal transformations on any array or object data on the Source side. This in turn puts potentially a lot of extra work on the devs to provide the right data input format, potentially increasing the size of the to be imported data significantly if the target structure has to be pre-built. This comes with a lot of implications like necessary adjustments on the data providing side, should anything ever change in the schema.

TomBa12Level 2

Allow Classifications based on Date dimensions like DayNew

Description - Allow classifications based on date dimensions like 'Day'- this would make analysis of custom date periods like financial years, financial weeks, seasons, etc. much easier. Currently if you want to work with something like this you have to create individual date ranges for every value. If you could classify 'Day', you would be able to upload a single .tab file that mapped the Day to all the custom date classifications you want, then to analyse these you would only have to drag in a single dimension in workspace.Why is this feature important to you - My analytics team have asked for better implementation for datespans like financial weeks. They currently use date ranges, which is very manual when analysing long periods that split into many date ranges as they have to drag in each week individually.How would you like the feature to work - In the report suite traffic/conversion classifications tab, add 'Day' to the available dimensions to create classifications based on. Then in classification importer you will be able to upload a classifications file that maps the Day value to custom date classifications. Current Behaviour - Currently the only way I can find to do something like this is to track a custom eVar for the date the user is on the website, then create classifications based on that. The issue with this is that it isn't retroactive, so you can't use these classifications for the time before you started tracking the evar. Currently no date dimensions are available in the classifications settings to use.

Scheduled Copy content activities to lower environments from Prod Using GenAI Models in AEM Cloud ManagerNew

Request for Feature Enhancement (RFE) Summary: Scheduled Copy content activities to lower environments from Prod environment Using GenAI Models in AEM Cloud Manager Use-case: We request the development of a feature in Adobe Experience Manager (AEM) Cloud Manager that leverages Generative AI (GenAI) models to enable the capability of scheduled copy content activities to lower environments from Prod environment. This feature would allow DevOps/Support teams to trigger content copy activities to lower environments on specific dates and times through GenAI-based workflows in a scheduled manner.   Key Benefits: Reduction in manual effort : The biggest advantage of this feature would be - a great reduction / savings of manual hours of the AEM support team/application team in the monitoring of Copy content activity , Greater efficiency and reduction in the waiting times during monitoring ( incurred by the support team while content is being copied in Stage and Dev ). Automated Scheduling: Users can set precise times and dates for copying content to lower environments, ensuring updates occur during optimal windows, such as off-peak hours, minimising disruption. This will reduce the manual effort in solving the most common business requirement of content refresh from Prod to the lower environments, after every regular interval like once in 15 days or after every release. Enhanced Efficiency: GenAI models streamline the content copy process, reducing manual intervention and potential errors. Predictive Analytics: Utilize GenAI to analyze past content copy activities and suggest optimal times for future operations, enhancing overall system performance. Customizable Workflows: Tailor content copy workflows to meet specific organizational needs, providing flexibility and control over the process. Improved Reliability: Scheduled content copy activities ensure consistency and reliability, with GenAI models monitoring and adjusting workflows as needed. Implementation Details: Integration with AEM Cloud Manager: Seamlessly integrate GenAI models within the existing AEM Cloud Manager framework. User Interface: Develop an intuitive UI for scheduling content copy activities, allowing users to easily configure and manage the timing of these operations. GenAI Workflow Engine: Create a robust workflow engine powered by GenAI to handle the scheduling, execution, and monitoring of content copy activities. Analytics Dashboard: Provide a dashboard for users to view content copy history, performance metrics, and predictive analytics. This feature will significantly enhance the content management and content backup capabilities of AEM Cloud Manager, offering application support teams a powerful tool to manage content copy activities efficiently and effectively, with very low effort. Current/Experienced Behavior: There is no such workflow/feature available in AEMaaCS as of now for scheduled copy content activities. Improved/Expected Behavior: We request the development of a feature in Adobe Experience Manager (AEM) Cloud Manager that leverages Generative AI (GenAI) models to enable the capability of scheduled copy content activities to lower environments ( at the time as scheduled by the DevOps team ), resulting in saving of manual efforts/hours during the copy content activity and enhanced efficiency. This will be of great help in solving the most common business requirement of content refresh from Prod to the lower environments, after every regular interval like once in 15 days or after every release. Environment Details (AEM version/service pack, any other specifics if applicable): AEM as a Cloud Service ( AEMaaCS ) Customer-name/Organization name: UnitedHealth Group ( UHG ) Screenshot (if applicable): N/A Code package (if applicable): N/A

Reducing Stage/Prod deployment Pipeline execution Time Using GenAI Models in AEM Cloud ManagerNew

Request for Feature Enhancement (RFE) Summary: Reducing Stage/Prod deployment Pipeline execution Time Using GenAI Models in AEM Cloud Manager Use-case: We are requesting for the development of a feature in Adobe Experience Manager (AEM) Cloud Manager that leverages Generative AI (GenAI) models to reduce the execution time required for stage and production deployment pipelines in AEM Cloud manager. This feature would fast-track regular deployment tasks in the Adobe Cloud pipeline using GenAI models , including Unit Testing, Code Scanning, Image Building, Product Functional Testing, Custom Functional Testing, Custom UI Testing, and Experience Audits. Key Benefits: Accelerated Deployment: GenAI models optimize and expedite deployment tasks, significantly reducing the overall pipeline time. Enhanced Efficiency: Automate routine tasks such as unit testing and code scanning, allowing for faster and more reliable deployments. Predictive Analytics: Utilize GenAI to predict potential issues and optimize the sequence of deployment tasks, ensuring smoother and quicker pipelines. Customizable Workflows: Tailor deployment workflows to meet specific organizational needs, providing flexibility and control over the deployment process. Improved Reliability: GenAI models continuously monitor and adjust workflows, ensuring consistent and reliable deployments. Implementation Details: Integration with AEM Cloud Manager: Seamlessly integrate GenAI models within the existing AEM Cloud Manager framework. User Interface: Develop an intuitive UI for configuring and managing deployment tasks, allowing users to easily set up and monitor pipeline activities. GenAI Workflow Engine: Create a robust workflow engine powered by GenAI to handle the optimization, execution, and monitoring of deployment tasks. Analytics Dashboard: Provide a dashboard for users to view pipeline performance metrics, historical data, and predictive analytics. This feature will significantly enhance the deployment capabilities of AEM Cloud Manager, offering users a powerful tool to manage stage and production pipelines efficiently and effectively. Essentially the idea is to reduce the overall execution time of Stage/Prod pipeline and hence making the deployments faster during the release calls etc. This will also reduce the manual effort/time spend during the release calls. Current/Experienced Behavior: Currently it takes around 1.5 hours to deploy the code to Stage and then promote to Prod, using the Stage/Prod pipeline in AEM Cloud manager. Improved/Expected Behavior: Expected or improved behaviour is cut down this execution time to around 30 mins for the entire Stage/Prod production pipeline. This can actually help in saving a lot of man hours/support hours from the application support team during the release calls. This would also make the deployment of breakfix/hotfix items to Prod very quick and easy. Environment Details (AEM version/service pack, any other specifics if applicable): AEM as a Cloud Service ( AEMaaCS ) Customer-name/Organization name: UnitedHealth Group ( UHG ) Screenshot (if applicable): NA Code package (if applicable): NA