Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Gupta, Samiksha; Kumar, Ajay; Mishra, Neha; Poonia, Ramesh Chandra Title A name given to the resource Advance Data Ingestion Framework - Integration, Processing, Transformation, and Loading Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2025 - Proceedings;pp.160-166 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ETNCC66224.2025.11299668" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ETNCC66224.2025.11299668</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031905921?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031905921?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gupta S., Jecrc University, Department of Data Analytics, Jaipur, India; Kumar A., Jecrc University, Department of Computer Science &amp; Engineering, Jaipur, India; Mishra N., Jecrc University, Department of Computer Science &amp; Engineering, Jaipur, India; Poonia R.C., Christ University, Department of Computer Science, Bangalore, India Description An account of the resource The research introduces a new concept known as the Advanced Data Ingestion Framework, which is aimed at enhancing the process of getting into stored information through some intelligent methods like data preprocessing, transformation and loading. By making use of Azure services, the platform considers distributed computing and parallel processing so that structured as well as unstructured data can be incorporated from various origins without any difficulty. To begin with, the proposed framework starts with setting up scalable Azure infrastructure and integrating SAP S4/HANA for secure and efficient data transfer purposes. Within Azure Data Factory the ingestion occurs while Delta Lake ensures proper housekeeping &amp; integrity within the system. It includes creating Power BI dashboards which allow users to see patterns easily and make better decisions based on what they know or can learn. The study brings out the flaws of current data input solutions and emphasizes the urgent requirement for a highly scalable low latency system that can support real time data processing efficiently. It tests the framework under different performance environments showing that it can effectively manage modern data within it. Finally, there is discussion about future improvements such as incorporating more sophisticated analytics or ML models thereby strengthening the decisionmaking process based on available facts. 2025 IEEE. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152565-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online