Authors:
R. Vanitha, C. Gayathri, P. Mohaideen Fathima, V. Sabaresan, Samson Davidson, Arno Onnen, Ijaz Ahad

Addresses:
Department of Computer Science and Engineering, KCG College of Technology, Chennai, Tamil Nadu, India. Department of Computer Science and Engineering, Easwari Engineering College, Chennai, Tamil Nadu, India. Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Easwari Engineering College, Chennai, Tamil Nadu, India. Department of Computer Science and Engineering, St. Joseph's Institute of Technology, Chennai, Tamil Nadu, India. School of Computer Science and Information Technology, University of Edenberg, Lusaka, Lusaka Province, Zambia. Department of Information Sciences, University of Library Studies and Information Technologies, Sofia, Sofia Province, Bulgaria. Department of Information Sciences, Duale Hochschule Baden-Württemberg, Stuttgart, Baden-Württemberg, Germany. Department of Computer Systems Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China. Department of Computer Systems Engineering, University of Engineering and Applied Sciences, Swat, Khyber Pakhtunkhwa, Pakistan.

Abstract:

This paper proposes an intelligent data engineering pipeline that enables real-time semantic integration across heterogeneous enterprise networks. This work uses an internal enterprise integration dataset researchers created, consisting of 132 cases across a range of simulated business events, application logs, service records, source metadata, and governance checkpoints. The dataset consists of various enterprise streams with different fact labels and fact representations, ownership constraints and fact time periods. The study uses Python, document engineering tools, spreadsheet processing and visualisation tools to design the pipeline, assess data quality and present results in a structured tabular format and charts. The proposed pipeline is a coordinated flow that includes ingestion, quality filtering, semantic mapping, contextual indexing and the delivery of governed actions. Its primary goal is to minimise broken interpretation, provide better reusable context, deliver a strong traceable delivery and facilitate quicker enterprise decision-making without relying on any particular source format. The results demonstrate the pipeline's ability to enhance the quality of the source, contextual linking, meaning alignment, latency readiness, exception closure, and governed release confidence in various enterprise functions. The paper presents a practical perspective, based on the paper, for organisations seeking a cleaner integration of the business worlds of sales, operations, service, finance and compliance.

Keywords: Data Engineering Pipeline; Semantic Integration; Decision-Making; Contextual Indexing; Cleaner Integration; Engineering Tools; Meaning Alignment; Practical Perspective.

Received on: 02/07/2025, Revised on: 17/09/2025, Accepted on: 02/11/2025, Published on: 15/08/2026

DOI: 10.69888/FTSIN.2026.000737

FMDB Transactions on Sustainable Intelligent Networks, 2026 Vol. 3 No. 3, Pages: 179-187

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