Driven by the rising need for scalable, accessible, and interdisciplinary structure/unstructured
data infrastructure in university education, this paper presents the development of a campus-scale Data
Lakehouse at Lamar University (Texas), designed to bridge important gaps in existing study around
unified data platforms for both learner and instruction. Built on an S3-compatible MinIO object store,
the Lamar University Data Lakehouse addresses four key challenges: (1) limited access and governance
for diverse users such as students, staff, and faculty, (2) fragmented technology stacks, (3) inconsistent
structure/unstructured dataset organization and retrieval, and (4) weak integration with academic workflows.
These problems have been tackled through the deployment of a web-based upload portal and a Python
utility, enabling seamless data ingestion, processing, and retrieval. Early implementations—such as a water
system monitoring pipeline for forecasting and a CMMS (Computerized Maintenance Management System)
workflow for operational analytics—demonstrate the Lakehouse’s dual value in research and teaching.
Findings and outcomes highlight the effectiveness of accessible interfaces and scalable practices in fostering
data-driven processing, with broader implications for replicability across institutions seeking to enhance
curriculum integration, research, hands-on experience, and institutional data strategy.