2026-04-17T03:03:38Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4450422025-10-31T01:33:22Zcom_2072_1033col_2072_452951

00925njm 22002777a 4500 dc Masip Plasencia, Eduard author 2025-10-23 This thesis addresses the architectural limitations of Tauniqo, a growing SaaS company, whose purely transactional (OLTP) infrastructure impeded critical data analysis. This created severe business blind spots, including reactive customer management, uncontrolled AI operational costs, and an engineering team bottlenecked by manual data requests. The system's normalized schema was unsuited for analytics, leading to query times exceeding 250 seconds and risking the stability of the user-facing application. To solve this, a dimensional Data Warehouse (DW) was designed and implemented, chosen for its ability to provide complete performance isolation and empower non-technical users with self-service analytics. This V1.0 platform was built pragmatically using PostgreSQL, Python/Pandas, and Metabase to accelerate time-to-value. The project delivered three key outcomes: Enabled Data-Driven Customer Success: An automated dashboard reduced query times for key KPIs to under 3 seconds, shifting the CS team from a reactive to a proactive, data-informed approach. Established an AI FinOps Framework: The platform provided first-ever visibility into 78% of the company's AI costs, enabling data-driven optimization of LLM usage. Created a Strategic AI Dataset: An analysis-ready "ground truth" dataset was engineered, providing a foundational asset for systematic LLMOps experimentation to improve AI evaluation accuracy and cost-effectiveness. The core conclusion is that the primary performance bottleneck was the logical data model, not the underlying hardware. By successfully delivering immediate business value and fostering a data-informed culture, the project provides a validated foundation and a clear business case for a V2.0 roadmap to evolve towards a more scalable, enterprise-grade architecture. http://hdl.handle.net/2117/445042 Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació::Bases de dades Multidimensional databases Business intelligence Data warehouse Business intelligence ETL OLTP OLAP Dimensional modeling Data pipeline SaaS Performance optimization Self-service analytics FinOps AI LLM Cost observability Data governance Customer success Scalability Magatzem de dades Intel·ligència de negoci Analítica d'autoservei IA Governança de dades Èxit del client Escalabilitat Escalabilitat Escalabilitat Escalabilitat Escalabilitat Bases de dades multidimensionals Intel·ligència de negoci Data-driven SaaS: architecting a dimensional data warehouse for BI and MLOps at Tauniqo