Thesis etd-03252026-153225 |
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Thesis type
Tesi di laurea magistrale
URN
etd-03252026-153225
Thesis title
Design and implementation of a streamset ETL pipeline supported by Generative AI
Department
INFORMATICA
Course of study
DATA SCIENCE AND BUSINESS INFORMATICS
Supervisors
.
relatore Prof. Mencagli, Gabriele
tutor Bardone, Andrea
tutor Bardone, Andrea
Keywords
- AI
- ETL Migration
- LLMs
Graduation session start date
10/04/2026
Availability
Withheld
Release date
10/04/2066
Abstract (Inglese)
This thesis presents the design and implementation of an AI-based multi-agent system for the automated analysis, documentation, and migration of legacy data processing pipelines used at Reale Mutua.
The proposed system includes an agent that analyzes IBM DataStage .dsx files to generate structured JSON documentation and a second agent that converts this information into equivalent Python code for migration to modern environments.
Additional agents support legacy modernization by generating Python code from technical documentation and extracting structured information from legacy Java insurance product logic.
The results show that AI-driven agents can significantly reduce manual effort, improve maintainability, and facilitate the transition to modern data engineering architectures.
The proposed system includes an agent that analyzes IBM DataStage .dsx files to generate structured JSON documentation and a second agent that converts this information into equivalent Python code for migration to modern environments.
Additional agents support legacy modernization by generating Python code from technical documentation and extracting structured information from legacy Java insurance product logic.
The results show that AI-driven agents can significantly reduce manual effort, improve maintainability, and facilitate the transition to modern data engineering architectures.
Abstract (Italiano)
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