Extension and improvement of Data Quality and Observability framework through DBT

The growing complexity and volume of data in modern business intelligence architectures requires increasing attention to data quality and traceability. In this context, improved data monitoring and validation processes are essential to ensure the reliability and correctness of analyses. This thesis explores the extension of Alertable, an existing platform focused on data quality and observability, by integrating it with DBT (Data Build Tool), one of the most popular tools for data management and transformation within data engineering pipelines. The main objective of this work is to develop new functionalities that improve Alertable's ability to monitor and validate data through the integration of advanced data quality tests, allowing anomalies and discrepancies in data flows to be automatically detected. The proposed extension involves the creation of a framework to perform quality tests directly in DBT or eventually convert DBT tests into the format used by Alertable, leveraging Alertable's data validation capabilities.