This paper presents a detailed framework for extracting process events from database management systems (DBMSs) for process analytics purposes. We analyze the standard auditing features of Oracle 12c and PostgreSQL 16 and evaluate their suitability for process mining applications. Based on this analysis, we propose optimized configurations for technological event collection. Four distinct methods of event collection are discussed in detail: interacting with audit files using log collectors, modifying custom procedures with HTTP request integration, implementing proxy servers between the DBMS and clients, and directly querying service tables. The practical implementation of each method is demonstrated, highlighting its respective advantages and disadvantages in terms of data comprehensiveness, system performance, and integration complexity. Experimental results indicate that a modular approach combining multiple collection methods offers the most robust solution. For process mining performing, our framework enables organizations to capture, normalize, and aggregate database events in an efficient manner, facilitating deeper insights into operational processes while maintaining system performance. The proposed solutions address key challenges in the collection of technical logs and provide a scalable architecture that supports advanced process analysis techniques.