Healthcare data is often complex, sparse, and frequently updated, which makes standardization essential. The OMOP Common Data Model (CDM) is gaining increasing attention because of its ability to standardize healthcare data and enable consistent and reproducible research outputs. This paper evaluates three ETL (Extract, Transform, Load) tools: Pentaho, Apache NiFi, and a custom-built tool to transform healthcare data into the OMOP CDM. The evaluation criteria, based on a literature review, include connectivity and interoperability, user interface and ease of use, performance, and technical flexibility. Results indicate that while minor differences in the tools’ features exist, all tools are adequate for basic transformation tasks. Pentaho is noted for its ease of use, particularly helpful for non-expert users, while Apache NiFi is better suited for more advanced users. A custom-built tool, though often offering user-friendly interfaces and more flexibility, comes at the cost of additional development efforts.