The digital universe is doubling in size every two years, with an expected 44 zettabytes of data by 2020. To discover and use patterns and connections in data for business purposes, graph databases are increasingly used. The authors have been dealing with census data for over 15 years and were initially hesitant to adopt a graph database concept due to complexity concerns. However, they found that representing citizen data as a graph database was the perfect solution for scenarios such as providing comprehensive and online real-time ancestral trees, determining heirs and calculating heritage shares, identifying old/helpless citizens without any relatives living nearby, and constructing domestic/international migration routes, investigating causes and discussing consequences. The authors faced challenges in data modeling, export, and import, including mutual relations, birthdate info representation, and special characters. They also discussed the importance of change data capture mechanisms to keep RDBMS and graph database synchronized. To address these issues, they developed a generic ETL solution that transforms and continuously synchronizes RDBMS data to a graph database, consisting of a visual design tool, an ETL execution engine, and APIs for development.