Change Data Capture (CDC) tools play a pivotal role in tracking and managing alterations to databases. Most of them become crucial components in the realm of data management,providing a systematic approach to identify and capture changes made to data over and across databases. However, the correctness of CDC tools is not guaranteed like DBMS, and the bugs in CDC tools may lead to severe consequences. Existing techniques such as fuzzing and differential testing are generally used to test DBMS. Approaches to find logic bugs, such as when a DBMS computes an incorrect result set, lead to automatically generate tools like SQLancer. SQLancer has been used to find bugs in a range of widely-used, production-quality DBMS including SQLite, MySQL, PostgreSQL. However, these techniques are not well used in finding bugs inside CDC tools. The key conceptual challenge was to tackle the test oracle and test-case generation problems together with heterogeneous databases connected by CDC tools. In this talk, I will introduce the study of testing CDC tools via SQLancer-CDC, a tool for automatic testing of database management systems together with change data capture tool chain. The primary findings of this tool show two schema mismatch error messages within the MySQL and Flink CDC tool.