With the recent success of ChatGPT, we can observe another wave of interest in the AI field and machine learning in general. The previous wave of interest in this field was, at least to a certain extent, caused by the fact that excellent ML frameworks like TensorFlow, PyTorch or general data processing frameworks like Spark became available and made the writing of ML models much more straightforward. Since that time, these frameworks have matured, and writing models are even more accessible, as you will see later in this blog. However, data set preparation and gathering data from various sources can sometimes take time and effort. Creating a complete pipeline that would pull existing or newly created data, adjust it, and ingest it into selected ML libraries can be challenging. Let’s investigate if Debezium can help with this task and explore how we can leverage Debezium’s capabilities to make it easier.