Relational databases are the backbone of many applications and the primary choice for data storage in various sectors. However, accessing this data for analytics presents challenges. Often, relational databases are not suited for analytical use cases, and extracting data in bulk can be both costly and resource-intensive, making it nearly impossible for data teams to access. Utilizing a change data capture approach to stream data into Apache Iceberg can provide analytics with low-latency data at a relatively low cost. Additionally, by creating a general-purpose data platform to which other disparate sources can be synchronized, it becomes possible to leverage all data collectively. This results in a data platform that is up to date with the application in near real-time, capable of supporting a wide range of backend analytics, reporting, and ETL use cases without overburdening or increasing the load on the RDBMS that serves customers.