Qlik Replicate uses parallel threading to process Big Data loads, making it a viable candidate for Big Data analytics and integrations. But when the process relies on bulk loading of the entire source database into the target system, it eats up a lot of system resources, making ETL occasionally impractical particularly for large datasets. This requires a fraction of the resources needed for full data batching. Delta-based Change Data Capture: This is a way of doing audit column-style CDC by computing incremental delta snapshots using a timestamp column in the table, Arcion is able to track modifications and convert that to operations in target. Capturing data changes - why log based CDC wins hands down Change data capture can't be enabled on tables with a clustered columnstore index. This opens the door to high-volume data transfers to the analytics target. How change data capture lets data teams do more with less CDC technology lets users apply changes downstream, throughout the enterprise. With an intuitive development environment, users can easily design, develop, and deploy processes for database conversion, data warehouse loading, real-time data synchronization, or any other integration project. Change data capture (CDC) is a process that captures changes made in a database, and ensures that those changes are replicated to a destination such as a data warehouse. Changes are captured without making application-level changes and without having to scan operational tables, both of which add additional workload and reduce source systems performance, The simplest method to extract incremental data with CDC, At least one timestamp field is required for implementing timestamp-based CDC, The timestamp column should be changed every time there is a change in a row, There may be issues with the integrity of the data in this method. When data is time-sensitive, its value to the business quickly expires. The capture process is also used to maintain history on the DDL changes to tracked tables. The scheduler runs capture and cleanup automatically within SQL Database, without any external dependency for reliability or performance. Applies to: However, below is some more general guidance, based on performance tests ran on TPCC workload: Consider increasing the number of vCores or shift to a higher database tier (for example, Hyperscale) to ensure the same performance level as before CDC was enabled on your Azure SQL Database.