What is — Incremental Data Processing ?

In simple words, Incremental data processing is a method of handling data updates or changes by only processing the new or modified data since the last processing cycle. Rather than reprocessing the entire dataset, incremental processing focuses on the delta, the changes that have occurred, and updates the dataset accordingly. This approach is particularly useful when dealing with large volumes of data, as it can save computational resources and processing time.

Key characteristics of incremental data processing include:

Efficiency: Incremental processing is more efficient than reprocessing the entire dataset, especially when dealing with large volumes of data. By identifying and processing only the changes, it reduces the computational load and processing time.

Change Data Capture (CDC): Change Data Capture is often employed in incremental data processing. It involves capturing and identifying changes in the data source, such as inserts, updates, or deletes. This allows for the selective processing of only the modified data.

Real-time and Streaming Data: Incremental processing is commonly used in real-time and streaming data scenarios. In these cases, data is processed as it arrives, and incremental updates are applied continuously. This is crucial for applications that require up-to-date…