dbt introduces incremental standard deviation calculation for efficient SQL data processing

SQL aggregation functions can be slow with large datasets. To improve efficiency, incremental aggregation updates metrics like standard deviation without recalculating from scratch. This method combines existing data with new data, streamlining the process. The article details a dbt SQL implementation for calculating incremental standard deviation using a transactions table. It explains how to set up an incremental model that updates user transaction statistics without scanning all historical data. By leveraging mathematical techniques, the approach allows for real-time data aggregation. This results in faster processing and better scalability for large datasets, making it easier to handle updates efficiently.