Unlock the Secret: 90% Faster Standard Deviation Calculation with SQL

Calculating standard deviation on large datasets can be computationally intensive. Traditional methods require scanning the entire dataset each time new data is added, which becomes inefficient as data grows. However, a new approach using incremental aggregation in SQL with dbt can reduce this computational load by up to 90%. This method involves maintaining a previous state and updating it with new data, avoiding the need to recalculate from scratch. The formula for incremental standard deviation computation breaks down into three parts: the existing set’s weighted variance, the new set’s weighted variance, and the mean difference variance. This allows for efficient updates by retaining and combining the count, average, and variance of both existing and new data sets. The implementation in dbt involves setting up an incremental model that aggregates user transaction data, ensuring that only new transactions are processed, thus significantly reducing the computational overhead. This technique not only speeds up the process but also scales effectively for real-time data aggregation.

Source: towardsdatascience.com