Learn to identify and address irregularities in data using SQL. This module covers detecting anomalies, backtesting techniques, and server log analysis to improve your database's efficiency.

Become a Database Professional with SQL

In this module, we’ll focus on detecting anomalies in data using SQL. We’ll explore how to apply mathematical concepts like mean, standard deviation, and z-scores to identify anomalies. Additionally, we’ll delve into backtesting techniques to investigate past data and refine our detection models. We’ll also cover server log analysis to improve and optimize the overall efficiency and scalability of databases.

Anomaly Detection

In the process so far, we’ve used several constants in your calculations:

- Lookback period: How far back we calculate the mean and standard deviation for each status code. The value we used is 60 minutes.
- Entries Threshold: The least amount of entries we want to get an alert for. The value we used is 10.
- Z-Score Threshold: The z-score after which we classify the value as an anomaly. The value we used is 6.


Learn how using different values impacts the outcome of the calculations.

Detecting Anomalies

Analyzing A Server Log

Backtesting

Improving Accuracy

Conclusion

Experiment with Different Values