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

To get a sense of the data, let's draw a stacked bar chart by status:



The chart shows a period of 12 hours. It looks like we have a nice trend with two peaks at around 09:30 and again at 18:00.

We can notice right away that at around 11:30, there was a significant increase in 500 status code errors. The burst died down after around 10 minutes. This is the type of anomalies we want to identify early on.

It is entirely possible that there were other problems during that time; we can't spot them with a naked eye.

Understand the data set provided in the previous lesson. 

Getting a Sense of the Data

Understand the data set provided in the previous lesson.