What types of anomalies are monitored in data observability solutions?

What types of anomalies are monitored in data observability solutions?
Data observability solutions detect anomalies using machine learning to understand how data typically behaves and then sends alerts when that behavior is outside the norm.
There are four types of data anomalies that are detected by this real-time monitoring.
- The first is data freshness: did the data arrive when it was supposed to?
- The second is data volume: did you receive too many rows or too few rows?
- The third is data schema: did the data’s structure change that will cause problems downstream?
These first three types of anomalies are anomalies in the behavior of your pipeline, but the data flowing through it can go bad as well.
That’s where the fourth type of anomaly, data quality, comes into play.
This monitor checks for data accuracy, such as if values are outside of the normal range, or validity, such as the NULL rates or % uniques, and much more.
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