1. Create a Virtual Dataset

2. Define an anomaly

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3. Execute Dataset SQL

4. Generate Sub dataframes

datasetDf[datasetDf[dimensionCol] == dimVal][[timestampCol, metricCol]]

5. Aggregate Sub dataframes

"df": aggregateDf(tempDf, timestampCol)

6. Generate…

Continue reading: https://towardsdatascience.com/running-timeseries-anomaly-detection-at-scale-on-sql-data-4407eb3d3bd3?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com