Example for persisting metrics from metricsRepository to file on S3 and reloading the metrics file in Glue to perform anomaly detection

**Is your feature request related to a problem? Please describe.**
I am unable to leverage the full benefits of the MetricsRepository feature in PyDeequ - especially with respect to persisting the metrics to an S3 file, and reload in a subsequent job run for historical comparison and anomaly detection

**Describe the solution you'd like**
An example code snippet where DeeQu and Analyzer metrics are persisted to an S3 file, and reloaded into another job run to perform anomaly detection. Currently, the example covers persisting to JSON file, would be great to know if the metrics can be persisted in parquet files as dataframe, and then reloaded to get historical metrics repository details.

**Describe alternatives you've considered**
Currently, the workaround is to compute the metrics by partition during every job run, and then do a comparison of analyzer metrics - but this is not very efficient, especially if the number of historical partitions is large

Inizia individuando l'esempio esistente di PyDeequ che rende persistenti i dati di MetricsRepository in JSON, quindi segui il modo in cui le metriche di MetricsRepository vengono caricate per il confronto storico e il rilevamento delle anomalie. Aggiungi un esempio che includa la persistenza e il ricaricamento da S3 in un job AWS Glue, compresa la verifica del supporto per un Parquet-backed dataframe workflow; il lavoro è completato quando l'esempio è eseguibile e dimostra il rilevamento storico delle anomalie.

Scritto dal modello di indicizzazione a partire dal testo della issue.