This project implements Autoencoder (AE) models using PySpark and Keras for anomaly detection using dataset stored in a Data Lakehouse. The goal is to preprocess the data stored as Iceberg tables on AWS S3 bucket (cataloged using Glue Catalog), train an AE or LSTM Autoencoder, and evaluate its performance in identifying anomalies based on MAE.