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Mastering EDA for Demand Forecasting on AWS

This article expands on a previous post about building a serverless ETL pipeline on AWS by focusing on Exploratory Data Analysis (EDA). It details how to establish the EDA environment using AWS Glue and PySpark after cleaning the dataset. Key insights include sales trends, store and item performance, and correlation analysis, laying the groundwork for a demand forecasting model.

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Enhancing Your ETL Pipeline with AWS Glue and PySpark

The post details enhancements made to a serverless ETL pipeline using AWS Glue and PySpark for retail sales data. Improvements include explicit column type conversions, missing value imputation, normalization of sales data, and integration of logging for observability. These changes aim to create a production-ready, machine-learning-friendly preprocessing layer for effective data analysis.