Transformation

The heart of my data pipeline resided in the transformation logic. I wrote Python scripts (or potentially AWS Glue jobs) to perform the following key tasks:

Data Validation: Ensuring data accuracy and integrity by checking for missing values, outliers, or format inconsistencies. Data Cleaning: Removing duplicates, correcting errors, and standardizing formats. Data Enrichment: Joining data with external datasets, adding calculated fields, or deriving new insights. Data Transformation: Restructuring the data into a format suitable for analysis or reporting. I followed best practices for modularity, readability, and maintainability. I also implemented robust error handling and logging mechanisms to facilitate debugging and troubleshooting.