DataForge is a Python-based ETL (Extract, Transform, Load) pipeline designed to automate the movement and processing of data from Amazon S3 into Amazon Redshift.
The project demonstrates a batch-processing architecture that extracts raw files from S3, validates and transforms incoming records, and loads clean data into Redshift.
It also incorporates fault-tolerance mechanisms, including retry logic with exponential backoff and a Dead Letter Queue (DLQ) to handle rejected records and failed batches.
Key Features:
Automated batch-based data extraction from Amazon S3.
Data validation, transformation and type coercion.
Efficient loading of processed data into Amazon Redshift.
Retry mechanism with exponential backoff for failed operations.
Dead Letter Queue (DLQ) for rejected records and failed batches.
Pipeline monitoring with extraction, loading, rejection and success metrics.
Run history and batch-level processing statistics.
Objective: Build a reliable and fault-tolerant data pipeline that simplifies data ingestion while improving data quality, error handling and observability.
0 likes · 0 views · Nov 11, 2026
Built a Python-based ETL pipeline that extracts data from Amazon S3, validates and transforms records, and loads them into Amazon Redshift with retry mechanisms
