AWS Big Data Blog

Shubham Kumar

Author: Shubham Kumar

This architecture diagram illustrates a comprehensive, end-to-end data processing pipeline built on AWS services, orchestrated through Amazon SageMaker Unified Studio. The pipeline demonstrates best practices for data ingestion, transformation, quality validation, advanced processing, and analytics.

Orchestrate end-to-end scalable ETL pipeline with Amazon SageMaker workflows

This post explores how to build and manage a comprehensive extract, transform, and load (ETL) pipeline using SageMaker Unified Studio workflows through a code-based approach. We demonstrate how to use a single, integrated interface to handle all aspects of data processing, from preparation to orchestration, by using AWS services including Amazon EMR, AWS Glue, Amazon Redshift, and Amazon MWAA. This solution streamlines the data pipeline through a single UI.