AWS Data Pipeline - DMS to EMR Analytics

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About This Architecture

AWS Data Pipeline using DMS to EMR analytics ingests data from MySQL and MSSQL source databases via AWS Database Migration Service into S3 raw storage. EMR Cluster processes raw data and writes optimized Hudi-formatted tables back to S3, while simultaneously loading transformed data into RDS PostgreSQL and DynamoDB for operational queries. Athena queries the Hudi layer for analytics, enabling cost-effective, scalable data warehousing without managing infrastructure. Fork this diagram to customize source connectors, EMR configurations, or destination targets for your multi-source analytics workload. This pattern demonstrates best practices for heterogeneous database consolidation and separation of raw, processed, and analytics layers.

People also ask

How do I build a scalable AWS data pipeline that ingests from multiple databases, processes with EMR, and enables analytics with Athena?

This diagram shows a complete AWS data pipeline: AWS DMS migrates data from MySQL and MSSQL into S3 raw storage, EMR Cluster transforms and optimizes data into Hudi format, and Athena queries the processed layer for analytics. RDS and DynamoDB receive transformed data for operational use, enabling separation of analytical and transactional workloads.

AWS Data Pipeline - DMS to EMR Analytics

AWSadvanceddata-engineeringEMRDMSETLanalytics
Domain: Data EngineeringAudience: Data engineers building AWS ETL pipelines with DMS and EMR
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Created by

March 27, 2026

Updated

March 27, 2026 at 9:42 PM

Type

architecture

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