AWS Data Pipeline - DMS to EMR Analytics

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About This Architecture

AWS Data Pipeline using DMS to EMR Analytics demonstrates a modern data integration architecture that ingests heterogeneous source databases (MySQL, MSSQL) via AWS Database Migration Service into S3 raw storage. EMR Cluster processes this data, writing optimized Hudi-formatted tables back to S3 while simultaneously populating RDS (Postgres) and DynamoDB for operational queries. Athena enables SQL analytics directly on Hudi data in S3, eliminating the need for separate data warehouses. This pattern solves the challenge of consolidating multi-source databases into a unified, cost-effective analytics platform while maintaining both batch and real-time query capabilities. Fork this diagram on Diagrams.so to customize source systems, add Glue jobs, or integrate Redshift for your specific use case.

People also ask

How do I build an AWS data pipeline that ingests multiple databases and enables analytics with EMR and 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 processes this data into Hudi-optimized tables, and Athena queries the results directly from S3. The pipeline also populates RDS (Postgres) and DynamoDB for operational use cases, creating a unified analytics platform.

AWS Data Pipeline - DMS to EMR Analytics

AWSadvanceddata-engineeringETLEMRDMSdata-pipeline
Domain: Data EngineeringAudience: Data engineers building cloud-native ETL pipelines on AWS
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Created by

March 27, 2026

Updated

March 27, 2026 at 9:43 PM

Type

architecture

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