RapidCanvas ETL and ML Pipeline Architecture
About This Architecture
End-to-end ML pipeline architecture on AWS using RapidCanvas for data ingestion, transformation, model training, and serving predictions. CSV files land in S3, flow through RapidCanvas S3 Connector into Recipe ETL pipelines that load MySQL Database, which feeds both Backend API REST services and ML Recipe Training workflows generating ML Projections. React-based DataApps Frontend consumes Backend API to deliver predictions to Users, demonstrating a complete data-to-insight workflow. This architecture solves the challenge of orchestrating ETL and ML training in a unified platform while maintaining separation between data storage, model training, and application layers. Fork this diagram on Diagrams.so to customize connectors, swap MySQL for Redshift or Snowflake, or add real-time streaming components.
People also ask
How do I design an end-to-end ETL and ML pipeline on AWS using RapidCanvas?
This diagram shows CSV files ingested into S3, processed by RapidCanvas S3 Connector and Recipe ETL pipelines into MySQL, which feeds ML Recipe Training for ML Projections and Backend API serving React DataApps to Users—a complete data-to-insight architecture on AWS.
- Domain:
- Ml Pipeline
- Audience:
- data engineers and ML engineers building end-to-end data pipelines on AWS
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