A Cloud Technology and Integration Services Company - USA
How a visual, AI-assisted ETL platform was built to connect disparate ERP and CRM systems with zero cloud storage costs and drag-and-drop pipeline design.
The client needed a scalable ETL platform to handle complex data transformation between enterprise systems — but existing solutions either carried high cloud storage costs or couldn’t handle the complexity of one-to-many and many-to-one field mapping logic. We built a custom platform with a visual pipeline designer, AI-assisted field mapping, and an in-memory processing architecture that eliminates cloud storage entirely while handling enterprise-grade transformation at scale.
Business Challenge:
Integrating multiple ERP and CRM systems required transformation logic far beyond what standard ETL tools could handle out of the box. Cloud storage costs for intermediate data were unsustainable at scale. Non-technical users needed to configure pipelines without writing code, and the platform needed to scale up and down automatically based on processing load.
Key Pain Points:
- Complex one-to-many and many-to-one field transformation logic not supported by standard tools
- Cloud storage costs prohibitive for high-volume intermediate data
- No visual interface for non-technical users to configure pipeline mappings
- Manual field mapping time-consuming and error-prone for large schemas
- No built-in auto-scaling or observability for pipeline health
Solution:
We built a FastAPI backend with PySpark for distributed data processing, a React Flow canvas for visual pipeline design, and an AI-assisted field mapping engine that suggests transformations based on schema analysis. The entire architecture processes data in-memory — AWS acts purely as compute, with no intermediate storage costs. PostgreSQL stores reusable transformation configurations. AWS Step Functions, EventBridge, and auto-scaling EC2 handle scheduling and compute management, with Prometheus and Grafana providing full pipeline observability.
| Platform Layer | What It Does |
|---|---|
| Visual Canvas | React Flow drag-and-drop interface for pipeline design |
| AI Field Mapping | AI engine suggests field matches and transformation logic |
| Processing Engine | FastAPI + PySpark handles distributed in-memory transformation |
| Configuration Store | PostgreSQL stores reusable pipeline and mapping configs |
| Compute Management | Auto-scaling EC2 + Step Functions manage job execution |
| Scheduling | EventBridge triggers pipelines on defined schedules |
| Observability | Prometheus and Grafana monitor pipeline health and throughput |
Technology Stack:
Python · FastAPI · PySpark · React Flow · AWS EC2 · AWS Step Functions · AWS EventBridge · PostgreSQL · Oracle ERP Cloud REST · Prometheus · Grafana
Results:
Zero cloud storage costs through fully in-memory processing architecture Visual drag-and-drop pipeline design accessible to non-technical users AI-assisted field mapping reduces manual configuration time significantly Complex one-to-many and many-to-one transformations handled natively Auto-scaling compute adjusts capacity to processing demand automatically Full pipeline observability through integrated monitoring stack
Ready to Eliminate ETL Complexity?
What would zero storage costs and drag‑and‑drop pipeline design mean for your business?