Blueprint: An Intelligent ETL Data Processing Platform Based on RAG Architecture

Blueprint is a high-performance cloud-native backend system built with Spring Boot. Its core innovation lies in the deep integration of traditional ETL processes with generative AI. Through the RAG architecture, it enables automatic conversion from natural language queries to SQL, providing intelligent analysis capabilities for large-scale telecom bill data, allowing business personnel to gain data insights without SQL skills.

In response to the needs of large-scale telecom bill data processing, traditional ETL processes have the pain point that business personnel need to master complex SQL syntax. The project goal is to build an intelligent analysis platform that can understand business context and answer natural language queries, seamlessly integrating modern AI capabilities into traditional enterprise data processes.

RAG-Driven Intelligent Querying

Integrates the Google Gemini model to convert natural language questions into validated PostgreSQL queries (e.g., "Which region had the fastest average bill growth in the past three months?").

Complete ETL Workflow

Supports CSV data ingestion: Extraction (parallel CSV reading) → Cleaning (handling formatting/missing values) → Transformation (mapping standardized entities) → Loading (bulk insertion into PostgreSQL).

Event-Driven High Concurrency

S3 file uploads trigger SQS messages to start processing; S3 stores files and intermediate results; elastic scaling is achieved via AWS ECS containerized deployment.

Technology Stack Selection

Layer	Technology Selection	Reason for Selection
Programming Language	Java 21	Latest LTS version, performance optimizations and syntax improvements
Application Framework	Spring Boot	Mature ecosystem, dependency injection and AOP support
AI Capability	Google Gemini GenAI	Strong code and natural language understanding
Cloud Services	AWS (ECS, RDS, SQS, SNS, S3)	Enterprise-level cloud-native service suite
Database	PostgreSQL	Powerful SQL and JSON processing capabilities
Deployment	Docker	Consistent runtime environment, simplified operation and maintenance
Testing	JUnit5 & Mockito	Unit testing and mocking framework

Practical Application

Deployed to production environment (https://telecom.jawadazeem.com), addressing core pain points: lowering technical barriers (business personnel can query independently), ensuring data accuracy (RAG avoids hallucinations + SQL validation), and supporting real-time analysis (event-driven minute-level data processing).

Highlights of Architecture Design

Adopts layered design: Access Layer (REST API with authentication and rate limiting) → Business Layer (Spring Boot handles ETL and AI interactions) → Data Layer (PostgreSQL + S3) → Messaging Layer (SQS/SNS for asynchronous decoupling) → AI Layer (Gemini), ensuring scalability and high availability.

Project Value

Represents the direction of enterprise data processing: combining traditional ETL with generative AI to unlock data value, serving as an excellent reference case for enterprise AI transformation.

Insights for developers:

1. Progressive AI integration: No need to rewrite the system; introduce AI capabilities gradually;
2. RAG architecture practice: Apply retrieval-augmented generation to structured data query scenarios;
3. Cloud-native best practices: Modern application development methodologies such as containerization + event-driven approaches.