Agentic AI-Powered Data Engineering Accelerator

Agentic AI-Powered Data Engineering accelerator, built on AWS, is a cloud-native solution that transforms traditional, manual data engineering into an automated, agent-driven process. It leverages large language models (LLMs) and modular agents to streamline schema mapping, ETL logic generation, data quality validation, and pipeline orchestration.

This platform addresses key challenges in legacy data modernization, multi-source integration, and regulatory compliance - enabling faster, more accurate, and cost-effective data product delivery.

Core Modules

• Source-Target Analyzer Agent - Extracts metadata and suggests intelligent schema mappings.

• Mapping Agent - Auto-generates source-target column mappings using semantic and contextual analysis.

• Transformation Code Generator - Produces SQL/Python ETL scripts with complex logic handling.

• Data Quality Rule Generator - Creates validation rules for null checks, range checks, and more.

• Validation & Scoring Agent - Compares transformed data against samples and assigns quality scores.

• Orchestration Engine - Coordinates agent workflows with error handling and UI-based control.

Key Benefits

• Accelerated Data Pipeline Creation

Automates schema mapping, transformation logic, and validation - reducing delivery timelines by up to 60% compared to traditional methods.

• Reduced Engineering Costs

Minimizes manual intervention and SME dependency, enabling up to 40% cost savings in data engineering efforts.

• Trustworthy Data Outputs

Score-based validation ensures accuracy, consistency, and compliance, improving data quality confidence by 3×.

• Scalable & Modular Architecture

Agent-based design supports multi-tenant environments and reusable logic across domains, reducing onboarding time by up to 50%.

• Rapid Prototyping & Experimentation

Enables instant code generation for fast POCs and iterative development, cutting prototyping cycles from weeks to days.

• Governed Self-Service Enablement

Empowers analysts with explainable logic and built-in guardrails, reducing IT dependency by up to 70%.

Real-World Use Cases

• Legacy System Modernization: LLM agents automate schema mapping and ETL logic, reducing transformation errors and manual effort.

• Accelerating New Data Product Launches: The platform rapidly integrates diverse data sources by auto-generating transformation logic and data quality rules.

• M&A Data Integration: Semantic-aware agents harmonize inconsistent schemas across systems, streamlining post-merger data alignment.

• Governed Self-Service Data Engineering: Analysts gain autonomy with built-in guardrails and explainable logic, reducing reliance on IT teams.

• Regulatory Compliance & Data Quality Validation: Automatically generates validation rules and scores data quality to meet compliance standards efficiently.

• Rapid Prototyping & Data Experimentation: Enables fast iteration with instant code generation and validation for proof-of-concept development.

• Multi-Tenant Data Product Enablement: Adaptable agents learn schema variations and reuse logic, simplifying onboarding across multiple clients.

Key AWS Components

Amazon S3, AWS Glue, Amazon Redshift Serverless, Amazon Kinesis, Amazon Athena, Amazon Bedrock, Amazon DataZone, AWS Lake Formation, Amazon EventBridge, Amazon CloudWatch.