Cross-Platform ETL Federation: A Unified Interface for Multi-Cloud Data Integration

Abstract

This article presents a novel federated ETL interface designed to address the challenges of multi-cloud data integration in modern enterprise environments. As organizations increasingly adopt multi-cloud strategies, leveraging different cloud providers for various workloads, traditional Extract, Transform, Load processes that are tightly coupled with specific vendors create data silos and operational complexity. The proposed solution introduces a unified abstraction layer that enables seamless data integration across major cloud platforms, including AWS Snowflake, Google Cloud Platform's BigQuery, and Microsoft Azure Synapse. The system employs a modular architecture with a middleware layer that utilizes standardized connectivity protocols, implements intelligent schema mapping, and provides automated conflict resolution through machine learning models. Key technical components include a Connection Manager for multi-cloud authentication, a Schema Translation Engine for real-time mapping between different data type systems, a Metadata Alignment Service for centralized schema management, and a Query Optimizer for platform-specific execution planning. Through comprehensive experimental evaluation across real-world data integration scenarios, the federated interface demonstrates significant improvements in integration efficiency, reduced operational overhead, and enhanced system performance while maintaining minimal latency and high fault tolerance. The article contributes to the advancement of vendor-agnostic data integration solutions that enable organizations to leverage the benefits of multi-cloud deployments while minimizing complexity and maintaining operational flexibility.