Modern Data Engineering Architecture Across AWS, GCP, and Azure

In modern data platforms, organizations build end-to-end data pipelines to collect, process, store, and analyze large volumes of data . Although different cloud providers offer different services, the core architecture pattern remains the same . A typical data engineering architecture contains the following stages: Data Generation Data Ingestion Data Processing Data Lake Storage SQL Query Layer Data Warehouse Analytics Business Intelligence Visualization End-to-End Data Pipeline Architecture The diagram above represents a typical enterprise data pipeline architecture used by modern companies. The goal of this architecture is to move data from operational systems into analytics platforms where it can generate business insights . Cloud Data Engineering Architecture Comparison Architecture Layer What Happens in This Layer AWS Implementation GCP Implementation Azure Implementation 1. Data Sources Data is generated from applications, IoT devices, databases, logs, and user transactions . App