Building Expertise in Data Engineering for AI Applications: A Comprehensive Guide

Abstract

Data engineering has evolved significantly with the integration of artificial intelligence in the financial sector, demanding robust infrastructures and sophisticated practices. This comprehensive guide explores the essential competencies, tools, and best practices required for modern data engineers to excel in AI-driven financial systems. It details the transformation from traditional batch processing to real-time streaming architectures, examining distributed computing solutions, cloud-native implementations, and quality assurance frameworks. The guide addresses critical aspects of system architecture, security protocols, and compliance requirements while highlighting emerging trends in stream processing, edge computing, and automation technologies that shape the future of data engineering.