SERVERLESS DATA ENGINEERING WITH AWS LAMBDA AND STEP FUNCTIONS FOR REAL-TIME ANALYTICS

Abstract

The ongoing trend, coupled with the challenges of managing large real-time data streams, has
transformed modern data engineering by creating the need for systems that are scalable, costeffective, and highly responsive. Furthermore, the inability to effectively handle dynamic
workloads undermines the advantages of infrastructure-driven approaches, often resulting in
inefficient resource utilization and delays. Another key element of the paradigm shift is that
AWS Lambda and Step Functions are event-driven, elastic, and highly modular, enabling the
construction of data pipelines that support real-time analysis. In this paper, the principles of
serverless architecture will be presented, where data engineering is guided by these
ideologies. AWS Lambda is highlighted as the central processing core, while Step Functions
serve as the orchestration core for managing complex workflows. It discusses intrinsic
issues, including execution limits, cold starts, monitoring complexity, provider lock-in, and
compliance, but also reports deployed applications in finance, IoT, health care,
cybersecurity, and personalisation. The future of serverless analytics will be
characterized by machine learning, hybrid architectures, edge computing, and enhanced
governance frameworks in the future. The objective planning background of the research,
which is furthermore factual, is the study that claims that when planned and optimized, both
AWS Lambda and Step Functions provide the agility and scalability that organizations
demand to make real-time decisions in the modern enterprise.