Serverless architecture for analytics: Transforming data processing in the cloud era

This article explores serverless architecture as a transformative approach to analytics in cloud computing environments. It examines how serverless models have restructured the economics of cloud computing by eliminating idle capacity concerns and implementing consumption-based pricing. It details the four key components of a serverless analytics pipeline: data ingestion, processing, storage, and visualization layers, highlighting how each contributes to efficient data handling. It further discusses implementation best practices, focusing on function design, data flow optimization, and scalability management strategies. Special attention is given to advanced tenancy approaches, resource optimization techniques, and resilience engineering patterns that enhance reliability. The article concludes by examining real-world applications across IoT data processing, real-time customer analytics, financial transaction analysis, and log monitoring domains, demonstrating how serverless architectures provide superior performance, cost-efficiency, and scalability compared to traditional approaches.