Building Serverless Predictive Analytics on AWS for EV Charging Industry

In this session, we’ll explore how to design and deploy a scalable, serverless analytics pipeline on AWS that supports everything from business dashboards to advanced predictive insights. By leveraging a range of AWS services, we will demonstrate how to build a flexible data architecture that adheres to the AWS Well-Architected Framework. From data ingestion and transformation to data storage, querying, business intelligence, and advanced analytics, we’ll cover the entire process, ensuring a solution that can handle real-time and batch analytics while integrating predictive insights.

As a practical example, we will focus on the electric vehicle (EV) charging station industry - showing how data from charging infrastructure can be used to forecast demand, optimize station utilization, and improve operational efficiency.

Listeners don’t need to have a background in data engineering or analytics to follow along. However, this session will also be valuable for professionals looking to enhance their AWS data analytics skills. It’s designed to show how approachable, flexible, and powerful AWS analytics tools are, even for those just starting.

The session includes a hands-on, step-by-step walkthrough of an end-to-end deployment, demonstrating how to transform raw data into actionable insights using AI/ML tools. By the end of the session, you’ll be equipped to build production-ready solutions that deliver real business value - whether you’re creating basic dashboards for a start-up or developing sophisticated, machine learning-driven insights for a corporate environment.

Keywords: Amazon S3, AWS Glue, Lake Formation, Redshift, Athena, QuickSight, SageMaker, Data Analytics, AI/ML, Serverless, Data Pipeline, Business Intelligence, Predictive Insights, EV Charging Stations.