An organization supplies electric car chargers in a nationwide network. They store customer and charging data in BigQuery. They want to efficiently upgrade and maintain their network to reduce waste…

Identity and Access Management controls who can access data, which is a security concern and does not help optimize network maintenance or sustainability.

Creating an API via Apigee exposes data to third-party developers but does not generate predictions or insights about maintenance needs.

BigQuery ML allows data analysts to create and train machine learning models using standard SQL within BigQuery, without needing to export data or use a separate ML platform. A predictive maintenance model built on historical charging patterns and failure data can forecast which chargers require service, enabling proactive scheduling that minimizes downtime and unnecessary maintenance runs. This directly addresses the goals of reducing waste and improving sustainability.

Moving data from BigQuery to Cloud Storage may reduce storage costs but does not provide any analytical or predictive capability for maintenance scheduling.