As organizations generate more data than ever, traditional ETL workflows often slow down decision making and create fragile systems that fail to scale. By applying AI techniques to cloud native data platforms, we can now design pipelines that are self healing, reliable, and capable of delivering insights in real time.

In this session, I will share how I have helped enterprises modernize their data engineering practices using AI powered automation in the cloud. I will discuss how predictive monitoring and anomaly detection reduce downtime, how orchestration frameworks improve reliability, and how platforms like Snowflake and dbt integrate to create cost efficient architectures.

Attendees will walk away with practical strategies for rethinking ETL through AI driven cloud data pipelines that improve scalability, reduce costs, and accelerate analytics delivery.