The migration of data-intensive analytics applications to cloud-native environments promises enhanced scalability and flexibility but introduces complex cost models that pose new challenges to traditional optimization strategies. While on-premises setups focused on speed, cloud deployments require a more nuanced approach, factoring in cloud storage operations costs, which can escalate rapidly in real-world scenarios.

In this presentation, Bin will analyze these challenges through a case study on Uber's large deployment analytics SQL platform on HDFS and GCS. They will show their findings of unexpected cost implications with standard I/O optimizations like table scans, filters, and broadcast joins when implemented in cloud environments. He will also highlight the need for a paradigm shift in optimizing data-intensive applications for the cloud and advocate for developing new I/O strategies, balancing performance and costs while tailored to cloud ecosystems' unique demands.