The feature of Google Cloud BigQuery that significantly enhances its performance for big data analysis is its serverless architecture. In a serverless setup, users do not need to provision or manage servers. This allows BigQuery to automatically scale resources up or down based on the workload, which is particularly beneficial for analyzing large volumes of data.
Serverless architecture simplifies the process of data analysis; users can query large datasets without worrying about the underlying infrastructure or performance bottlenecks. This leads to faster query execution times, as BigQuery can dynamically allocate resources needed to process data efficiently. Additionally, it reduces operational costs since users pay for the actual resources consumed during the analyses rather than maintaining unused capacity.
In contrast, features like integration with IoT devices and real-time data streaming capabilities provide functionalities that are valuable in specific contexts, but they do not directly relate to the underlying performance enhancements that the serverless model offers for big data analytics. Automatic data redundancy contributes to data safety and availability, but again, it does not intrinsically enhance performance for data analysis tasks in the same way that a serverless architecture does.