Comparative Performance Analysis of Data Processing Technologies: Google App Script and Distributed Frameworks for Varying Data Volumes

While distributed frameworks like Apache Spark, Apache Flink, and Dask are frequently benchmarked against one another, their performance relative to widely accessible scripting platforms such as Google Apps Script remains under-quantified, particularly across a wide spectrum of data volumes. This paper addresses this critical gap by conducting a comparative performance analysis focusing on batch operations (reading, transforming, writing) across datasets ranging from 10 to 1,000,000 records. Our empirical evaluation demonstrates that Google Apps Script exhibits severe scalability limitations, with execution times increasing dramatically and ultimately failing for datasets exceeding 100,000 records. Conversely, Spark, Flink, and Dask consistently deliver significantly faster processing and superior scalability. Although these distributed frameworks incur a minor initial overhead for the smallest datasets, their parallel processing capabilities ensure minimal execution times for mid-range volumes. At the 1,000,000-record mark, I/O operations—specifically data writing—emerge as the dominant performance bottleneck for all distributed frameworks. This research provides novel empirical evidence that quantifies the performance cliff between simple scripting tools and distributed systems, underscoring that while Google Apps Script is suitable for small-scale tasks, distributed frameworks are essential for scalable batch processing in modern data-intensive environments.