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With the rapid advancement of Big Data platforms such as Hadoop, Spark, andDataflow, many tools are being developed that are intended to provide end userswith an interactive environment for large-scale data analysis (e.g., IQmulus).However, there are challenges using these platforms. For example, developersfind it difficult to use these platforms when developing interactive andreusable data analytic tools. One approach to better support interactivity andreusability is the use of microlevel modularisation for computation-intensivetasks, which splits data operations into independent, composable modules.However, modularizing data and computation-intensive tasks into independentcomponents differs from traditional programming, e.g., when accessing largescale data, controlling data-flow among components, and structuring computationlogic. In this paper, we present a case study on modularizing real worldcomputationintensive tasks that investigates the impact of modularization onprocessing large scale image data. To that end, we synthesize imagedata-processing patterns and propose a unified modular model for the effectiveimplementation of computation-intensive tasks on data-parallel frameworksconsidering reproducibility, reusability, and customization. We present variousinsights of using the modularity model based on our experimental results fromrunning image processing tasks on Spark and Hadoop clusters.

Micro-level Modularity of Computaion-intensive Programs in Big Data Platforms: A Case Study with Image Data