On Building an End-To-End Prototype System for Harvesting Performance Characteristics of Code Snippets

On the one hand, in recent years, end-to-end solutions for running various data engineering experiments and getting insights from them are gaining interest from research communities. The insights are often learned from applying machine learning algorithms on experimental data. In this context, experiments repeatability and open access experimental data become new important trends. On the other hand, with the widespread of big data, integration architectures and processes are among the most frequently researched topics, as they are critical in modern data management systems, aimed at consolidating data from diverse sources to offer a unified perspective for users. In this paper we propose an end-to-end prototype system for collecting and analyzing performance characteristics of code snippets. The system was built, deployed, and tested for the problem of building performance characteristics of user defined functions.