Data Engineering for the Factory of the Future, Multimedia Applications and Cyber-Physical Systems

Abstract

This chapter is the companion chapter to “Part 1: State-of-the-Art Time-Series Data Formats Performance Evaluation.” In this chapter, algorithms for converting data from one format to other formats are presented. To implement the algorithms, existing open-source Python libraries are used extensively, and where needed, new Python routines for converting data formats are developed. It is envisaged that the algorithms and Python libraries and routines that are freely provided in this chapter will be useful for data engineers, data scientists, and for industrial IoT, cyber-physical systems (CPS), multimedia, and big data practitioners who are on the quest to use different types of data formats that are compatible with memory-constrained factory floor IoT devices. It will also be useful for Delta Lake and big data engineers, who are on the quest for delivering robust bronze, silver, and gold data lakes in the cloud.