2. Data Engineering

After project initiation, the data engineering team takes over to build necessary infrastructure to acquire (identify, retrieve, and query), munge, explore, and persist data. The goal is to enable further data analysis tasks. Data engineering requires different expertise than is required in later stages of a data science process. It is typically an engineering discipline oriented toward craftsmanship to provide necessary input to later phases. Often disparate technologies must be orchestrated to handle data communication protocols and formats, perform exploratory visualizations, and preprocess (clean, integrate, and package), scale, and transform data. All these tasks must be done in context of a global project vision and mission relying on domain knowledge. It is extremely rare that raw data from sources is immediately in perfect shape to perform analysis. Even in the case of a clean dataset, there is often a need to simplify it. Consequently, dimensionality reduction coupled with feature selection (remove, add, and combine) is also part of data engineering. This chapter illustrates data engineering through two detailed case studies, which highlight most aspects of it.