Data engineering across industries: Enabling scalable, real-time, and insightful solutions

Data engineering focuses on acquiring, organizing, and preparing raw data for analytics and other use cases and making the right data available at the right place and at the right time so that it can be consumed and processed without friction. Data engineers implement and maintain the infrastructure such as databases and data lakes pivotal in the extract, transform, load process and data pipelines. Responsible for the architecture and engineering of data systems, data engineers build and deploy data pipelines from which, in turn, data scientists, analysts, and other consumers can extract actionable insights (Napoli, 2011; Lotz, 2017; Lobato, 2019). Data engineering encompasses an array of tasks including centralizing, modeling, cleansing, and transforming data. Businesses, organizations, and institutions rely on data engineers to ensure that the volume and variety of incoming data — structured, semi-structured, unstructured, time-series, or geographical — conforms to the guidelines established by the organization and is stored in a way that optimizes computing capabilities. Big data technologies and programming languages for data engineering enable data engineers to aggregate structured and unstructured data from disparate sources, cleanse and transform it, and load it into relational and non-relational databases and data lakes for downstream reporting and machine learning. At the same time, modern analytical business intelligence and dashboarding solutions have empowered analysts to perform their own ETL without having to work with data engineers (Taneja et al., 2012; Webster et al., 2013).