Data engineering builds the foundation for data science and analytics in production. It exists since companies worked with data (predictive analysis, descripticve analysis and reports). It came into sharp focus during the rise of data science in the 2010s.
Data engineering is the development, implementation, and maintenance of systems and processes that take in raw data and produce high-quality, consistent information that supports downstream use cases, such as analysis and machine learning. Data engi‐ neering is the intersection of security, data management, DataOps, data architecture, orchestration, and software engineering. A data engineer manages the data engineering lifecycle, beginning with getting data from source systems and ending with serving data for use cases, such as analysis or machine learning.