Part 8/11:
The core activities of data engineering—source identification, data cleansing, transformation, and deployment—are expected to persist in some form. However, their execution is transforming:
Increased automation and orchestration: Using modern workflows, machine learning, and AI to automate pipelines.
Business-aligned pipeline design: Data engineers must understand domain requirements thoroughly.
Application and API integration: Stronger ties between data and applications, requiring engineers to have some app development skills.
Data quality frameworks: Emphasizing the standardization, quality, and governance of data.
Framework adoption: Building scalable, reusable frameworks for data management to ensure consistent practices across larger organizations.