Part 4/11:
With advancements, packaged analytics and application frameworks emerged, simplifying some aspects of data architecture. These systems combined data models with specific functions, reducing complexity but still requiring traditional ETL workflows. The core activities of data engineers in sourcing, cleansing, transforming, and deploying data persisted through this era.
In the last decade, the landscape has become more complex, integrating multiple data types—structured, semi-structured, and unstructured—and supporting diverse consumption needs such as data science, machine learning, real-time analytics, and domain-specific data marts.