Imagine data as water. Every day, water flows from multiple taps—each tap representing a different data source. This water is collected and channeled through pipelines, just like raw data flows through ETL processes. In this module, I learned how data architecture is essentially the blueprint for managing and analyzing this constant flow of information.
Data ingestion is like gathering water from various sources. Once collected, this water is routed through pipes (→), where it’s cleaned and transformed. The storage part is managed in different reservoirs:
- Data Warehouses are like huge, organized tanks that store structured water for immediate use.
- Data Marts are smaller, specialized tanks for focused needs.
- Data Lakes are open lakes holding every drop of water—structured, semi-structured, and unstructured.
The system relies on key principles: fast flow (velocity), plenty of water (volume), clean water (veracity), various types (variety), and finally, water that provides essential value (value).
Understanding data architecture is like mastering water management—ensuring every drop is captured and transformed into something useful. This insight has deepened my appreciation for data engineering and its role in powering modern business decisions.
