Part 6/11:

• Handling Data Changes: For instance, if the 'sanctioned amount' field changes, pipelines must be designed to accommodate such schema variations without breaking downstream KPIs. This entails adopting microservices architecture for KPIs, containerization, and separate processing pipelines, preventing a single point of failure.

This process-centric view ensures the data architecture mirrors real-world workflows, enabling accurate reporting, compliance, and actionable insights.

Building a Scalable Data Architecture

The speaker shared a technical blueprint for robust data pipelines:

• Layered Data Lake and Data Warehouse: Raw data from core applications enters a data lake; subsequent transformation layers create domain-specific data products.