This is a very generic question with an even broader response. However, think of scenarios in which the most common architecture called Medallion Architecture can be applied along with very high volume of data:
https://learn.microsoft.com/en-us/azure/databricks/lakehouse/medallion
https://www.databricks.com/glossary/medallion-architecture
Based on the above, some real-life scenarios:
1. Building a 360° Customer View
Business problem
Customer data lives in multiple systems:
CRM (Salesforce)
Support tickets (Zendesk)
Marketing tools
Website logs
In-store POS
Leaders want one unified view of the customer:
Lifetime value
Churn risk
Purchase history
Behavior patterns
Data Engineering role
Integrate, clean, and merge sources → maintain a golden customer table used by analytics, marketing, and ML
2. Real-Time Operational Dashboards
Business problem
Managers need up-to-the-minute insights:
Orders per minute
Fraud alerts
Inventory levels
Shipments in transit
Data Engineering role
Build streaming pipelines that feed dashboards with low latency, powering decisions like:
Detecting issues earlier
Balancing supply/demand faster
Notifying teams when KPIs drop
3. Supply Chain Optimization
Business problem
Logistics teams want to:
Predict stock shortages
Optimize delivery routes
Reduce warehouse costs
Track shipments in real time
Data Engineering role
Integrate:
Vendor data
Warehouse systems
IoT sensors
Transportation APIs
Deliver actionable datasets to planning/ML teams.
4. Executive Decision Dashboards
Business problem
C-level wants:
One version of truth
KPIs updated daily
A curated layer of governed metrics
Data Engineering role
Provide a semantic layer:
Sales
Revenue
Retention
Operational KPIs
Forecasts
Ensure dashboards don’t break and metrics are consistent across the company. models.
5. Predictive Maintenance (IoT)
Business problem
Manufacturers need to avoid:
Machine failures
Unexpected downtime
Costly repairs
Data Engineering role
Ingest IoT sensor data:
Temperature
Vibration
Pressure
Usage cycles
Provide structured data for ML models that predict failures.
