QN : stages for processing big data
Stage Purpose Common Azure/Microsoft Tools Ingest Collect data from source systems Microsoft Fabric Pipelines, Azure Event Hubs, Azure Stream Analytics Store Save the data securely and scalably Azure Data Lake Stor
| Stage | Purpose | Common Azure/Microsoft Tools |
|---|---|---|
| Ingest | Collect data from source systems | Microsoft Fabric Pipelines, Azure Event Hubs, Azure Stream Analytics |
| Store | Save the data securely and scalably | Azure Data Lake Storage Gen2 |
| Prep & Train | Clean data, transform data, build ML models | Azure Databricks, Microsoft Fabric, Azure Machine Learning |
| Model & Serve | Deliver insights to users | Microsoft Power BI, Microsoft Fabric |
- Ingest
Goal: Bring data into the data lake.
Data sources:
Files
Logs
Applications
IoT devices
Databases
Tools:
Batch ingestion → Fabric Pipelines
Real-time ingestion → Azure Event Hubs, Azure Stream Analytics, Fabric Real-Time Intelligence
- Store
Goal: Store the ingested data.
Technology:
Azure Data Lake Storage Gen2
Benefits:
Secure
Scalable
Cost-effective
Supports analytics workloads
- Prep & Train
Goal: Transform data and build machine learning models.
Activities:
Data cleaning
Data transformation
Feature engineering
Model training
Model scoring
Tools:
Azure Databricks
Microsoft Fabric
Azure Machine Learning
- Model & Serve
Goal: Present insights to users.
Outputs:
Dashboards
Reports
Predictions
Analytics applications
Tools:
Microsoft Power BI
Microsoft Fabric
Exam Shortcut
Think of a data lake as a factory:
Raw Data → Ingest → Store → Prep & Train → Model & Serve → Business Insights
Example:
Sales transactions arrive → Ingest
Stored in ADLS Gen2 → Store
Cleaned and transformed in Databricks → Prep & Train
Visualized in Power BI → Model & Serve
Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes — full credit and traffic to the original publisher.