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 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

1. 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

1. Store

Goal: Store the ingested data.

Technology:

Azure Data Lake Storage Gen2

Benefits:

Secure
Scalable
Cost-effective
Supports analytics workloads

1. 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

1. 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