Building ETL Pipelines with Azure Data Factory

 🧱 ETL Overview in Azure Data Factory

Phase Purpose

Extract Pull data from sources (SQL, Blob, APIs)

Transform Clean, filter, join, aggregate data

Load Push data to a destination (Data Lake, SQL DB, etc.)

🧰 Key Components of Azure Data Factory

Component Description

Pipeline A logical grouping of activities (steps in ETL process)

Activity A single step in the pipeline (e.g., copy data, execute stored procedure)

Dataset Metadata that represents data (e.g., a table, file, blob)

Linked Service Connection info for data sources/destinations

Trigger Initiates pipeline runs (e.g., scheduled, event-based, manual)

Integration Runtime The compute infrastructure to run data flows and copy activities

🏗️ Steps to Build an ETL Pipeline in Azure Data Factory

🥇 1. Create an Azure Data Factory Instance

Go to Azure Portal → Create a Resource → Search for Data Factory

Set up basic info: Name, region, resource group

Choose V2 version (recommended)

🥈 2. Create Linked Services

Navigate to Manage > Linked services

Add connections to:

Source (e.g., Azure Blob Storage, SQL Server, REST API)

Sink (e.g., Azure SQL DB, Data Lake)

🥉 3. Define Datasets

Go to Author > Datasets

Create datasets for both source and destination

Choose the format (e.g., CSV, JSON, Parquet)

Link to the appropriate linked service

🏃 4. Build the Pipeline

Go to Author > Pipelines

Add activities to perform ETL:

🚀 Common Activities:

Activity Description

Copy Data Extract from source and load to destination

Data Flow Perform transformations visually (Spark-based)

Stored Procedure Run SQL procedures

Web/API Call Call REST endpoints or functions

Wait, If, ForEach Control flow and logic

🧬 5. Add Data Flow (Optional for Complex Transforms)

Use Mapping Data Flows to do transformations like:

Join, filter, pivot, flatten, aggregate

Derived columns, conditional splits, lookups

Example transformation:

text

Copy

Edit

CSV → Clean Data (remove nulls) → Join with another dataset → Aggregate by region → Load to SQL

⏰ 6. Add a Trigger

Schedule your pipeline:

Time-based (e.g., daily at midnight)

Event-based (e.g., when a blob is uploaded)

Manual (for testing)

📊 7. Monitor Pipeline Runs

Go to Monitor tab to:

View run status (Succeeded, Failed)

Rerun failed pipelines

Check logs, activity run details

💡 Example Use Case: CSV to Azure SQL DB

Extract CSV file from Azure Blob

Clean/transform with Mapping Data Flow

Load into Azure SQL Database

🔐 Security and Optimization Tips

Use managed identities to securely access Azure resources

Enable data lineage and logging

Use integration runtimes wisely (Self-hosted for on-prem, Azure IR for cloud)

Apply parameterization for reusable and dynamic pipelines

Monitor with Azure Monitor or Log Analytics

📌 Summary

Step Purpose

Create Linked Services Connect to data systems

Define Datasets Represent your source/target data

Build Pipeline Orchestrate ETL activities

Add Data Flows Transform data visually

Monitor & Optimize Ensure reliability and performance

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