15-Day Azure Data Factory (ADF) for Data Engineering Master Guide
For 4–10 Years Experienced Data Engineers
Objective
This guide is designed to:
Build strong Azure Data Factory fundamentals
Understand enterprise ETL orchestration
Learn real-time pipeline implementations
Master integration and automation concepts
Prepare for senior-level interviews
Build scalable cloud data engineering mindset
Target Audience:
Data Engineers
Azure Data Engineers
ETL Developers
Cloud Data Platform Engineers
Integration Engineers
Daily Time Commitment:
3 Hours Per Day
15 Days Total
Learning Strategy:
20% Theory
80% Hands-On Practice
Goal:
Build enterprise-grade ETL pipelines
Understand orchestration deeply
Handle production workflows
Implement incremental processing
Build scalable cloud integrations
Daily Learning Structure
Hour 1 – Learn Concepts
Focus on:
Understanding WHY ADF exists
Pipeline orchestration concepts
Integration architecture
Real-time enterprise use cases
Avoid:
Memorizing UI clicks blindly
Watching endless tutorials
Hour 2 – Hands-On Development
Focus on:
Building pipelines
Configuring linked services
Creating dynamic ETL workflows
Parameterization
Hour 3 – Real-Time Scenarios
Focus on:
Pipeline failures
Monitoring
Incremental loads
Optimization
Debugging
Enterprise architecture understanding
SECTION 1 – AZURE DATA FACTORY BASICS
Topics:
What is ADF
Why ADF
ETL vs ELT
ADF Components
Data Integration Concepts
WHAT IS ADF
Azure Data Factory is a cloud-based data integration and orchestration service.
Purpose:
Build ETL pipelines
Move data between systems
Orchestrate workflows
Automate data processing
WHY ADF IS USED
Problems ADF Solves:
Complex data movement
ETL orchestration
Cloud integration
Scheduling workflows
Incremental processing
Enterprise automation
ETL VS ELT
Critical Interview Topic.
Understand:
ETL processing
ELT processing
Transformation location
Cloud optimization
ADF COMPONENTS
Topics:
Pipelines
Activities
Datasets
Linked Services
Integration Runtime
Triggers
Data Flows
SECTION 2 – ADF ARCHITECTURE
Topics:
Control Flow
Data Flow
Integration Runtime
Linked Services
Datasets
CONTROL FLOW
Purpose:
Pipeline orchestration.
Activities:
Execute pipeline
If condition
Switch
ForEach
Until
Wait
DATA FLOW
Purpose:
Transformation logic.
Use Cases:
Cleansing
Aggregations
Joins
Derived columns
INTEGRATION RUNTIME (IR)
Critical Topic.
Types:
Azure IR
Self-hosted IR
Azure SSIS IR
Purpose:
Data movement
Compute execution
Connectivity
LINKED SERVICES
Purpose:
Connection management.
Examples:
Azure SQL
Blob Storage
ADLS
SQL Server
REST APIs
Databricks
DATASETS
Purpose:
Represent data structures.
Examples:
CSV files
JSON files
Database tables
SECTION 3 – PIPELINES
Topics:
Pipeline creation
Activities
Dependency management
Parameterization
Variables
PIPELINES
Purpose:
Logical grouping of activities.
Real-Time Usage:
ETL orchestration
Batch processing
Data synchronization
ACTIVITIES
Most Important Topic.
Types:
Copy activity
Lookup activity
Stored procedure activity
Execute pipeline
Web activity
Databricks notebook activity
Data flow activity
COPY ACTIVITY
Critical Interview Topic.
Purpose:
Move data between systems.
Practice:
SQL to Blob
Blob to SQL
API to ADLS
CSV to Parquet
LOOKUP ACTIVITY
Purpose:
Retrieve metadata/configuration.
Real-Time Usage:
Dynamic pipelines
Config-driven ETL
STORED PROCEDURE ACTIVITY
Purpose:
Execute SQL procedures.
Use Cases:
Logging
Post-processing
Data validation
EXECUTE PIPELINE ACTIVITY
Purpose:
Parent-child orchestration.
Benefits:
Modular pipeline design
Reusability
WEB ACTIVITY
Purpose:
Call REST APIs.
Use Cases:
Trigger APIs
Notifications
External integrations
DATABRICKS NOTEBOOK ACTIVITY
Purpose:
Trigger Databricks notebooks.
Use Cases:
PySpark transformations
Delta processing
Advanced ETL logic
SECTION 4 – PARAMETERIZATION AND DYNAMIC CONTENT
Topics:
Parameters
Variables
Expressions
Dynamic content
PARAMETERS
Purpose:
Reusable pipelines.
Practice:
Dynamic file paths
Dynamic table names
Environment handling
VARIABLES
Purpose:
Store runtime values.
Practice:
Counters
Status tracking
Dynamic processing
EXPRESSIONS
Critical Topic.
Functions:
concat
substring
utcNow
pipeline
activity
replace
Practice:
Dynamic file generation
Timestamp creation
SECTION 5 – CONTROL FLOW ACTIVITIES
Topics:
If condition
Switch
ForEach
Until
Wait
FOREACH ACTIVITY
Critical Topic.
Purpose:
Loop processing.
Real-Time Usage:
Process multiple files
Dynamic table loads
IF CONDITION
Purpose:
Conditional execution.
Use Cases:
Success/failure handling
Validation logic
UNTIL ACTIVITY
Purpose:
Loop until condition met.
Use Cases:
Polling APIs
Monitoring jobs
SECTION 6 – DATA FLOWS
Topics:
Mapping Data Flow
Source
Sink
Derived column
Aggregate
Join
Filter
Window
DATA FLOWS
Purpose:
Graphical transformations.
Real-Time Usage:
ETL transformations
Cleansing
Aggregations
DERIVED COLUMN
Purpose:
Create calculated fields.
AGGREGATE
Purpose:
Summarize data.
JOIN TRANSFORMATIONS
Topics:
Inner join
Left join
Exists
WINDOW TRANSFORMATIONS
Use Cases:
Ranking
Running totals
Deduplication
SECTION 7 – INCREMENTAL LOADS
Topics:
Watermarking
CDC
Delta loads
Upserts
WATERMARKING
Critical Topic.
Purpose:
Load only changed data.
Practice:
Timestamp tracking
Last successful load logic
CDC
Purpose:
Capture inserts/updates/deletes.
Real-Time Usage:
Incremental ETL
Audit pipelines
UPSERT LOGIC
Use Cases:
Delta processing
Historical tracking
SECTION 8 – TRIGGERS
Topics:
Schedule trigger
Tumbling window trigger
Event trigger
SCHEDULE TRIGGERS
Purpose:
Run pipelines on schedule.
EVENT TRIGGERS
Purpose:
Trigger on file arrival.
Use Cases:
Real-time ingestion
Landing zone automation
TUMBLING WINDOW TRIGGERS
Purpose:
Time-based dependency processing.
SECTION 9 – MONITORING AND DEBUGGING
Topics:
Monitoring
Debugging
Alerts
Logging
Retry handling
MONITORING
Critical Production Topic.
Learn:
Activity runs
Pipeline runs
Error tracking
Performance monitoring
RETRY POLICIES
Purpose:
Handle transient failures.
Practice:
Retry configuration
Timeout handling
ERROR HANDLING
Topics:
Fail activity
Try-catch pattern
Logging tables
SECTION 10 – SECURITY
Topics:
Managed identities
Key Vault
RBAC
Private endpoints
AZURE KEY VAULT
Critical Topic.
Purpose:
Secure secrets.
Use Cases:
Password storage
API keys
Connection strings
MANAGED IDENTITIES
Purpose:
Secure authentication.
Benefits:
No hardcoded secrets
Secure access
SECTION 11 – PERFORMANCE OPTIMIZATION
Topics:
Parallelism
Partitioning
Batch size
Staging
Compression
PARALLEL COPY
Purpose:
Improve throughput.
Practice:
Parallel file processing
Partitioned loading
STAGING
Purpose:
Improve large data movement.
COMPRESSION
Formats:
gzip
snappy
Purpose:
Reduce storage and transfer cost.
SECTION 12 – REAL-TIME ETL ARCHITECTURE
Topics:
Batch pipelines
Incremental pipelines
Medallion architecture
Orchestration
ENTERPRISE ETL FLOW
Source Systems
↓
Landing Layer
↓
ADF Orchestration
↓
Databricks Processing
↓
Delta Lake
↓
Gold Reporting Layer
↓
Power BI / Analytics
MEDALLION ARCHITECTURE
Layers:
Bronze
Silver
Gold
BRONZE LAYER
Purpose:
Raw ingestion.
SILVER LAYER
Purpose:
Validated and cleansed data.
GOLD LAYER
Purpose:
Business-ready analytics.
SECTION 13 – REAL-TIME PROJECT STRUCTURE
Typical ADF Project Structure:
project/
│
├── pipelines/
│ ├── ingestion_pipeline
│ ├── transformation_pipeline
│ └── reporting_pipeline
│
├── datasets/
│ ├── source_datasets
│ └── sink_datasets
│
├── linked_services/
│ ├── sql_ls
│ ├── blob_ls
│ └── databricks_ls
│
├── triggers/
│ └── daily_trigger
│
├── dataflows/
│ └── cleansing_flow
│
├── config/
│ └── config.json
│
└── documentation/
└── pipeline_design.docx
SECTION 14 – MID-LEVEL PROJECTS
PROJECT 1 – SALES INGESTION PIPELINE
Requirements:
Read CSV files
Load to ADLS
Trigger Databricks notebook
Generate logs
Concepts Used:
Copy activity
Triggers
Dynamic content
PROJECT 2 – INCREMENTAL CUSTOMER PIPELINE
Requirements:
Process changed records only
Maintain watermark table
Trigger Delta merge
Concepts Used:
Watermarking
Stored procedures
Dynamic pipelines
PROJECT 3 – API INGESTION PIPELINE
Requirements:
Call REST API
Store JSON response
Process nested data
Concepts Used:
Web activity
JSON handling
Error handling
PROJECT 4 – MULTI-FILE PROCESSING FRAMEWORK
Requirements:
Process multiple source files
Loop dynamically
Generate success/failure reports
Concepts Used:
ForEach
Lookup
Dynamic parameters
PROJECT 5 – ENTERPRISE ETL ORCHESTRATION
Requirements:
Parent-child pipelines
Databricks integration
Retry handling
Logging framework
Concepts Used:
Execute pipeline
Logging
Monitoring
Error handling
SECTION 15 – ADF INTERVIEW QUESTIONS
BASIC QUESTIONS
What is ADF?
Difference between ETL and ELT.
What are linked services?
What are datasets?
What is Integration Runtime?
Difference between Azure IR and Self-hosted IR.
What are triggers?
What is Copy Activity?
What are pipelines?
What are Data Flows?
INTERMEDIATE QUESTIONS
Explain ADF architecture.
Explain dynamic content.
Explain parameterization.
Explain watermarking.
Explain incremental loads.
Explain event triggers.
Explain retry policies.
Explain ForEach activity.
Explain Databricks integration.
Explain monitoring strategy.
ADVANCED QUESTIONS
Design enterprise ETL orchestration.
Handle millions of files efficiently.
Optimize slow copy activity.
Design incremental CDC pipelines.
Implement metadata-driven framework.
Explain production debugging approach.
Design parent-child orchestration.
Handle pipeline failures gracefully.
Explain secure secret management.
Design scalable cloud ETL architecture.
SECTION 16 – 15-DAY EXECUTION PLAN
WEEK 1 – FOUNDATION
Day 1
ADF basics
Architecture
Components overview
Day 2
Linked services
Datasets
Integration Runtime
Day 3
Pipelines
Activities
Copy activity
Day 4
Parameters
Variables
Dynamic content
Day 5
ForEach
If condition
Control flow
Day 6
Data flows
Aggregations
Joins
Derived columns
Day 7
Mini ETL project
WEEK 2 – ADVANCED ADF
Day 8
Incremental loads
Watermarking
CDC
Day 9
Triggers
Event-based pipelines
Day 10
Monitoring
Logging
Retry handling
Day 11
Security
Key Vault
Managed identities
Day 12
Performance optimization
Parallelism
Partitioning
Day 13
Databricks integration
Enterprise orchestration
Day 14
Mid-level projects
Day 15
FINAL MOCK INTERVIEW + REVISION
REAL-TIME BEST PRACTICES
Always Follow:
Use parameterized pipelines
Avoid hardcoded values
Use Key Vault
Implement logging
Handle failures gracefully
Use modular design
Use metadata-driven frameworks
Implement retries
Use proper naming conventions
Monitor pipelines proactively
MOST IMPORTANT SKILLS FOR SENIOR ENGINEERS
You must become strong in:
Pipeline orchestration
Incremental processing
Cloud integration
Dynamic ETL frameworks
Monitoring and debugging
Security implementation
Databricks integration
Real-time troubleshooting
Scalability thinking
Enterprise architecture understanding
FINAL INTERVIEW EXPECTATIONS
At 4–10 years experience, interviewers expect:
Strong orchestration understanding
Enterprise ETL design capability
Dynamic pipeline implementation knowledge
Incremental loading expertise
Production troubleshooting mindset
Secure integration understanding
Databricks + ADF integration knowledge
Real-time implementation experience
They do NOT expect only UI knowledge.
They expect:
Engineering mindset
Scalable orchestration design
Production-level troubleshooting
Cloud integration understanding
Enterprise ETL architecture capability
END OF DOCUMENT
Comments
Post a Comment