Azure Data Factory and Fabric Data Factory: Data Integration for Data Professionals

Learn to design, automate, and optimize data pipelines across Azure and Microsoft Fabric environments

Course Summary

Are you ready to master modern data integration with Azure and Fabric Data Factory?

Our comprehensive course is your gateway to becoming highly proficient in both Azure Data Factory and Microsoft Fabric Data Factory — the next-generation tools for cloud-scale data movement, transformation, and orchestration.

Ask Yourself:

Have you used Azure Data Factory or just heard about it?
Are you working with Power BI, Fabric, or SQL and need to move and transform data reliably?
Are you curious about how Microsoft Fabric changes the data engineering landscape?
Do you want to create automated pipelines that bring data from multiple sources into OneLake, a data warehouse, or a database?
Are you looking to understand the differences and overlaps between Azure and Fabric Data Factory — and how to use both effectively?
Would you like to learn from an expert who works with these tools daily and explains them in simple, practical terms?

If any of these sound like you, then this course is tailor-made to help you build real skills, fast.

You’ll gain hands-on experience in building pipelines, working with dataflows, automating workflows, integrating with handling security and governance, and optimizing performance — across both Azure and Fabric.

Whether you’re new to data integration or want to take your skills to the next level, this course offers detailed instruction, practical exercises, real-world scenarios, and knowledge checks to ensure that what you learn sticks. Don’t miss this opportunity to grow your data engineering capabilities and unlock your full potential with Azure and Fabric Data Factory. This is one of the most complete and current courses available today.

Enroll now for $295 USD !

Modules and Lessons

Module 0: Getting started

Who is this course for?
Who is Greg?
What will I learn in this course?
Configuring your lab environment

Module 1: Core concepts

Module introduction
Role of Integration in modern analytics
Common loading patterns - ETL vs ELT
On-premises tooling vs cloud-based tooling
What are pipelines?
What is Azure Data Factory (ADF)?
What is Fabric Data Factory (FDF)?
Core Data Factory components
Differences between ADF and FDF
Pros and cons of each Data Factory platform
Quiz 1

Module 2: Provisioning data factories

Module introduction
Creating an Azure Resource Group
Creating an Azure Data Factory
Using ADF with and without source control
Connecting source control
Using ADF Studio
Creating a Fabric Workspace and Data Factory
Using Fabric Studio
Lab 2
Quiz 2

Module 3: Wizard-based copying of data

Module introduction
Types of Azure storage
Azure Storage Explorer
SQL Server Management Studio (SSMS)
Copy Data Tool in ADF
Storage in Fabric
Copy Data Job in FDF
Lab 3
Quiz 3

Module 4: Concepts common to both ADF and FDF

Module introduction
Pipeline core components
Pipeline design interface elements
Connections
Linked services
Datasets
Folders
Lab 4
Quiz 4

Module 5: Implementing workflow logic in pipelines

Module introduction
Pipeline workflow
Common activity settings
Wait activity
Common activity actions
Variables
Set Variable activity
Expression language
Append Variable activity
If Condition activity
Implementing For Each loops
Implementing While Until loops
Filter activity
Switch activity
Logical AND vs logical OR
Lab 5
Quiz 5

Module 6: Working with parameters and templates

Module introduction
Pipeline parameters
Using built-in templates
Creating templates
Lab 6

Module 7: Triggering pipeline execution

Module introduction
Scheduling in ADF
Manual triggers
Schedule triggers
Tumbling window triggers
Storage Event triggers
Custom event triggers
Scheduling in FDF
FDF scheduling update
Lab 7
Quiz 7

Module 8: Working with common activities

Module introduction
Delete activity
Copy activity
Stored Procedure activity
Lookup activity
Script activity
Get Metadata activity
Execute Pipeline activity
Web activity
Fail activity
Notebook activity
Lab 8
Quiz 8

Module 9: Managing integration runtimes

Module introduction
Integration runtimes
Azure integration runtimes
Self-hosted integration runtimes
Linking and sharing integration runtimes
SQL Server Integration Services
Executing SSIS packages in ADF
Azure SSIS integration runtimes
SSIS IR cost implications
Lab 9
Quiz 9

Module 10: Working with data flows

Module introduction
ADF mapping data flows
ADF data flow performance and cost implications
ADF Power Queries
Comparing Power Queries to data flows
ADF flowlets
FDF gen 2 data flows
Comparing ADF and FDF data flows
Lab 10
Quiz 10

Module 11: Managing pipelines

Module introduction
Debugging pipelines
Annotations
User properties
Defining dynamic connections by using expressions
Monitoring ADF pipelines
Sending alerts on failure
Copying JSON to create pipelines
Useful 3rd party deployment utilities and tools
Git Integration in Fabric
Monitoring FDF pipelines
Lab 11
Quiz 11

Module 12: Managing Data Factory security

Module introduction
Identity concepts in Azure
RBAC based security
System-assigned managed identities
User-assigned managed identities
Workspace identities in FDF
Using managed identities in connections
Azure Key Vault
Integrating ADF with Azure Key Vault
Integrating FDF with Azure Key Vault
Using key vault secrets in connections
Connecting via private endpoints
Lab 12
Quiz 12

Module 13: Medium complexity worked example

Module introduction
Problem to solve
REST based calls required
Configuring security
Making refresh request
Checking latest refreshes
Filter to current refresh
Retry until complete
Throw error on failure

Module 14: Very detailed worked example (medium complexity)

Module introduction
Problem to solve
Overall solution design
Create resource group
Create and configure storage account
Create SQL database
Create data factory
Configure linked services
Upload test files
Create datasets
Create and test file move pipeline
Create incremental load pipeline
Add and test loop for file processing
Create database objects
Add database processing to file processing loop
Test completed pipeline
Further thoughts

Module 15: Complex worked example

Module introduction
Problem to solve
Options considered
Overall solution design
File in storage account
Testing requirements
First attempt - add dataset reference
Correct multipart form data
Attachment file contents
Returned preview data
Multipart form data format
Web request content type and body
Configure the web activity for upload
HTTP request timeout
Line endings
Requirement achieved
Further thoughts

Module 16: Advanced ADF and FDF concepts

Module introduction
Validation activity
Azure Data Explorer command
Azure Function activity
Databricks-related activities
WebHook activity
HDInsight-related activities
Data Lake Analytics-related activities
Machine Learning-related activities

Module 17: Next steps

Remove lab resources
Summary and further steps

Enroll now for $295 USD !