Azure Data Factory Training: Designing and Implementing Data Integration Solutions

Course Overview

The Azure Data Factory Training: Designing and Implementing Data Integration Solutions course is designed for professionals who want to build scalable, secure, and high-performance data integration pipelines using Microsoft Azure. This course focuses on transforming raw, distributed data into reliable and actionable datasets that support analytics, reporting, and enterprise decision-making.

Participants will gain in-depth knowledge of Azure Data Factory (ADF) architecture, including linked services, datasets, pipelines, triggers, and integration runtimes. The training emphasises real-world scenarios such as data ingestion from on-premises and cloud sources, data transformation, orchestration, and automation across hybrid environments.

Beyond development, the course addresses performance optimisation, monitoring, error handling, security, and governance, ensuring learners can design production-ready data integration solutions. By aligning with modern data engineering and cloud best practices, this training prepares professionals to confidently implement enterprise-scale data workflows and supports progression toward advanced Azure data certifications.

Azure Data Factory Training Information:

In this Azure Data Factory course, you will learn how to:

Build end-to-end ETL and ELT solutions using Azure Data Factory v2
Architect, develop, and deploy sophisticated, high-performance, easy-to-maintain, and secure pipelines that integrate data from a variety of Azure and non-Azure data sources.
Apply the latest DevOps best practices available for the ADF v2 platform.

Prerequisites:

Introduction to Cloud Infrastructure (AZ-900), or equivalent experience.

Flexible Training Options to
Meet Your Needs

We know the importance of flexibility in the success of learning and professional development. This is the reason CounselTrain provides a variety of delivery options for all IT training offered in the UAE to ensure access and convenience for each learner.

Select the method that best suits your needs.

Online Instructor-Led Training

Learn from the comfort of your workplace or at home through live virtual sessions led by expert trainers.

Classroom Training

Participate in interactive, face-to-face training in our top 5-star training facilities in Dubai

Onsite Training

Learn a customized curriculum in your workplace to ensure the most impact and team participation.

Overseas Training

Participate in our international training sessions and improve your abilities with world-class instructors.

Schedule Dates

09 February 2026 - 11 February 2026

Azure Data Factory Training: Designing and Implementing Data Integration Solutions

11 May 2026 - 13 May 2026

Azure Data Factory Training: Designing and Implementing Data Integration Solutions

17 August 2026 - 19 August 2026

Azure Data Factory Training: Designing and Implementing Data Integration Solutions

23 November 2026 - 25 November 2026

Azure Data Factory Training: Designing and Implementing Data Integration Solutions

Course Content

Historical background: SSIS, ADF v1, other ETL/ELT tools
Key capabilities and benefits of ADF v2 Recent feature updates and enhancements
Recent feature updates and enhancements

Connectors: Azure services, databases, NoSQL, files, generic protocols, services & apps, custom
Pipelines
Activities: data movement, data transformation, control flow
Datasets: source, sink
Integration Runtimes: Azure, Self-Hosted, Azure-SSIS

Creating ADF v2 instance
Creating a pipeline and associated activities
Executing the pipeline
Monitoring execution
Reviewing results

Copying Tools and SDKS
- Copy Data Tool/Wizard
- Copy activity
- SDKs: Python, .NET
- Automation: PowerShell, REST API, ARM Templates
Copying Considerations
- File formats: Avro, binary, delimited, JSON, ORC, Parquet
- Data store support matrix
- Write behavior: append, upsert, overwrite, write with custom logic
- Schema and data type mapping
- Fault tolerance options

Transformation with Mapping Data Flows
- Introduction to mapping
- Data flows Data flow canvas
- Debug mode
- Dealing with schema drift
- Expression builder & language
- Transformation types: Aggregate, Alter row, Conditional split, Derived column, Exists, Filter, Flatten, Join, Lookup, New branch, Pivot, Select, Sink, Sort, Source, Surrogate key, Union, Unpivot, Window
Transformation with External Services
- Databricks: Notebook, Jar, Python
- HDInsight: Hive, Pig, MapReduce, Streaming, Spark
- Azure Machine Learning service
- SQL Stored procedures
- Azure Data Lake Analytics U-SQL
- Custom activities with .NET or R

Purpose of activity dependencies: branching and chaining
Activity dependency conditions: succeeded, failed, skipped, completed
Control flow activities: Append Variable, Azure Function, Execute Pipeline, Filter, ForEach, Get Metadata, If Condition, Lookup, Set Variable, Until, Wait, Web

Debugging
Monitoring: visual, Azure Monitor, SDKs, runtime-specific best practices
Scheduling execution with triggers: event-based, schedule, tumbling window
Performance, scalability, tuning
Common troubleshooting scenarios in activities, connectors, data flows and integration runtimes

Quick introduction to source control with Git
Integration with GitHub and Azure DevOps platforms
Environment management: Development, QA, Production
Iterative development best practices
Continuous Integration (CI) pipelines
Continuous Delivery (CD) pipelines

Templates: out-of-the-box and organizational
Parameters
Naming convention

Data movement security
Azure Key Vault
Self-hosted IR considerations
IP address blocks
Managed identity

FAQs

This course is ideal for data engineers, cloud engineers, BI professionals, solution architects, and technical consultants responsible for building and managing data integration and orchestration solutions.

Participants learn to integrate data from on-premises systems, Azure services, third-party cloud platforms, databases, APIs, and data lakes, supporting hybrid and multi-cloud environments.

Learners explore best practices for pipeline optimisation, parallel execution, integration runtime configuration, and cost-efficient scaling in enterprise environments.

Yes. The training includes monitoring, logging, alerting, and troubleshooting techniques to ensure reliability and operational continuity of data workflows.

Yes. The course is designed with enterprise architecture, governance, and scalability in mind, making it suitable for complex and high-volume data environments.

This training supports career growth in data engineering and cloud analytics roles and aligns with advanced Azure data certifications and real-world enterprise implementations.

Azure Data Factory Training: Designing and Implementing Data Integration Solutions

Course Overview

Azure Data Factory Training Information:

Prerequisites:

Flexible Training Options to Meet Your Needs

Online Instructor-Led Training

Classroom Training

Onsite Training

Overseas Training

Schedule Dates

Azure Data Factory Training: Designing and Implementing Data Integration Solutions

Azure Data Factory Training: Designing and Implementing Data Integration Solutions

Azure Data Factory Training: Designing and Implementing Data Integration Solutions

Azure Data Factory Training: Designing and Implementing Data Integration Solutions

Course Content

Module 1: Introduction to ADF

Module 2: Core Architectural Components

Module 3: Building and Executing Your First Pipeline

Module 4: Data Movement

Module 5: Data Transformation

Module 6: Control Flow

Module 7: Runtime and Operations

Module 8: DevOps with ADF

Module 9: Promoting Reuse

Module 10: Security

FAQs

What professional roles is this Azure Data Factory training designed for?

What types of data sources are covered in this course?

How does the course address performance and scalability?

Will I learn how to monitor and troubleshoot data pipelines?

Is this course suitable for enterprise and large-scale data projects?

What career and certification pathways does this course support?

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Flexible Training Options to
Meet Your Needs