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Azure Data Factory Training: Designing and Implementing Data Integration Solutions

Azure Data Factory Training: Designing and Implementing Data Integration Solutions

Course 1231

Duration: 3 days
Labs: Yes
Language: English
Level: Intermediate

This Azure Data Factory Training covers all key aspects of the Azure Data Factory v2 platform. Special attention is paid to covering Azure services which are commonly used with ADF v2 solutions. These services are Azure Data Lake Storage Gen 2, Azure SQL Database, Azure Databricks, Azure Key Vault, Azure Functions, and a few others.

Azure Data Factory Training Delivery Methods

In-Person
Online

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

Learning Tree course 8566, Microsoft Azure Fundamentals Training (AZ-900T00), or equivalent experience.

Azure Data Factory Training Outline

Module 1: Introduction to ADF

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

Module 2: Core Architectural Components

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

Module 3: Building and Executing Your First Pipeline

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

Module 4: Data Movement

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 behaviour: append, upsert, overwrite, write with custom logic
Schema and data type mapping
Fault tolerance options

Module 5: Data Transformation

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

Module 6: Control Flow

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

Module 7: Runtime and Operations

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

Module 8: DevOps with ADF

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

Module 9: Promoting Reuse

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

Module 10: Security

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

Get This Course 26 110 kr

3-day instructor-led training course
Hands-on labs included
One-on-one after-course instructor coaching

Include dates with afternoon start times

#1231

nov 29 - dec 1 15:00 - 22:30 CET

Herndon, VA or Virtual
jan 9 - 11 10:00 - 18:00 CET

Virtual
jan 9 - 11 9:00 - 16:30 CET

Virtual
jan 17 - 19 15:00 - 22:30 CET

Ottawa or Virtual
feb 7 - 9 15:00 - 22:30 CET

Virtual
feb 28 - mar 1 15:00 - 22:30 CET

New York or Virtual
mar 26 - 28 14:00 - 21:30 CET

Virtual
apr 3 - 5 10:00 - 18:00 CEST

London or Virtual
apr 3 - 5 9:00 - 16:30 CEST

Stockholm or Virtual
maj 1 - 3 15:00 - 22:30 CEST

Virtual
maj 29 - 31 15:00 - 22:30 CEST

Herndon, VA or Virtual
jun 26 - 28 15:00 - 22:30 CEST

Virtual
jul 3 - 5 10:00 - 18:00 CEST

London or Virtual
jul 3 - 5 9:00 - 16:30 CEST

Stockholm or Virtual
jul 24 - 26 15:00 - 22:30 CEST

Herndon, VA or Virtual
sep 25 - 27 15:00 - 22:30 CEST

Herndon, VA or Virtual

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Azure Data Factory Training FAQs

Azure Data Factory (ADF) v2 is an Azure data integration service which allows creation of data-driven workflows to orchestrate and automate data movement and transformation across cloud, on-prem and hybrid environments.

While this new course is designed to bring students from zero expertise with ADF v2 to an intermediate or even advanced level of knowledge, Microsoft Azure Fundamentals Training (AZ-900T00) or equivalent is expected.

It is ideal for architects, developers, administrators, IT managers, and anyone else who would like to make the best possible use of this Azure service. Key areas covered include ADF v2 architecture, UI-based and automated data movement mechanisms, 10+ data transformation approaches, control-flow activities, reuse options, operational best practices, and a multi-tiered approach to ADF security.

6 hands-on instructor-led labs are included with the course. These allow students to practice applying ADF v2 concepts and prepare them for real-world Azure data integration projects.