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Building low-code pipelines in Azure Data Factory



Duration (fully-guided training)


Flipped-classroom training duration:

of videos and

of interactive workshop.

About the Course

As organizations create ever more data pipelines, one quickly loses the oversight. Several vendors provide convenient tooling to manage and monitor data pipelines though. Some of these tools require decent coding skills, others are low code or even no-code tools, which are excellent in many organizations as they leverage existing well-written functionality and the domain knowledge of the people inside these organizations. Azure Data Factory is one of these low code/no-code tools that integrates very well with other cloud services offered by Azure and can be picked up by most data workers easily.

This course is an introduction to Azure Data Factory.

What will we cover?
For each of the following concepts the course includes conceptual and technical information as well as hands-on practice with the most important aspects.

- What is Azure ADF?
- The UI
- ADF Concepts
- Linked Services
- Runtime integrations
- Datasets
- Pipeline & Activities
- Data Flows
- Monitoring
- Triggers
- Versioning
- Security
- Costs

Participants must be registered in an Azure Active Directory using their e-mail address. Participants will be sent an invitation to access our learning environment.

What will you be able to do after participating in this workshop?
- Know what ADF is and get a sense of what it can do
Understand the concept of linked services and create them.
- Understand the concept of Datasets and create them.
- Understand the concept of Activities and Pipelines and create very common patterns in the world of data engineering
- Understand the concept of Data flows and create them.
- Know where and how to monitor your pipelines in a manner that is scalable and clear.
- Know how to schedule and run your pipelines in an efficient manner that reduces the cost
- Learn about ADF versioning and set it up for yourself.
- Get best practice tips on how to reduce costs and reduce pipeline runtime.

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