Azure Data Factory Course: Free Real-World Project

Learn Azure Data Factory Step By Step With Real World Project is a free video course that teaches Azure Data Factory (ADF) by building one data project from start to finish. It is for aspiring data engineers, ETL and BI developers, and SQL professionals who want to learn how to ingest, transform, orchestrate and monitor data pipelines in Azure.

The course has 88 videos and about 7.9 hours of content on YouTube. Most lessons are short, focused “how to” videos of 2 to 11 minutes, so you build the project one small step at a time. It covers storage, environment setup, linked services, datasets, pipelines and data flows, then goes further into Databricks, production readiness, monitoring and Power BI. See the full list on the Azure Data Factory course page.

Who this course is for

• Aspiring data engineers who want a practical first project in Azure.
• ETL and SSIS developers moving their skills to a cloud data integration service.
• SQL and BI developers who need to load and shape data before reporting.
• Students and career changers who learn best by building something real.

The level is Intermediate. A basic understanding of databases and SQL helps. You do not need prior Azure experience; the course shows how to create a free account and every resource you need.

What you will learn

The 88 videos group into seven themes.

1. ADF and the project. “Azure Data Factory Overview”, “Project Overview”, “Solution Architecture Overview” and “Understand Azure Storage Solutions” explain what you are building and why each storage option is used. Seeing the architecture first makes every later step make sense.

2. Environment setup. Videos on the Azure free account, then creating Azure Data Factory, a storage account, Azure Data Lake Storage Gen2 and an Azure SQL Database, and installing Azure Storage Explorer and Azure Data Studio.

3. Ingestion from Blob Storage. The copy activity, ADF naming standards, “How To Work With Linked Services And Data Sets In ADF”, creating a pipeline, control flow activities (validation, delete and more), triggers and a storage event trigger. This is the core of everyday ADF work.

4. Ingestion from HTTP and dynamic pipelines. Understanding the data source, pipeline variables and parameters, pipeline parameters with a schedule trigger, linked service parameters and a metadata-driven pipeline. Parameters let one pipeline handle many files instead of building one pipeline per file.

5. Data flows for transformation. An introduction to data flows and the Data Flow UI, then source, filter, select, pivot, lookup and sink transformations. An assignment section adds conditional split, derived column, aggregate, join and sort transformations, so you practise on your own.

6. Databricks and loading to Azure SQL. Creating a Databricks service and compute, mounting Azure Data Lake Storage, running a Databricks notebook from an ADF pipeline, and copy data activity scenarios into Azure SQL. One video also covers upgrading from the free subscription to pay-as-you-go before this section.

7. Production, monitoring and reporting. Preparing a pipeline for production, pipeline and trigger dependencies, the ADF monitor, alerts, monitoring and re-running failed pipelines, Azure Monitor and Log Analytics, and Power BI reports built on the project. The last video shows how to delete the Azure resource group.

How the course is organised

• 88 videos, about 7.9 hours in total, opening with an 18-minute course introduction.
• Sections run in project order: setup, ingestion from Blob Storage, ingestion from HTTP, data flows, data flow assignments, Databricks, copy to Azure SQL, production, monitoring and Power BI.
• Each section starts with a short “section overview” video.
• The data flow section is followed by an assignment section where you apply the same ideas yourself.

How to study it step by step

1. Watch the course introduction and the architecture videos until you can sketch the solution on paper.
2. Set up the environment exactly as shown: free account, Data Factory, storage, Data Lake Gen2 and Azure SQL Database.
3. Build the Blob Storage ingestion pipeline, including the validation and delete activities and the storage event trigger.
4. Build the HTTP ingestion with parameters and a schedule trigger, then make it metadata-driven.
5. Work through the data flow lessons, then do the assignments before watching the solutions.
6. Add Databricks and the copy to Azure SQL steps. Check the upgrade video first, since the course covers moving to pay-as-you-go here, and keep an eye on your Azure costs.
7. Make it production ready: dependencies, monitoring, alerts and re-running failed runs.
8. Finish with Power BI, then delete the resource group as shown in the last video.

At five to eight short videos a day, you can finish in two to three weeks with practice. The full course is also a YouTube playlist.

To go further, the Learn Azure Synapse Step By Step With Project course covers SQL pools, Spark and Synapse pipelines, and Data Engineering with Microsoft Fabric shows the same kind of work in Microsoft Fabric.

Frequently asked questions

Is this Azure Data Factory course free?

Yes. All 88 videos are free on YouTube. You can follow them from the course page or the playlist.

Do I need an Azure account?

To build the project yourself, yes. The course explains the Azure free account and shows how to create one. Later sections, such as Databricks, come after a video on upgrading to a pay-as-you-go subscription, so watch your costs and delete resources when you finish.

How long does the course take?

The videos total about 7.9 hours. Because you build the project as you go, plan for two to three weeks of regular practice.

Does the course cover data flows and Databricks?

Yes. There is a full data flow section with source, filter, select, pivot, lookup and sink transformations, an assignment section with more transformations, and a Databricks section that mounts Data Lake Storage and runs a notebook from ADF.