This course helps you master Azure Data Factory, build scalable pipelines, integrate data, orchestrate workflows, and excel in data engineering roles.

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Azure has become a leading cloud platform. Expertise in ADF opens doors to lucrative roles in data engineering and analytics.

This course provides you with the essential skills to master Azure Data Factory. You’ll start with ADF’s core concepts, architecture, and data connectivity to both on-premises and cloud sources. Through hands-on lessons and real-world examples, you’ll learn to create, manage, and monitor pipelines, handle incremental data loads, and work with version control using Git. Advanced topics include big data integration with Azure SQL, HDInsight, and Hive and designing analytics and machine learning workflows.

After completing this course, you’ll confidently design, develop, and maintain data pipelines in Azure Data Factory. You’ll be equipped to tackle integration challenges, optimize cost and performance, ensure scalability and high availability, and implement data governance and security—boosting your career prospects in data engineering and analytics.

Azure Data Factory Bootcamp: From Beginner to Expert

A machine learning platform that allows developers to build, train, and deploy models at scale

A cloud storage service offered by Microsoft

    PublicationYear > 2000 && Genre == 'Science Fiction'

    Filter(PublicationYear > 2000 && Genre == 'Science Fiction')

    Filter(PublicationYear > 2000) && Filter(Genre == 'Science Fiction')

    Filter(PublicationYear > 2000 || Genre == 'Science Fiction')

When working with a dataset in ADF that contains information about books, including the columns “PublicationYear” and “Genre.” 

The task is to filter the dataset to include only books published after the year 2000 and belonging to the “Science Fiction” genre. 

Which code snippet correctly implements this filter in an Azure Data Factory data flow?

When working with a dataset in ADF that contains information about books, including the columns “PublicationYear” and “Genre.”
The task is to filter the dataset to include only books published after the year 2000 and belonging to the “Science Fiction” genre.
Which code snippet correctly implements this filter in an Azure Data Factory data flow?

Register the pipeline in the Azure Machine Learning Workspace and deploy it to an Azure Kubernetes Service cluster.

Deploy the pipeline to an Azure Function App.

Publish the pipeline as an Azure Container Instance.

What is the process of publishing a machine learning pipeline in Azure Data Factory?

What is the process of publishing a machine learning pipeline in Azure Data Factory?

Lower cost compared to other storage options

Supports ACID transactions for data consistency

Faster data ingestion and query performance

Which option is not an advantage of using Delta Lake?

Which option is not an advantage of using Delta Lake?

Machine Learning batch execution activity

**(Select all that apply.)**
Which activities can be used to access and process Delta Lake in Azure Data Factory?

(Select all that apply.)
Which activities can be used to access and process Delta Lake in Azure Data Factory?

Which option is not a Delta Lake transformation available in Azure Data Factory?

Which option is not a Delta Lake transformation available in Azure Data Factory?

Monitoring the activity run status in the ADF portal

Using Azure Monitor to track the number of rows in the Delta Lake table

Using Azure Synapse Analytics to run SQL queries against the Delta Lake table

Using Azure Data Factory Metrics to view activity run time and success rate

What is not a way to monitor an ADF pipeline pushing updates to Delta Lake?

What is not a way to monitor an ADF pipeline pushing updates to Delta Lake?

Filter(toInteger(Age) >= 30 || toInteger(Age) <= 45 && Income > 50000 || Purchased == true)

Filter(Age >= 30 && Age <= 45 && Income > 50000 && Purchased)

Filter(toInteger(Age) >= 30 && toInteger(Age) <= 45 && Income > 50000 && Purchased == true)

Filter(Age >= 30 && Age <= 45 || Income > 50000 && Purchased == true)

Working with a dataset named "CustomerData" with columns: Age, Income, and Purchased, how can we filter this dataset to include customers who are aged between 30 and 45, have an income greater than 50000, and have made a purchase? 

Choose the correct filter functions from the options below:

Working with a dataset named “CustomerData” with columns: Age, Income, and Purchased, how can we filter this dataset to include customers who are aged between 30 and 45, have an income greater than 50000, and have made a purchase?
Choose the correct filter functions from the options below:

It captures employees aged 25 or 35 in any department.

It captures employees aged between 25 and 35 in the Sales department.

It captures employees aged 35 or above in the Sales department.

Throws an exception for the incorrect syntax of filtering.

Consider the following filter expression applied to a dataset named "EmployeeData":

 Filter(toInteger(Age) >= 25) && Filter(toInteger(Age) <= 35 && Department == 'Sales')

What does this filter expression achieve for the "EmployeeData" dataset?

Consider the following filter expression applied to a dataset named “EmployeeData”:
<pre><code>Filter(toInteger(Age) &gt;= 25) &amp;&amp; Filter(toInteger(Age) &lt;= 35 &amp;&amp; Department == 'Sales')
</code></pre>
What does this filter expression achieve for the “EmployeeData” dataset?

Test your understanding of machine learning pipelines in Azure Data Factory.

Getting Started

Introduction to Azure Data Factory

Setting Up an Azure Data Factory Environment

Data Connectivity and Management

Azure Data Factory: Introduction and Connectivity Exam

Creating Data Pipelines in Azure Data Factory

Managing and Monitoring Azure Data Factory Pipelines

Azure Data Factory: Designing and Maintaining Data Pipelines Exam

Big Data Integration and Processing

Machine Learning and Advanced Analytics

Azure Data Factory: Big Data Processing and Machine Learning Exam

Data Governance and Security

Azure Data Factory: Best Practices

Conclusion

Appendix

Quiz: Machine Learning and Advanced Analytics