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

Azure SQL Database is a fully managed service, while Azure SQL Managed Instance provides more control over the database.

Azure SQL Database allows cross-database querying, while Azure SQL Managed Instance does not.

Azure SQL Managed Instance is less expensive than Azure SQL Database.

Azure SQL Database supports Always Encrypted, while Azure SQL Managed Instance does not.

What is the difference between Azure SQL Database and Azure SQL Managed Instance?

What is the difference between Azure SQL Database and Azure SQL Managed Instance?

Saving as a text file to the Hadoop Distributed File System (HDFS) for later processing within the same Spark cluster

Saving as a text file to a remote FTP server for external data sharing and analysis

Saving as a text file to a local file system directory for subsequent access by a different Spark job

Saving as a text file to Azure Blob Storage to store the output of a Spark DataFrame transformation

What is the purpose of the following command in the Spark script?
```text
counts.saveAsTextFile("wasbs://adftutorial@azeduhdinsighhdistorage.blob.core.windows.net/spark/outputfiles/wordcount")
```


What is the purpose of the following command in the Spark script?
<pre><code class="language-text">counts.saveAsTextFile(&quot;wasbs://adftutorial@azeduhdinsighhdistorage.blob.core.windows.net/spark/outputfiles/wordcount&quot;)
</code></pre>

Which type of storage is used by Hive?

The Hadoop cluster is created successfully.

An invalid storage account key error occurrs.

Authentication fails for the HTTP password.

The Hadoop cluster creation fails due to an invalid location.

Consider the following Azure CLI command used to create an HDInsight Hadoop cluster:

    az hdinsight create --name "az-edu-hdinsight-cluster" --resource-group "az-edu-hdinsight-rg" --type hadoop --http-password "tESt123##654gcb" --location eastus --storage-account "azhdinsight890" --storage-account-key $HD_AZURE_STORAGE_KEY --storage-container "hdinsight-az-container"

What is the expected output of this command?


Consider the following Azure CLI command used to create an HDInsight Hadoop cluster:
<pre><code>az hdinsight create --name &quot;az-edu-hdinsight-cluster&quot; --resource-group &quot;az-edu-hdinsight-rg&quot; --type hadoop --http-password &quot;tESt123##654gcb&quot; --location eastus --storage-account &quot;azhdinsight890&quot; --storage-account-key $HD_AZURE_STORAGE_KEY --storage-container &quot;hdinsight-az-container&quot;
</code></pre>
What is the expected output of this command?

**(Select all that apply.)** When designing a Hive transformation in Azure Data Factory, which data format is supported for input and output data?



(Select all that apply.) When designing a Hive transformation in Azure Data Factory, which data format is supported for input and output data?

The resource group `az-edu-hdinsight-rg` is deleted successfully.

Confirmation prompt deletes the resource group.

An error indicates that the resource group is not empty.

The resource group is deactivated but not deleted.

Consider the following Azure CLI command used to delete a resource group:

    az group delete --name az-edu-hdinsight-rg --yes

What is the expected output of this command?


Consider the following Azure CLI command used to delete a resource group:
<pre><code>az group delete --name az-edu-hdinsight-rg --yes
</code></pre>
What is the expected output of this command?

Which Spark component is used for in-memory data processing?

Which Spark component is used for in-memory data processing?

  
    CREATE TABLE EmployeeData ( employee_id INT,
    employee_name STRING,
    salary DOUBLE);


  
    CREATE EXTERNAL TABLE EmployeeData ( employee_id INT,
    employee_name STRING,
    salary DOUBLE) STORED AS PARQUET;


  
    CREATE TABLE EmployeeData ( employee_id INT,
    employee_name STRING,
    salary DOUBLE) EXTERNAL LOCATION '/data/employee';


  
    CREATE EXTERNAL TABLE EmployeeData ( employee_id INT,
    employee_name STRING,
    salary DOUBLE) LOCATION '/data/employee';


Which command in an HQL script is used to create an external table named EmployeeData in Hive?

Which command in an HQL script is used to create an external table named EmployeeData in Hive?

Which services can be used to create an HDInsight cluster?

Which services can be used to create an HDInsight cluster?

Integrated with various big data technologies

Faster processing speeds compared to traditional relational databases

**(Select all that apply.)** What are the advantages of using HDInsight for big data processing in Azure? 



(Select all that apply.) What are the advantages of using HDInsight for big data processing in Azure?

Test your understanding of big data concepts for 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: Big Data Integration and Processing