Top Azure Data Factory Interview Questions: Prepare for Your Next Job Interview

Get ready for your Azure Data Factory interview with our comprehensive guide. Learn about key features, data integration types, data sources and destinations, pipeline creation, data transformation, scheduling, monitoring, and security. Discover best practices for using Azure Data Factory to manage data pipelines in the cloud and ace your interview with confidence.

Users can create, schedule, and orchestrate data workflows using Azure Data Factory, a cloud-based data integration service. It enables the extraction, transformation, and loading (ETL) of data from various sources, such as databases, cloud storage, and on-premises systems, into a central repository, such as Azure Data Lake Storage or Azure Blob Storage. With Azure Data Factory, users can easily build, deploy, and manage data pipelines at scale, enabling efficient data processing and analysis.
Now let’s take a look at a couple of frequently asked interview questions associated with Azure Data Factory.

Ques-1: Why do we need Azure Data Factory?

Here are some of the key reasons why organizations need Azure Data Factory:

• Data Integration: Azure Data Factory provides a unified platform to connect to and integrate data from various sources such as databases, cloud storage, and on-premises systems. It enables efficient extraction, transformation, and loading (ETL) of data into a central repository for further processing and analysis.
• Scalability: Azure Data Factory is built on cloud-based services and storage, providing scalability to handle large and diverse datasets. It enables users to scale up or down their data processing needs based on the workload without having to invest in costly infrastructure.
• Cost-Effectiveness: Azure Data Factory eliminates the need for extensive coding and scripting, reducing development time and cost. It also allows users to take advantage of cloud-based services and storage, reducing the need for on-premises hardware and software.
• Automation: Azure Data Factory enables users to create and schedule data workflows, automating data processing and reducing the risk of errors. It also provides a single platform to manage and monitor data pipelines, ensuring data quality and consistency.
• Integration with other Azure Services: Azure Data Factory seamlessly integrates with other Azure services, such as Azure Data Lake Storage and Azure Databricks, enabling users to take advantage of the full suite of Azure data analytics tools.

Ques-2: What is the integration runtime in Azure Data Factory and its Types?

Integration Runtime is a component of Azure Data Factory that enables data integration across on-premises and cloud-based systems. It provides a secure and efficient way to move data between different data stores, enabling seamless data integration across different environments.

There are three types of Integration Runtimes in Azure Data Factory:

• Azure Integration Runtime: This type of Integration Runtime is fully managed by Azure and is used for data integration between cloud-based data stores such as Azure SQL Database, Azure Data Lake Storage, and Azure Blob Storage.

• Self-hosted Integration Runtime: This type of Integration Runtime is installed on a virtual machine or an on-premises machine within the organization’s network. It is used for data integration between on-premises systems and cloud-based data stores.

• Azure-SSIS Integration Runtime: This type of Integration Runtime is designed specifically for SQL Server Integration Services (SSIS) packages, enabling users to run their SSIS packages in Azure Data Factory without the need for extensive changes to their existing SSIS packages.

The Azure Integration Runtime and the Self-hosted Integration Runtime can be further classified into three subtypes based on their deployment:

• Automatic Deployment: This type of deployment is used for the Azure Integration Runtime and enables automatic provisioning and configuration of the Integration Runtime.
• Self-hosted (with Gateway) Deployment: This type of deployment is used for the Self-hosted Integration Runtime and requires a gateway to be installed on a machine within the organization’s network to facilitate communication between the Integration Runtime and the on-premises systems.

• Self-hosted (without Gateway) Deployment: This type of deployment is also used for the Self-hosted Integration Runtime and does not require a gateway. Instead, the Integration Runtime is installed directly on a machine within the organization’s network and communicates directly with the on-premises systems.

Ques-3: What is the limit on the number of integration runtimes in Azure Data Factory?

In Azure Data Factory, there is no limit on the number of integration runtimes that can be created. Users can create as many integration runtimes as they need to meet their data integration requirements.

Ques-4: What is the Difference Between Azure Data Lake and Azure Data Warehouse?

The below table lists the key differences between Azure Data Lake and Azure Data Warehouse:

Criteria	Azure Data Lake	Azure Data Warehouse
Purpose	Designed for big data processing and analytics	Designed for analytical workloads
Storage	Supports unstructured, semi-structured, and structured data	Supports structured data only
Scalability	Scales horizontally, enabling unlimited storage capacity	Scales vertically, enabling high compute capacity
Query Performance	Provides low latency querying with distributed analytics	Provides high performance with optimized query processing
Cost	Low cost per GB stored	Higher cost per GB stored
User Interface	Primarily used through APIs	Provides a user interface for business intelligence (BI)

Ques-5: What is Blob Storage in Azure?

In Azure, Blob storage is a type of cloud storage that allows users to store unstructured data such as text, binary data, images, and video as blobs. We can access these from anywhere in the world via HTTP or HTTPS. Blob storage is designed to provide high availability, durability, and scalability to meet the needs of modern cloud applications. It is commonly used for a variety of purposes, such as data backup and recovery, application data storage, and media storage and delivery.

Ques-6: What is the Difference Between Azure Data Lake store and Blob storage?

Criteria	Azure Data Lake Store	Blob Storage
Purpose	Designed for big data analytics and processing	Designed for unstructured data storage
Data Type Support	Supports unstructured, semi-structured, and structured data	Supports unstructured data only
Performance	Optimized for parallel processing and large-scale analytics	Not optimized for parallel processing or large analytics
Access control	Provides granular access control with POSIX-compliant ACLs	Provides basic access control with role-based access control (RBAC)
CostDesigned for big data analytics and processing	Higher cost per GB stored	Lower cost per GB stored

Ques-7: What are the steps for creating ETL process in Azure Data Factory?

Follow the below steps to create an ETL process in Azure Data Factory:

• Create an Azure Data Factory instance.
• Create an integration runtime.
• Define linked services for your input and output data sources.
• Create datasets to represent the input and output data.
• Create a pipeline to encapsulate your ETL process.
• Add activities to the pipeline to define the ETL process.
• Configure the activities to define their inputs and outputs.
• Schedule the pipeline to run at a specific time or interval.
• Monitor the pipeline to ensure that it completes successfully.

Ques-8: What is the Difference Between HDInsight & Azure Data Lake Analytics?

Features	HDInsight	Azure Data Lake Analytics
Primary use Case	Hadoop and Spark processing	Data processing and analysis
Query Language	Hive, Pig, HBase, and Spark	U-SQL
Cluster Management	Fully Managed	Severless
Data Storage	Can use various storage options, including Azure Data Lake Store, Blob storage, and HDFS	Uses Azure Data Lake Store
Cost Model	Pay per hour for the size of the cluster, with options for on-demand and reserved instances	Pay per job based on the amount of data processed
Integration with Services	Integrates with various Azure services, including HDInsight, Azure Storage, and Azure SQL Database	Integrates with various Azure services, including Azure Data Lake Store, Azure Blob storage, and Azure Event Hubs
Developer Tools	Supports various development environments and languages, including Eclipse, IntelliJ, and Visual Studio	Supports Visual Studio, Visual Studio Code, and Azure portal
Job Orchestration	Supports Oozie, which allows for workflow scheduling and coordination	Uses Azure Data Factory for job orchestration and scheduling

Ques-9: What are the top-level Concepts of Azure Data Factory?

The top-level concepts of Azure Data Factory are:

• Pipeline: A pipeline is a logical grouping of activities that perform a specific task. It defines the order in which the activities should be executed.
• Activity: An activity represents a unit of work that is performed in a pipeline. It can be a data movement activity, a data transformation activity, or a control activity.
• Dataset: A dataset represents a data structure that is used as input or output by an activity. It defines the data schema, location, and format.
• Linked Service: A linked service is a configuration that defines the connection information for a data store or a compute resource. It includes information such as the connection string, authentication, and other properties required to connect to the data store or compute resource.
• Trigger: A trigger is a time-based or event-based mechanism that starts the execution of a pipeline. It is used to schedule or initiate the execution of a pipeline based on a specific condition or event.

Ques-10: How to schedule a pipeline in Azure Data Factory?

To schedule a pipeline in Azure Data Factory, follow these steps:

• Navigate to the “Author & Monitor” section of your Azure Data Factory instance.
• Open the pipeline that you want to schedule.
• Click on the “Add trigger” button in the top-right corner of the page.
• Choose the type of trigger you want to use, such as a “Recurrence” trigger or a “Tumbling Window” trigger.
• Configure the trigger settings, such as the start time and interval for recurrence or the window size and offset for the tumbling window.
• Save the trigger and publish your changes.
• The pipeline will now run according to the schedule you have set.

Note: You can also use external triggers like Azure Event Grid or Azure Logic Apps to trigger pipelines.

Ques-11: Can we pass parameters to a pipeline run?

Yes, you can pass parameters to a pipeline run in Azure Data Factory.

To pass parameters, you need to create a pipeline parameter in your pipeline and then pass the parameter value when you trigger the pipeline. You can pass parameter values from a trigger, a pipeline REST API call, or from another pipeline using the “Execute Pipeline” activity.

Here are the steps to pass parameters to a pipeline run in Azure Data Factory:

• Create a pipeline parameter in your pipeline by defining it in the “Parameters” tab of your pipeline.
• Use the parameter in your pipeline by referencing it in your activities using the format “@pipeline().parameters.parameter_name”.
• When triggering the pipeline, pass the parameter value by specifying it in the “Parameter” section of your trigger, API call, or “Execute Pipeline” activity.

That’s it! Your pipeline will now run with the parameter value you have specified.

Ques-12: Can we define default values for the pipeline parameters?

Yes, you can define default values for pipeline parameters in Azure Data Factory.

To define a default value for a pipeline parameter, follow these steps:

• Open the pipeline in the Azure Data Factory Designer.
• Click on the “Parameters” tab.
• Select the parameter you want to set a default value for.
• In the “Default Value” field, enter the default value you want to use.
• Save and publish your changes.

Now, when you trigger the pipeline, if a value for the parameter is not provided, the default value will be used. If a value is provided, the provided value will override the default value.

Defining default values for pipeline parameters can save time and effort by reducing the need to specify the same values repeatedly for each pipeline run.

Ques-13: Can an activity in a pipeline consume arguments that are passed to a pipeline run?

Yes, an activity in a pipeline can consume arguments that are passed to a pipeline run in Azure Data Factory.

To consume pipeline arguments in an activity, you need to define the arguments as pipeline parameters and then reference them in your activity using the format “@pipeline().parameters.parameter_name”.

Here are the steps to consume pipeline arguments in an activity:

• Define the arguments as pipeline parameters in the “Parameters” tab of your pipeline.
• Reference the parameters in your activity by using the “@pipeline().parameters.parameter_name” syntax.
• When triggering the pipeline, pass the argument values as parameters in the “Parameter” section of your trigger, API call, or “Execute Pipeline” activity.

Once the argument values are passed to the pipeline run, the activity can consume the values of the arguments by referencing the corresponding parameters. This can be useful for passing dynamic values to the activities in your pipeline, such as connection strings, file paths, or database credentials.

Ques-14: Can an activity output property be consumed in another activity?

Yes, an activity output property can be consumed in another activity in Azure Data Factory.

When an activity runs, it can produce an output that can be used as input for another activity. The output of one activity is stored in the output property of that activity and can be referenced in subsequent activities in the pipeline.

To consume an activity output property in another activity, you need to reference the activity output using the “@activity(‘activity_name’).output.property_name” syntax.

Here are the steps to consume an activity output property in another activity:

• Define the output property in the activity that produces the output.
• Reference the output property in the subsequent activity using the “@activity(‘activity_name’).output.property_name” syntax.
• Use the output property value in the subsequent activity as needed.

By consuming the output of one activity in another activity, you can create more complex pipelines that process and transform data in multiple steps. This can be especially useful for data integration and ETL workflows, where data needs to be transformed and processed before it can be loaded into a target system.

Ques-15: How do we handle null values in an activity output?

Handling null values in an activity output is important to ensure that subsequent activities in the pipeline do not encounter errors or produce unexpected results.

Here are some ways to gracefully handle null values in an activity output in Azure Data Factory:

• Use conditional logic: Use the “if” and “else” conditions in subsequent activities to check for null values in the output of the previous activity. If a null value is encountered, you can choose to skip the activity or provide a default value.
• Use the coalesce function: Use the coalesce function to replace any null values with a default value. The coalesce function returns the first non-null value in a list of expressions, allowing you to provide a default value if the original value is null.
• Use the null function: Use the null function to test for null values in the activity output. The null function returns a Boolean value indicating whether the specified expression is null, allowing you to branch the pipeline based on whether the output is null or not.

By handling null values in your activity output, you can ensure that subsequent activities in the pipeline are executed correctly and that the pipeline produces the expected results.

Ques-16: Which Data Factory version do we use to create data flows?

To create data flows in Azure Data Factory, you need to use version 2 of the Data Factory service.

Version 2 of Azure Data Factory provides a code-free, visual environment for creating and managing data integration workflows. It includes the Data Flow feature, which allows you to create data transformation logic using a drag-and-drop interface without requiring any coding.

Ques-17: What has changed from private preview to limited public preview in regard to data flows?

There were several changes that occurred from the private preview to the limited public preview of data flows in Azure Data Factory. Here are a few key changes:

• User Interface: The user interface for building data flows has been significantly improved and refined, with a new design that makes it easier to use.

• Data Sources and Sinks: In the private preview, data flows supported a limited set of data sources and sinks. However, in the limited public preview, data flows now support a much wider range of data sources and sinks, including various file formats, databases, and cloud storage services.
• Performance and Scalability: The performance and scalability of data flows has been greatly improved in the limited public preview, with the ability to handle larger data volumes and support for parallel execution of data transformation logic.
• Debugging and Monitoring: The limited public preview includes new debugging and monitoring features that provide greater visibility into the execution of data flows, allowing you to identify and resolve issues more easily.

Overall, the limited public preview of data flows in Azure Data Factory represents a significant improvement over the private preview, with a more robust and feature-rich offering that can handle a wider range of data integration scenarios.

Ques-18: Explain the two levels of security in ADLS Gen2?

Azure Data Lake Storage Gen2 (ADLS Gen2) provides two levels of security to protect your data:

• Access Control: The first level of security in ADLS Gen2 is access control, which is used to control who has access to your data. You can define access control lists (ACLs) on directories and files in ADLS Gen2, allowing you to grant or deny access to specific users or groups. Additionally, you can use role-based access control (RBAC) to assign permissions to users or groups at the storage account level, allowing you to control access to the entire storage account.
• Data Encryption: The second level of security in ADLS Gen2 is data encryption, which is used to protect your data from unauthorized access. ADLS Gen2 encrypts data at rest using Azure Storage Service Encryption (SSE), which encrypts data before it is written to disk and decrypts it when it is read back. Additionally, you can use client-side encryption to encrypt data before it is uploaded to ADLS Gen2, providing an additional layer of security.

Ques-19: How many types of triggers are supported by Azure Data Factory?

Azure Data Factory supports several types of triggers that can be used to start pipeline runs automatically. Here are the trigger types supported by Azure Data Factory:

• Schedule Trigger: This type of trigger runs a pipeline on a schedule, such as every hour, day, or week.

• Tumbling Window Trigger: This trigger type is used to run a pipeline on a recurring basis, such as every 15 minutes or every hour.

• Event-based Trigger: This type of trigger starts a pipeline when a specific event occurs, such as a file being added to a folder or a new message being posted to a queue.

• Manual Trigger: This type of trigger allows you to start a pipeline manually, either through the Azure portal or programmatically using the Azure Data Factory REST API.

• External Trigger: This type of trigger allows you to start a pipeline run externally, using a webhook or an Azure Logic App.

Ques -20: What are the different rich cross-platform SDKs for advanced users in Azure Data Factory?

Azure Data Factory provides several rich cross-platform software development kits (SDKs) for advanced users who want to programmatically manage their data integration workflows. Here are some of the SDKs available in Azure Data Factory:

• .NET SDK: This SDK provides a .NET library for interacting with Azure Data Factory and can be used with .NET languages such as C# and Visual Basic.

• Python SDK: This SDK provides a Python library for interacting with Azure Data Factory and can be used with Python 2.7, 3.5, 3.6, and 3.7.

• REST API: Azure Data Factory also provides a REST API that allows you to programmatically manage your data integration workflows using HTTP requests.

• Azure PowerShell: This SDK provides a PowerShell module that allows you to manage Azure resources, including Azure Data Factory, from the command line.

• Azure CLI: This SDK provides a cross-platform command-line interface that allows you to manage Azure resources, including Azure Data Factory, from the command line.

Conclusion

This article lists out some of the key interview questions associated with Azure Data Factory. Hope this helps you. Best of luck.