Ace Your Azure Data Factory Interview with These Top 20 Questions and Answers (2024)

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Are you preparing for an interview as an Azure Data Factory engineer? As businesses continue to embrace cloud computing and data-driven decision-making, the demand for skilled professionals in Azure Data Factory is skyrocketing. This powerful data integration service from Microsoft allows you to create data-driven workflows for orchestrating and automating data movement and data transformation across various data stores.

To help you stand out in your upcoming interviews, we’ve compiled a list of the top 20 Azure Data Factory interview questions and answers for 2022. These questions cover a wide range of topics, from basic concepts to advanced scenarios, ensuring that you’re well-prepared to showcase your expertise.

1. Why do we need Azure Data Factory?

Azure Data Factory is a cloud-based integration service that allows you to create data-driven workflows for orchestrating and automating data movement and transformation. It provides a robust solution for collecting raw business data from various sources, transforming it into usable information, and delivering it to the intended destinations.

2. What is Azure Data Factory?

Azure Data Factory is a cloud data integration service that allows you to create data-driven workflows (called pipelines) for orchestrating and automating data movement and data transformation at scale. These pipelines can ingest data from disparate data stores, process or transform the data using compute services like Azure HDInsight Hadoop, Spark, Azure Data Lake Analytics, and Azure Machine Learning, and then publish the output data to data stores like Azure Synapse Analytics for business intelligence (BI) applications to consume.

3. What is an Integration Runtime in Azure Data Factory?

An Integration Runtime (IR) is the compute infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments. There are three types of Integration Runtimes:

• Azure Integration Runtime: Can copy data between cloud data stores and dispatch activities to various computing services like Azure HDInsight.
• Self-Hosted Integration Runtime: A software installed on an on-premises machine or a virtual machine in a private network, enabling data movement between cloud and on-premises data stores.
• Azure-SSIS Integration Runtime: A fully managed cluster of Azure virtual machines dedicated to running SQL Server Integration Services (SSIS) packages in the cloud.

4. What is the limit on the number of Integration Runtimes in Azure Data Factory?

There is no hard limit on the number of Integration Runtime instances you can have in a Data Factory. However, there is a limit on the number of virtual machine (VM) cores that the Integration Runtime can use per subscription for executing SSIS packages.

5. What are the top-level concepts in Azure Data Factory?

The top-level concepts in Azure Data Factory are:

• Pipeline: A logical grouping of activities that performs a unit of work.
• Activity: A step in a pipeline that defines the actions to be performed on the data.
• Dataset: A named view or reference to the data you want to use in your activities as inputs or outputs.
• Linked Service: Information about the connection to the data source required by the Data Factory service to connect to the data resource.
• Trigger: An execution unit that defines the schedule for a pipeline to run.
• Integration Runtime: The compute infrastructure used by Azure Data Factory to provide data integration capabilities.

6. How can you schedule a pipeline in Azure Data Factory?

You can schedule a pipeline in Azure Data Factory using the following triggers:

• Schedule Trigger: Executes pipelines following a wall-clock schedule (e.g., hourly, daily, weekly).
• Tumbling Window Trigger: Executes pipelines on a periodic schedule with a retro-active fixed time window.
• Event-Based Trigger: Executes pipelines in response to an event, such as the arrival of a file in a blob storage account.

7. Can you pass parameters to a pipeline run in Azure Data Factory?

Yes, you can pass parameters to a pipeline run in Azure Data Factory. Parameters are first-class, top-level concepts in Data Factory, and you can define parameters at the pipeline level and then pass arguments when executing the pipeline run on-demand or by using a trigger.

8. Can you define default values for pipeline parameters in Azure Data Factory?

Yes, you can define default values for the parameters in pipelines in Azure Data Factory. This allows you to provide a fallback value if no value is explicitly provided during pipeline execution.

9. Can an activity output property be consumed in another activity in Azure Data Factory?

Yes, an activity output can be consumed in a subsequent activity within the same pipeline using the @activity construct. This allows you to chain activities together and pass data between them.

10. How do you handle null values in an activity output in Azure Data Factory?

You can use the @coalesce construct in expressions to handle null values in an activity output gracefully. The @coalesce function returns the value of the first non-null expression from a set of expressions.

11. Which version of Azure Data Factory do you use to create data flows?

You use Azure Data Factory version 2 (also known as ADF V2) to create data flows. Data flows are visually designed data transformation pipelines that allow you to develop transformation logic without writing code.

12. What is the difference between Azure Data Lake and Azure Data Warehouse?

13. What is Azure Blob Storage, and how is it used in Azure Data Factory?

Azure Blob Storage is a service for storing large amounts of unstructured object data, such as text or binary data. In Azure Data Factory, Blob Storage can be used for various purposes, including:

• Serving images or documents directly to a browser
• Storing files for distributed access
• Streaming video and audio
• Storing data for backup, restore, and archiving
• Storing data for analysis by an on-premises or Azure-hosted service

14. What are the steps to create an ETL (Extract, Transform, Load) process in Azure Data Factory?

Here are the typical steps to create an ETL process in Azure Data Factory:

1. Create a linked service for the source data store (e.g., SQL Server Database).
2. Create a linked service for the destination data store (e.g., Azure Data Lake Storage).
3. Create a dataset for the source data.
4. Create a dataset for the destination data.
5. Create a pipeline and add a Copy activity to move data from the source to a staging area.
6. (Optional) Add a Data Flow activity to transform the data.
7. Add another Copy activity to move the transformed data to the destination.
8. Schedule the pipeline by adding a trigger.

15. What is the difference between Azure HDInsight and Azure Data Lake Analytics?

16. What is the difference between Mapping Data Flow and Wrangling Data Flow in Azure Data Factory?

• Mapping Data Flow is a visually designed data transformation activity that allows users to design graphical data transformation logic without needing expert coding skills. It is executed as an activity within ADF pipelines on a fully managed, scaled-out Spark cluster.
• Wrangling Data Flow is a code-free data preparation activity integrated with Power Query Online, providing the Power Query M functions for data wrangling using Spark execution.

17. Is coding required for Azure Data Factory?

No, coding is not strictly required for Azure Data Factory. While you can use custom code activities for advanced scenarios, Azure Data Factory provides a code-free experience through its visual authoring tools and over 90 built-in connectors. You can create data integration workflows and transform data using mapping data flows without extensive programming skills or knowledge of Apache Spark clusters.

18. What has changed from the private preview to the limited public preview regarding data flows in Azure Data Factory?

Some key changes from the private preview to the limited public preview for data flows in Azure Data Factory include:

• Users no longer need to bring their own Azure Databricks clusters.
• Azure Data Factory will manage cluster creation and tear-down.
• Blob datasets and Azure Data Lake Storage Gen2 datasets are separated into delimited text and Apache Parquet datasets.
• Users can still use Data Lake Storage Gen2 and Blob Storage to store files, using the appropriate linked services.

19. How do you access data using the other 80+ dataset types in Azure Data Factory?

The mapping data flow feature in Azure Data Factory currently supports Azure SQL Database, Azure SQL Data Warehouse, delimited text files from Azure Blob Storage or Azure Data Lake Storage Gen2, and Parquet files from Blob Storage or Data Lake Storage Gen2 natively for source and sink. To access data from other dataset types, you can use the Copy activity to stage the data into a supported source, and then execute a Data Flow activity to transform the staged data.

20. What are the two levels of security in Azure Data Lake Storage Gen2?

The two levels of security in Azure Data Lake Storage Gen2 are:

1. Role-Based Access Control (RBAC): Includes built-in Azure roles (reader, contributor, owner) or custom roles to specify who can manage the service and access built-in data explorer tools.
2. Access Control Lists (ACLs): POSIX-compliant ACLs that specify which data objects a user can read, write, or execute (browse the directory structure). ACLs must be set for every object, and Azure Active Directory groups are typically used to manage data-level security.

By understanding and being prepared to answer these Azure Data Factory interview questions, you’ll be well-equipped to showcase your expertise and increase your chances of landing your dream job as an Azure Data Factory engineer.

Remember, preparation is key to acing any technical interview. Continue practicing, stay up-to-date with the latest Azure Data Factory developments, and don’t hesitate to seek guidance from experienced professionals or online resources whenever needed.

Good luck with your interviews!