Azure Data Factory is one of Microsoft Azure’s leading services for working with data of any size or format at scale. Its intuitive and data engineer-friendly interface allows anyone to interact efficiently with large amounts of data. This article will cover the top 25 Azure Data Factory interview questions for all experience levels, including beginners, experienced, and advanced candidates. In addition, we will provide example responses to assist you in preparing for your following interview.
1. Why Are You Interested In Working For Our Organization In This Position?
I’ve dug into the philosophies that guide your marketing team’s work. My impression is that your company values the concept of teamwork to cultivate a more collaborative thought pool. Because I do my best work in an environment that encourages open communication, lack of judgment, and collaboration with coworkers, I am grateful for the existence of this group attitude.
2. Why Did You Decide To Pursue A Career In This Field?
When I was 12, I considered getting a paper route to earn money for a school field trip. My dad said no. I was responsible for providing him with a report detailing how much money I would make, how long it would take, and the reasons why the sacrifices, such as not being able to sleep in, would be worthwhile. As a result of that procedure, I developed an interest in data analysis.
3. What Would Be Your Ideal Working Environment?
My ideal working environment would be one in which I would be given the freedom to finish my work at my own pace and on schedule. I enjoy working with others, but I don’t particularly enjoy being micromanaged. I believe that I do my finest work when I am allowed room to grow and develop.
4. Do You Get Along Well With Others When You’Re Working Together?
I believe that I perform the most incredible work when I collaborate with others because they always provide me with a perspective that I would not have been able to develop alone. In most cases, I believe people perform better professionally when working together as a cohesive team. That style of working together as a team is fun for me.
5. What Is Your Definition Of Effective Communication?
In a broader sense, I would characterize effective communication as a conversation between two or more individuals that concludes with all participants leaving the exchange with a shared comprehension of what was stated. An equal amount of time is spent listening and speaking to ensure that everyone feels like they have been heard.
6. Please Share An Experience With Us Where You Could Not Make A Deadline.
When I was working at my former company, the team I led had difficulty locating data from specific sources to carry out an environmental impact analysis. I got in touch with the customer and described the situation, including why we were having difficulty and what steps we were taking to address the issue. Because it was still very early in the process, the extension that I requested for one week was granted to me.
7. What Are Some Of The Challenges You Frequently Face While Performing Your Job Duties In This Field?
While working in data analysis, one of the most typical challenges that my coworkers and I have is sifting through mountains of data that are irrelevant or of no value to identify one or two pieces of beneficial information. Employing automatic organization systems that provide alerts whenever relevant data or patterns arise is one approach that I take to resolve this challenge in the field of data analysis.
8. What Exactly Is Azure’s Data Factory?
Microsoft provides a solution known as Azure Data Factory that combines integration and ETL functions. You can construct processes driven by data to automate and choreograph the movement of data. We can transform the data as it is being stored in the cloud. It allows you to design and operate data pipelines that can assist in moving and changing data as well as running channels on a schedule.
9. Why Is It Necessary For Us To Use Azure Data Factory?
As the world transitions to the cloud and big data, data integration and migration continue to be essential to businesses across all industries. By focusing on the data and designing, monitoring, and managing the ETL / ELT pipeline from a single perspective, ADF facilitates the efficient resolution of both difficulties. The reasons for Azure Data Factory’s increasing popularity are:
- Enhanced value
- Enhanced outcomes of business processes
- Decreased overhead expenses
- Enhanced choice making
- Enhanced business process adaptability
10. What Is Blob Storage In Azure?
Blob storage is a type of storage that Microsoft developed explicitly to store large amounts of unstructured data like text, photos, and binary data. It helps make your data accessible to the public all over the world. The most typical applications for blob storage include:
- Streaming audio and video files.
- Storing data for backup and analysis.
- Similar uses.
Utilizing blob storage for data analytics work can also be done with data lakes.
11. Is It Feasible To Get A Value For A New Column In Adf Mapping From One Of The Existing Columns In The Database?
We can use the derive transformation in the mapping data flow to build a new column depending on the desired logic. When constructing a derived column, you may either create a new derived column or modify an existing one. Input the name of the new column into the Column textbox.
Using the column selector, you can replace an existing column in your schema. Click the Enter expression textbox to create the derived column’s expression. You may either enter your expression manually or use the expression builder to develop your reasoning.
12. What Is The Definition Of Integration Runtime?
Integration runtime is a computational infrastructure that Azure Data Factory uses. It gives features for integrating diverse network environments. The many Types of Integration Runtimes consist of:
- Azure Integration Runtime – Capable of copying data between cloud data stores and sending activities to various computing services such SQL Server, Azure HDInsight, etc.
- Self-Hosted Integration Runtime – This is essentially the same software as the Azure Integration Runtime, but it is deployed on your local system or virtual machine across a virtual network.
- Azure SSIS Integration Runtime – This component enables the execution of SSIS packages in a managed environment. Therefore, Azure SSIS Integration Runtime is used while lifting and shifting SSIS packages to the data factory.
13. What Are ARM Templates In Azure Data Factory? What Purpose Do They Serve?
An ARM template is a JSON (JavaScript Object Notation) file that specifies the data factory pipeline’s infrastructure and configuration, including pipeline activities, related services, datasets, etc. The template will contain nearly identical code to that of our channel. ARM templates are helpful when migrating pipeline code from Development to higher environments, such as Production or Staging, once we have confirmed that the code is functioning appropriately.
14. What Distinguishes Azure Data Factory From Other ETL Tools?
Azure Data Factory distinguishes itself from other ETL tools by providing: –
- Enterprise Readiness: Cloud-Scale Data Integration for Big Data Analytics!
- Enterprise Data Readiness: More than 90 connectors are available to transfer data from various sources to the Azure cloud.
- Code-Free Transformation: mapping dataflows driven by an interface.
- Capability to Execute Code on Any Azure Compute: Manual data transformations
- Three-step ability to rehost on-premises services on Azure Cloud: Numerous SSIS programs operate on Azure cloud.
- Making DataOps seamless: Integrating Source control, automated deployment, and simple templates into DataOps.
- Managed virtual networks defend against data exfiltration, which in turn, simplifies your networking.
15. What Are The Many Ways To Execute Azure Data Factory Pipelines?
There are three methods for executing a pipeline in Data Factory:
- Debug mode is useful while testing pipeline programming and serves as a tool for testing and debugging our code.
- Manual Execution occurs when the ‘Trigger now’ option in a pipeline is clicked. It is useful if you intend to run your pipelines ad hoc.
- A Trigger allows us to schedule our pipelines at predetermined periods and intervals. As we will see later in this post, Data Factory supports three types of triggers.
16. What Are The Several Stages That Make Up The ETL Process?
The ETL (Extract, Transform, Load) process consists of the following four steps:
- Connect and Collect: Connect to the data source or sources and transfer data to local and crowdsourced data storage.
- Transforming data utilizing computer services such as HDInsight, Hadoop, Spark, etc.
- Publish: To load data into Azure data lake storage, Azure SQL data warehouse, Azure SQL databases, Azure Cosmos DB, and other Azure services.
- Azure Data Factory includes built-in support for pipeline monitoring via Azure Monitor, API, PowerShell, Azure Monitor logs, and Azure portal health panels.
17. What Do Variables In The Azure Data Factory Entail?
In the Azure Data Factory pipeline, variables provide the capability to store values. They serve the same purpose as variables in any programming language and are accessible within the channel. Set Variable and add variable are operations used to set or manipulate the variable values. A data factory contains two distinct types of variables:
- System variables: These are fixed Azure pipeline variables. We mostly require these to retrieve system information that may be necessary for our use case.
- User variable: A user variable is manually declared in your code per your pipeline logic.
18. What Exactly Is Meant By “Mapping Data Flows”?
Data flows allow engineers to develop logic for data transformation without writing code. The generated data flows are executed as activities within Azure Data Factory pipelines that employ Apache Spark clusters with a scalability factor. Data flow operations can be operationalized using the current scheduling, control flow, and monitoring capabilities of Azure Data Factory. Mapping data flow provides an entirely visual experience without requiring any coding. For scalable data processing, ADF-managed execution clusters execute data flows. Azure Data Factory manages code translation, path optimization, and data flow task execution.
19. In The Azure Data Factory, What Is Copy Activity?
Copy is one of the most common and widely utilized operations in the Azure data factory. It is used for ETL (Extract, Transform, and Load) or Lifts and Shift, where data is moved from one data source to another. While copying the data, we can also change it. For instance, we read data from a txt/csv file that contains 12 columns, but we want to retain only seven columns when writing to our target data source. We can change it and deliver to the destination data source only the required amount of columns.
20. What Are The Various Azure Data Factory Actions You Have Utilized?
Among the Azure Data Factory activities I have utilized are the following:
- Copy Data Activity to copy data from one dataset to another.
- ForEach Activity for iteration.
- Get Metadata is an Activity that returns Metadata for any data source.
- Set Variable Activity to define and activate pipeline variables.
- Lookup Activity to retrieve values from a table or file using a lookup.
- Wait for Activity to wait for a specific amount of time before/during pipeline execution.
- The Validation Activity will verify the existence of files in the collection.
- Web Activity used within an ADF pipeline to call a custom REST endpoint.
21. How Do I Organize A Pipeline?
You can schedule a pipeline using either the time window or the scheduler trigger. The trigger utilizes a wall-clock calendar schedule to schedule pipelines periodically or in calendar-based recurrent patterns.
The service now supports three types of triggers:
- Tumbling window trigger: A trigger that works while retaining its state.
- Schedule Trigger: A trigger that executes a pipeline on a predetermined timetable.
- Schedule Trigger: A trigger that responds to a specific event. Including the placement of a file inside a blob.
- The relationship between pipelines and triggers is a many-to-many one. Multiple triggers may initiate a channel, or a single trigger may create various pipelines.
22. When Should Azure Data Factory Be Selected?
One should consider using Data Factory-
- When working with extensive data, they must establish a data warehouse; this may need the usage of a cloud-based integration solution such as ADF.
- Not all team members have coding skills and may prefer graphical tools for data manipulation.
- We would need a single analytics solution when raw business data is kept at many on-premises and cloud-based data sources.
- When one wants to utilize easily accessible data transfer and processing options, keep infrastructure administration to a minimum; therefore, a managed solution like ADF is preferable.
23. Is It Possible To Calculate A Value For A New Column In Adf By Mapping It To An Existing Column?
We can construct transformations in the mapping data flow to generate a new column depending on our desired logic. When developing a derived column, we can build a new one or edit an existing one. Input the name of the new column into the Column textbox. The column dropdown can replace a current column in your schema. Click the Enter expression textbox to create the derived column’s expression. You can manually enter or use the expression builder to construct logic.
24. How Can You Copy Data From Numerous Excel Sheets?
We can copy data using an Excel connector within a data factory and ensure the sheet name from which data is loaded and specified. This strategy is nuanced when dealing with the data of a single sheet or a small number of sheets. Still, dealing with many sheets becomes laborious because the sheet name is changed every time! However, we may utilize a data factory binary data format connector by pointing it to the Excel file and omitting the sheet names. We can use the copy activity to copy the data from every sheet in the file.
25. Which Three Tasks Can We Perform With Microsoft Azure Data Factory?
- Data movement activities: As the name suggests, data movement activities help move data from one location to another. For example, Copy Activity in Data Factory transfers data from a source data store to a sink data store.
- Data transformation activities: These activities assist in transforming the data as it is being loaded into its goal or destination. Such as Stored Procedure, U-SQL, and Azure Functions.
- Control flow operations: Control (flow) actions regulate the flow of any pipeline operation. For instance, the Wait activity causes the pipeline to wait for a particular period.
Conclusion
Microsoft Azure Data Factory has simplified data coordination between diverse relational and non-relational data sources. Currently, most organizations that have migrated to Azure Cloud use ADF. Therefore, it creates multiple opportunities for Data Engineers. Since most of you are interested in opportunities in this industry, we have selected the Top 25 ADF Interview Questions. These questions will undoubtedly give you an advantage during your ADF interview.