Azure Data Factory is a Microsoft cloud-based solution that captures and turns raw corporate data into meaningful information. It's an ETL (extract, transform, and load) data integration service that automates the transformation of given raw data.
This Azure Data Factory Interview Questions blog lists the most common questions asked during Azure employment interviews. The questions you must prepare for are as follows:
Basic Azure Data Factory Interview Questions for Freshers
1. What is Azure Data Factory?
Ans. Azure Data Factory is like a digital manager that helps move and change data from your computer or the internet to where you want it to be. It is used to make data look better, move it around, and put it in the right places.
2. Why is Azure Data Factory important?
Ans. Azure Data Factory is essential because it acts as a super helpful assistant. It ensures information goes smoothly from your computer to the internet and back. It's good at organizing complex tasks with data. It can easily handle big loads of communication and tell you if something goes wrong. It also works well with other tools to help you understand information better.
3. How is Azure Data Lake Store different from Blob Storage?
Ans.
Feature
Azure Data Lake Store
Blob Storage
Use Case
Analyzing large datasets
Storing various types of data
Data Handling
Optimized for structured & unstructured data
Generally for unstructured data
Fine-grained Control
More control over data access
Limited control
Complexity
Can handle complex data scenarios
More straightforward usage
4. What do we understand by Integration Runtime?
Ans. Azure Data Factory's computational infrastructure is referred to as integration runtime. It can integrate across a variety of network environments. There are three types of Integration Runtime:
Azure Integration Runtime: It can replicate data between cloud data stores and communicate activities to SQL Server, Azure HDInsight, and other computing services.
Self-Hosted Integration Runtime: It's mainly the same software as the Azure Integration Runtime, except it's installed locally or on a virtual machine across a virtual network.
Azure SSIS Integration Runtime: It enables the execution of SSIS packages in a controlled environment. We use Azure SSIS Integration Runtime to lift and shift SSIS packages to the data factory.
5. Briefly explain the purpose of ADF Service.
Ans. Azure Data Factory (ADF) primarily orchestrates data copying across various relational and non-relational data sources, whether hosted in the cloud or on-premises. ADF can also be used to change the data ingested to match your business needs. Most Big Data systems use an ETL or ELT tool for data ingestion.
6. Write the limit on the number of integration runtimes?
Ans. In a data factory, there is no hard restriction on the amount of integration runtime instances you can have. However, the amount of Virtual Machine cores that the integration runtime can employ for SSIS package execution is limited per subscription.
7. What do you understand by Blob Storage in Azure?
Ans. Blob storage is made for storing large amounts of unstructured data like text, pictures, and binary data. It aids in making your data accessible to the general public on a worldwide scale. Blob storage is most commonly used to transmit audio and video and store data for backup and analysis. Blob storage can also be used to perform analytics using data lakes.
8. What is Mapping Data Flow?
Ans. Mapping Data Flow activity is a visually designed data transformation activity that permits us to construct graphical data transformation logic without having to be a programming expert, and it's run as an activity within the ADF pipeline on an ADF fully managed scaled-out Spark cluster.
9. What is the use of Azure Data Lake in Azure Data Factory (ADF)?
Ans. Azure Data Lake Storage Gen2 is a suite of big data analytics-focused features built into Azure Blob storage. Users can interact with data via the file system and object storage models. Azure Data Factory (ADF) is a fully managed cloud-based data integration service.
10. What is parameterization in Azure?
Ans. Parameterization in Azure allows us to dynamically provide the server name, database name, credentials, and so on when executing the pipeline, allowing us to reuse rather than build one for each request. In Azure Data Factory, parameterization is critical for effective design and reusability and lower solution maintenance costs.
11. What are Global parameters in Azure Data Factory?
Ans. Global parameters are constants that a pipeline can utilize in any expression in a data factory. It is handy when we have multiple pipelines with the same parameter names and values. We can alter these parameters in each environment when promoting a data factory utilizing the continuous integration and deployment process (CI/CD).
Intermediate Azure Data Factory Interview Questions
12. What is the use of datasets in the Azure Data Factory?
Ans. A dataset is a named view of data that links to or references data that may be used as inputs and outputs in activities. Tables, files, folders, and documents are examples of data storage employing datasets to identify data.
13. What is Copy activity in Azure Data Factory?
Ans. The Copy activity in Azure Data Factory can copy data between on-premises and cloud data stores (supported data stores and formats) and use the copied data in subsequent transformation or analysis actions.
Azure Data Factory uses the Integration Runtime (IR) as a secure compute architecture to run the copy activity across various network environments and ensure that it is performed in the region closest to the data storage.
14. Explain the difference between Azure Data Lake and Azure Data Warehouse?
Ans.
Data Lake
Data Warehouse
Data Lakes have the ability to store data in any format, size, or shape.
A Data Warehouse is a repository for data that has been filtered from a certain source previously.
Data is written in non-structured form, and you can define your schema in n number of ways (Schema on Read).
Data is written in a particular schema or structured form(Schema on write).
It is easily accessible and receives frequent changes.
Changing the Data Warehouse becomes a very strict and costly task.
It's an excellent tool for conducting in-depth research.
It is the finest platform for operational users.
15. What is Azure Datanricks?
Ans. Azure Databricks is an Azure-optimized Apache Spark-based analytics platform that is simple, fast, and collaborative. It is being developed in collaboration with the Apache Spark founders. Furthermore, Azure Databricks combines the best of Databricks and Azure to help clients accelerate innovation with a simple setup.
Data scientists, engineers, and business analysts can collaborate more easily thanks to smooth workflows and an austere environment.
16. Write the types of triggers supported by Azure Data Factory.
Ans. There are three types of triggers that Azure Data Factory supports:
Tumbling Window Trigger: The Azure Data Factory pipelines are executed at cyclic intervals by the Tumbling Window Trigger. It's also utilized to keep track of the pipeline's condition.
Event-based Trigger: The event-driven Trigger generates a response to any blob storage-related event. When you add or remove blob storage, they are formed.
Schedule Trigger: The Schedule Trigger executes the Azure Data Factory pipelines that follow the wall clock timetable.
17. How to schedule a pipeline?
Ans. The two ways to schedule a pipeline are:
We can utilize the scheduler or time frame triggers for scheduling a pipeline.
To schedule pipelines periodically or in calendar-based recurrent patterns, a trigger employs a wall-clock calendar schedule.
18. Is it possible for an activity within a pipeline to utilize arguments that have been passed to a pipeline run?
Ans. Yes, an activity within a pipeline can utilize arguments passed to a pipeline run. These arguments can be used to customize the behavior of the activity during runtime.
19. Define Azure SQL Data Warehouse.
Ans. It is a large database of data collected from various sources and used to make management decisions in a corporation. These warehouses allow you to collect data from multiple databases that are either distant or distributed.
Data from different sources can be combined to build an Azure SQL Data Warehouse, which can be used for decision-making, analytical reporting, and other purposes.
20. Explain data source in Azure Data Factory.
Ans. The data source is the system with the data to be used or processed on, whether a source or a destination system. Binary, text, CSV files, JSON files, and other data types can be used. It could be image files, video, or music, or it could even be a database.
Azure data lake storage, Azure blob storage, or any other database such as MySQL DB, Azure SQL database, Postgres, and so on are examples of data sources.
21. How is lookup activity useful in the azure data factory?
Ans. The Lookup activity in the ADF pipeline is frequently used for configuration lookup. It contains the original dataset. Furthermore, it retrieves data from the source dataset and provides it as the activity's output. In most cases, the lookup activity's output is used in the pipeline to make decisions or deliver a configuration.
22. What are variables in Azure Data Factory?
Ans. Variables in the ADF pipeline allow values to be temporarily stored. They are used in the same way as variables are used in programming languages. Two types of activities, Set Variable and Append Variable, are used to assign and manipulate variable values. There are two types of variables in Azure Data Factory:
System variable: These are the Azure pipeline's fixed variables. Pipeline id, pipeline name, trigger name, and so on are some of their instances.
User variable: Depending on the logic of the pipeline, user variables are manually declared.
23. What does it mean by the breakpoint in the ADF pipeline?
Ans. A breakpoint denotes the debug phase of the pipeline. The breakpoints can be used to examine the pipeline for any specified action. For example, suppose you have a pipeline with three activities and want to debug the second one. Place the breakpoint at the second activity to accomplish this. You can create a breakpoint by clicking the circle at the top of the activity.
24. What is a linked service in ADF?
Ans. The linked service in Azure Data Factory represents the connection system used to connect the external source. It holds the user validation information and serves as a connection string. There are two approaches to constructing a linked service:
The ARM template method
Making Use of the Azure Portal
25. What are Datasets in ADF?
Ans. The Dataset contains the data in the form of inputs and outputs that you will utilize in your pipeline activities. In general, datasets denote the data structure within linked data stores such as documents, files, and folders. An Azure blob dataset, for example, describes the blob storage folder and container from which a certain pipeline activity must read data as input for processing.
Azure Data Factory Interview Questions for Experienced
26. Explain the top-level concepts of Azure Data Factory?
Ans. One or more Azure Data Factory instances can be added to an Azure subscription (or data factories). Azure Data Factory comprises four core components that work together to build a platform for creating data-driven workflows, including data movement and transformation processes.
27. How can you make Azure functions?
Ans. An Azure Function is a cloud-based method for implementing minor function lines or code. You can use any programming language you choose with Azure Functions. A pay-per-use mechanism is used, with users only paying the first time they run the code. C#, F#, Java, Python, PHP, and Node.JS are just a few of the languages supported by functions.
Azure Functions also provides a consistent integration and deployment experience. By utilizing Azure Functions, businesses may create apps that do not require servers.
28. How can I access the data using the other 80 dataset types in the Data Factory?
Ans. Dataflow mapping allows you to create native build tools for source and receiver from Azure SQL databases, Azure Blob storage, data warehouses, or delimited text files in Azure Data Lake storage. You can declare data from one of the other connectors using a copy operation, then convert the data using a data stream operation.
29. What is Azure SSIS Integration Runtime?
Ans. Azure SSIS Integration is a fully managed cluster of virtual machines that runs SSIS packages in your data factory and is hosted in Azure. Scaling up SSIS nodes is as simple as configuring the node size, and scaling out is as simple as configuring the number of virtual machine cluster nodes.
30. What are the steps for creating an ETL process in Azure Data Factory?
Ans. Here are the steps for creating an ETL process in Azure Data Factory:
Set Up Data Factory: Create a new Azure Data Factory resource in the Azure portal.
Create Linked Services: To define connections to data sources and destinations.
Design Datasets: To describe the structure of your data sources and destinations.
Build Pipelines: To create a pipeline to organize your ETL workflow.
Add Activities: Inside the pipeline, add activities for data extraction, transformation, and loading.
Configure Activities: To specify each activity's settings, mappings, and transformations.
Set Dependencies: To define the order in which activities should run using dependencies.
Debug and Test: Use debugging tools to ensure your pipeline works as intended.
Publish and Trigger: Publish and trigger your pipeline manually or on a schedule.
Monitor and Optimize: To monitor pipeline performance using monitoring tools. Optimize and fine-tune for better efficiency if needed.
Iterate and Improve: Regularly review and enhance your ETL process based on changing requirements.
31. Is it feasible to calculate a value for a new column from the existing column from mapping in ADF?
Ans. You can derive transformation in the mapping data flow to create a new column based on the logic you want. When generating a derived column, you can either build a new one or update an existing one. In the Column textbox, type the name of the column you're creating.
32. What differences can be observed in data flows when comparing the private preview phase to the limited public preview phase?
During the private preview phase of Azure Data Factory, the service is accessible only to a selected group of customers, typically for testing and validation. In this phase, features might be limited, and the focus is on refining functionality based on feedback. In the limited public preview phase, Azure Data Factory becomes available to a broader audience but still with certain restrictions. Additional features and improvements might be introduced based on feedback from the private preview. The main differences between these phases typically include user base, feature availability, and the extent of testing and feedback incorporation.
33. Write the objective of Microsoft Azure's Data Factory service.
Ans. Data Factory's primary goal is to organize data copying between many non-relational and relational data sources stored locally within enterprise data centers or cloud platforms. In addition, Data Factory Service can be used to alter ingested data to achieve business goals. Data Factory Service is an ETL or ETL technology that facilitates data ingestion in a typical Big Data solution.
34. What are the two levels of security in ADLS Gen2?
Ans. The two levels of security in ADLS Gen2 are: Role-Based Access Control: It has built-in Azure rules for roles, including the reader, contributor, owner, and customer. There are two explanations for this. The first is who can operate the service themselves, and the second is to allow consumers to use built-in data explorer tools. Access Control List: Users can read, write, and execute specific data objects in Azure Data Lake Storage.
35. Can we pass parameters to a pipeline run?
Ans. Yes, we can certainly pass parameters to a pipeline run. Pipeline runs are the top-tier, first-class concepts in Azure Data Factory. We can define pipeline parameters and pass the arguments to run the pipeline.
Azure Data Factory Salary Trends
Azure Data Factory professionals are seeing increasing demand due to the growing reliance on cloud-based data integration solutions. Salaries for these roles vary based on factors such as experience, location, and the complexity of the projects handled. Entry-level positions typically start at around 15 lakh INR per year, while more experienced professionals can earn upwards of 30-40 lakh INR annually. In regions with a high cost of living or a high demand for cloud expertise, salaries can be significantly higher. The trend shows a steady increase as more organizations migrate to Azure, recognizing the importance of skilled professionals in ensuring successful data integration and management.
Azure Data Factory Roles & Responsibilities
Azure Data Factory (ADF) roles encompass a range of responsibilities aimed at building, managing, and optimizing data pipelines. Key roles include Data Engineers, who design and implement data workflows; Data Architects, who develop the overall data integration architecture; and Data Analysts, who use the data processed by ADF for insights. Responsibilities include creating data pipelines, managing data flows, ensuring data quality, and optimizing performance. These roles also involve collaborating with various stakeholders to understand data requirements and ensure that the data infrastructure supports business needs. Additionally, ADF professionals must stay updated with Azure’s latest features and best practices.
Azure Data Factory Job Trends
The job market for Azure Data Factory professionals is robust and growing. As more companies adopt cloud-based solutions for their data integration needs, the demand for ADF experts has surged. Job postings for roles such as Azure Data Engineer, Data Architect, and Cloud Data Specialist have seen a significant uptick. Industries like finance, healthcare, and retail are particularly active in seeking these skills, driven by the need to handle large volumes of data efficiently. The trend indicates a strong and sustained demand for ADF skills, with more organizations recognizing the value of efficient data pipelines and the expertise required to manage them.
Frequently Asked Questions
Is Azure Data Factory difficult?
Azure Data Factory's complexity depends on varies with familiarity. It is easy for basic things, but you must learn more to do very complicated stuff.
What is Azure Data Factory used for?
Azure Data Factory helps move and change data between different places. It's like a helper for organizing and analyzing data.
What kind of tool is Azure Data Factory?
Azure Data Factory is like a digital manager that helps move and organize data between different places in the cloud. It also helps with making data look better and moving it around.
Is Azure Data Factory PaaS or IAAS?
Azure Data Factory is Platform as a Service (PaaS), streamlining integration, transformation, and orchestration without infrastructure management.
Is Azure Data Factory Certification worth doing?
Yes, there is a massive demand for Azure Data Engineers who are Data Factory experts. Because many firms use Microsoft Azure as a cloud computing platform, skilled people are required to manage their operations.
Conclusion
We hope that this blog has helped you enhance your knowledge regarding the Azure Data Factory Interview Questions. Mastering Azure Data Factory (ADF) is increasingly essential for professionals in the data integration and management field. This blog has covered a range of interview questions, from basic concepts to advanced features, to help you prepare effectively for an ADF-related role. Understanding these questions and their answers will not only boost your confidence but also demonstrate your expertise in handling complex data workflows on the Azure platform.
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