Introduction As the demand for Azure Data Factory Engineers increases, one must be very keen during their interview preparation. In this blog, you will learn the top 20 Azure Data Factory interview questions and answers for 2024 for an in-depth grasp. Understand Azure Data Factory Azure Data Factory is a critical big data integration and transformation tool in the cloud. It provides the foundation for building a data-driven workflow for data orchestration and data workload automation in the cloud. With many companies adopting cloud-based solutions for data processing, an Azure Data Factory Engineer becomes very important in an organization. Cracking an interview for such a position requires understanding fundamental concepts, practical applications, and advanced features of ADF. This guide presents the most likely questions you might encounter in an interview and clear, concise answers to help you prepare effectively. 1. Why do we need Azure Data Factory? Azure Data Factory does not store any data itself. It allows you to create workflows that orchestrate the movement of data between supported data stores and data processing. You can monitor and manage your workflows—both via programmatic and UI mechanisms. It is the best available tool for ETL (Extract, Transform, Load) processes with an easy-to-use interface, so that is why I believe it's necessary. 2. What is Azure Data Factory? Azure Data Factory is a service developed by Microsoft that is generally a cloud-based data integration service. It is used to create and schedule data-driven workflows, also known as pipelines, move data between supported data stores, and process or transform data. 3. What is Integration Runtime? It is the computing infrastructure for Azure Data Factory that provides different types of integration capabilities across the network environment. It includes different types of the following: Azure Integration Runtime: Data is copied from cloud sources. Self-Hosted Integration Runtime: Data is copied from on-premises and internet sources. Azure SSIS Integration Runtime: Implemented for the execution of SSIS packages. 4. What is the limit of the number of integration runtimes? There is no specified limit on the number of integration runtime instances. Still, there is a limit on the number of VM cores to be used by the Integration Runtime for SSIS package execution per subscription. 5. What are the different components of Azure Data Factory? The components of Azure Data Factory are as follows: Pipeline: A pipeline is a logical grouping of activities. Activity: Activity is just a step that measures the execution of the data factory pipeline. Dataset: A dataset represents a data structure within the data factory. Mapping Data Flow: Mapping data flow is the UI logic for data transformation. Linked Service: A linked service is an abstract or declarative connection to the data source. Trigger: It helps to schedule when the pipeline will execute its functionalities. Control Flow: It is used by the executable functions to manage the process. 6. What is the key difference between the Dataset and Linked Service in Azure Data Factory? The dataset specifies a source to the data store described by the linked service, such as a table name or query. Linked service specifies the connection string to data stores, including server instance names and credentials. Relevant Reading: Top 40 selected question Answers for Microsoft Azure interview The Ultimate Future of Cloud Computing 7. How many types of triggers are supported by Azure Data Factory? Azure Data Factory supports three types of triggers: Tumbling Window Trigger: Executes pipelines over cyclic intervals and maintains the state. Event-based Trigger: Responds to blob storage events like additions or deletions. Schedule Trigger: Executes pipelines based on a wall clock schedule. 8. What are the rich cross-platform SDKs for advanced users in Azure Data Factory? ADF V2 provides several SDKs for writing, managing, and monitoring pipelines: Python SDK C# SDK PowerShell CLI REST APIs for interfacing with Azure Data Factory. 9. What is the difference between Azure Data Lake and Azure Data Warehouse? Azure Data Lake Data Warehouse Stores any type, size, and shape of data Repository for filtered data from specific sources Used by data scientists Used by business professionals Highly accessible with quick updates Modifying can be challenging and expensive Schema defined after data storage Schema defined before data storage Uses ELT process Uses ETL process Ideal for in-depth analysis Ideal for operational users 10. What is Blob Storage in Azure? Blob Storage stores large amounts of unstructured data such as text, images, or binary. It's used for streaming audio or video, data backup, disaster recovery, and analysis. Blob Storage can also create Data Lakes for analytics. 11. What is the difference between Data Lake Storage and Blob Storage? Data Lake Storage Blob Storage Optimized for big data analytics workloads General-purpose storage Follows a hierarchical file system Utilizes an object store with a straightforward namespace structure Stores data as files in folders Containers within a storage account Used for batch, interactive, stream analytics, and machine learning data Stores text files, binary data, media, and general-purpose data 12. What are the steps to create an ETL process in Azure Data Factory? Creating an ETL process involves: Creating a service for a linked data store (e.g., SQL Server Database). Creating a linked service for the destination data store (e.g., Azure Data Lake). Creating a dataset for data saving. Creating a pipeline and copy activity. Scheduling the pipeline with a trigger. 13. What is the difference between Azure HDInsight and Azure Data Lake Analytics? Azure HDInsight Azure Data Lake Analytics Platform as a Service (PaaS) Software as a Service (SaaS) Requires configuring clusters with predefined nodes Processes data by passing queries Flexible configuration of HDInsight Clusters Less flexible, automatically managed by Azure 14. What are the top-level concepts of Azure Data Factory? Top-level concepts in ADF include: Pipeline: Carrier where processes occur. Activities: Steps within the pipeline. Data Sets: Structures holding data. Linked Services: Store information for connecting resources. 15. What are the key differences between Mapping Data Flow and Wrangling Data Flow in Azure Data Factory? The  key differences between Mapping Data Flow and Wrangling Data Flow in Azure Data Factory are: Mapping Data Flow: Graphical data transformation logic, no coding required, executed on a Spark cluster. Wrangling Data Flow: Code-free data preparation using Power Query M functions, integrated with Power Query Online. 16. Is the knowledge of coding required for Azure Data Factory? No, coding knowledge is not necessary. ADF provides 90 built-in connectors and mapping data flow activities, enabling data transformation without programming skills. 17. What changes can we see regarding data flows, from private to limited public preview? Key changes include: No need for Azure Databricks Clusters. Use of Data Lake Storage Gen 2 and Blob Storage. ADF handles cluster creation and tear-down. Separating Blob datasets and Azure Data Lake Storage Gen 2 into delimited text and Apache Parquet datasets. 18. How can we schedule a pipeline? A pipeline can be scheduled using: Schedule Trigger Window Trigger 19. Can we pass parameters to a pipeline run? Yes, parameters can be passed to a pipeline run. Define parameters at the pipeline level and pass arguments during pipeline execution. 20. Can I define default values for the pipeline parameters? Yes, you can define default values for parameters within pipelines. Conclusion Mastering Azure Data Factory is essential for data engineers in today's cloud-based data management landscape. Understanding these top interview questions and answers offered by https://www.technologycrowds.com/ will help you prepare effectively and increase your chances of success. Azure Data Factory offers robust solutions for data integration, transformation, and orchestration, making it a valuable skill in the industry. Frequently Asked Questions About Azure Data Factory What is the primary use of Azure Data Factory? Azure Data Factory is primarily used for cloud data integration, transformation, and orchestration. Do I need to know coding to use Azure Data Factory? Azure Data Factory provides tools and connectors for data transformation without requiring programming skills. How does Azure Data Factory handle data security? Azure Data Factory ensures data security through encryption, compliance with industry standards, and secure network integration. What are the advantages of using Azure Data Factory? Advantages include automated data workflows, seamless cloud integration, flexible scheduling, and support for various data sources and formats. Can Azure Data Factory handle real-time data processing? Azure Data Factory can handle real-time data processing through event-based triggers and data streaming capabilities. What is the pricing model for Azure Data Factory? Azure Data Factory's pricing is based on usage, including data pipeline execution, data movement, and data volume processed. How does Azure Data Factory integrate with other Azure services? Azure Data Factory integrates seamlessly with other Azure services such as Azure Data Lake, Azure SQL Database, and Azure Machine Learning. Can I use Azure Data Factory to schedule data pipelines? You can schedule data pipelines in Azure Data Factory using schedule and tumbling window triggers.

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