Azure Data Factory Interview Questions and Answers for 10 years experience

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Azure Data Factory Interview Questions (10 Years Experience)

  1. What are the key differences between Azure Data Factory and Azure Databricks?

    • Answer: Azure Data Factory (ADF) is primarily an ETL/ELT service for orchestrating data movement and transformation, while Azure Databricks is a managed Spark service focused on data processing and analytics. ADF excels at scheduling and managing data pipelines across various sources and sinks, whereas Databricks provides a powerful environment for performing complex data transformations and analysis using Spark. ADF is better suited for scheduled, repeatable data integration tasks, while Databricks is preferred for interactive data exploration, machine learning, and large-scale data processing.

  2. Explain the different types of activities available in Azure Data Factory.

    • Answer: Azure Data Factory offers a wide range of activities, categorized broadly as data movement, data transformation, control flow, and utility activities. Data movement activities include Copy Data, which moves data between various sources and sinks. Data transformation activities encompass Azure Data Lake Analytics U-SQL, HDInsight Hive, Spark, and MapReduce activities. Control flow activities manage the pipeline's execution flow, including ForEach, IfCondition, and Wait activities. Utility activities perform supporting tasks such as Lookup, Web activity, and Execute Pipeline.

  3. How do you handle errors and exceptions in an Azure Data Factory pipeline?

    • Answer: Error handling in ADF involves using try-catch blocks within activities or configuring pipeline-level retry policies. Try-catch blocks allow specific error handling within individual activities. Pipeline-level retry policies define the number of retries and the delay between retries for the entire pipeline or specific activities. Monitoring the pipeline's execution using Azure Monitor and setting up alerts based on specific error codes or statuses are also crucial for proactive error management. Implementing logging and custom error handling using script activities allows for detailed analysis and resolution.

  4. Describe different approaches to data transformation in ADF.

    • Answer: ADF offers several approaches: Mapping Data Flows provide a visual, low-code interface for data transformation using graphical mappings. Azure Data Lake Analytics U-SQL allows writing U-SQL scripts for complex transformations. HDInsight Hadoop clusters (Hive, Pig, Spark) offer powerful transformations for larger datasets. Custom activities, including Azure Functions or stored procedures, allow for integration with external transformation logic. The choice depends on factors like complexity, scale, and familiarity with different technologies.

  5. How do you monitor and troubleshoot ADF pipelines?

    • Answer: Monitoring is done primarily through the ADF monitoring interface, which displays pipeline runs, activity executions, and associated metrics. Azure Monitor integrates with ADF, providing detailed logs and performance insights. Troubleshooting involves reviewing activity logs for error messages, checking data lineage, examining data quality, and using debugging tools within ADF. For complex issues, Azure Monitor logs and application insights can provide deeper diagnostic information. Understanding pipeline execution plans and identifying bottlenecks is crucial for effective troubleshooting.

  • Explain the concept of Linked Services in ADF.

    • Answer: Linked services in ADF are connections to external data stores and services. They store connection details securely, enabling pipelines to access data from various sources like SQL Server, Azure Blob Storage, and other cloud services. They are crucial for abstraction and secure management of access credentials, preventing hardcoding sensitive information within pipeline definitions.

  • Discuss the benefits of using self-hosted integration runtimes in ADF.

    • Answer: Self-hosted integration runtimes (SHIR) offer benefits when dealing with on-premises data sources that are not directly accessible from the cloud. They provide the ability to run ADF pipelines on-premises, providing access to sensitive or private data while maintaining security and compliance requirements. They are beneficial for hybrid cloud architectures.

  • How do you manage and control access to your Azure Data Factory resources?

    • Answer: Access control is managed through Azure Role-Based Access Control (RBAC). Specific roles with predefined permissions can be assigned to users and groups, controlling their ability to create, modify, and monitor pipelines and resources within the ADF instance. This granular control helps ensure data security and governance.

  • Explain the concept of datasets in Azure Data Factory.

    • Answer: Datasets define the structure and metadata of the data stored in a specific location, such as a table in a database or a file in a storage account. They abstract the physical location and provide a consistent way to reference data within pipelines.

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