Azure Data Factory Top 20 interview questions & answer

Azure Data Factory Top 20 interview questions

1. What is Azure Data Factory?

Answer: Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create, schedule, and orchestrate data workflows. It can move and transform data from various sources to destinations, making it suitable for ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) operations.

Use Case: Migrating on-premises data to Azure SQL Database for better scalability and performance.

2. What are the key components of Azure Data Factory?

Answer: The key components of ADF include:

• Pipelines: Define the workflow of data movement and transformation activities.
• Activities: Individual steps within a pipeline, such as data movement (Copy Activity) or data transformation (Data Flow).
• Datasets: Represent data structures within the data stores, defining the inputs and outputs of activities.
• Linked Services: Define the connection information to external data sources.
• Triggers: Define when pipelines should be run.

Use Case: Creating a pipeline to copy data from an Azure Blob Storage to an Azure SQL Database on a daily schedule using a time-based trigger.

3. Explain the difference between Azure Data Factory V1 and V2.

Answer: ADF V1 was more limited, focusing primarily on data movement. ADF V2 introduced a more advanced, feature-rich environment, including:

• More control over activities with branching, loops, and conditional logic.
• Integration with other Azure services.
• Improved monitoring and management capabilities.
• Support for SSIS (SQL Server Integration Services) packages.

Use Case: Using ADF V2 to implement complex ETL workflows involving conditional execution and iteration over data slices.

4. How does ADF handle data movement between different data stores?

Answer: ADF uses the Copy Activity to move data between different data stores. It supports over 90 connectors, including Azure Blob Storage, Azure SQL Database, Amazon S3, and more. The Copy Activity can be configured with source and sink datasets, and data movement can be monitored and logged.

Use Case: Copying data from an on-premises SQL Server to Azure Data Lake Storage for big data analytics.

5. What is a Data Flow in ADF, and how is it different from a Copy Activity?

Answer: A Data Flow in ADF is used for transforming data at scale, using a Spark-based execution environment. Unlike the Copy Activity, which simply moves data, Data Flows allow for complex data transformations such as joins, aggregations, and data cleansing.

Use Case: Transforming raw sales data by aggregating monthly sales figures and cleaning up missing values before loading into a data warehouse.

6. Describe how you can schedule a pipeline in ADF.

Answer: Pipelines in ADF can be scheduled using Triggers. There are different types of triggers:

• Schedule Triggers: Run pipelines on a specified schedule.
• Event-based Triggers: Run pipelines in response to events, such as the arrival of a file in Blob Storage.
• Manual Triggers: Start pipelines manually.

Use Case: Scheduling a pipeline to run at midnight every day to process and load daily transaction data into a database.

7. What are Linked Services in ADF?

Answer: Linked Services in ADF are used to define the connection information to data stores or compute environments. They act as connection strings for activities in a pipeline, enabling ADF to interact with various data sources and sinks.

Use Case: Creating Linked Services to connect ADF to an Azure SQL Database and an Azure Blob Storage for data extraction and loading.

8. How can you handle errors in ADF pipelines?

Answer: Errors in ADF pipelines can be handled using:

• Retry policies: Configure activities to retry on failure.
• On-failure actions: Define actions to be taken if an activity fails, such as sending notifications or executing other activities.
• Error logging and monitoring: Use ADF’s built-in monitoring tools to log and track errors.

Use Case: Configuring a pipeline to retry a Copy Activity up to three times before logging the error and sending an email notification to the admin.

9. What are Integration Runtimes in ADF, and why are they important?

Answer: Integration Runtimes (IR) in ADF provide the compute infrastructure used to move and transform data. There are three types:

• Azure IR: Runs in the Azure public cloud.
• Self-hosted IR: Runs on-premises or in a virtual machine, useful for accessing on-premises data stores.
• Azure-SSIS IR: Runs SSIS packages in Azure.

Use Case: Using a self-hosted IR to connect to and move data from an on-premises Oracle database to Azure Blob Storage.

10. How can you monitor the performance of ADF pipelines?

Answer: ADF provides monitoring capabilities through its user interface, where you can:

• View pipeline and activity runs.
• Check the status, duration, and performance metrics.
• Set up alerts for failed activities.
• Use Azure Monitor and Log Analytics for advanced monitoring and alerting.

Use Case: Setting up alerts to notify the team if a data transformation pipeline fails or exceeds a specific runtime threshold.

11. What is the role of parameters in ADF?

Answer: Parameters in ADF allow you to pass values into pipelines, datasets, and linked services at runtime. They enable dynamic configuration, making pipelines more reusable and flexible.

Use Case: Creating a parameterized pipeline that accepts the name of a source file and the destination table, allowing the same pipeline to process different files and load them into corresponding tables.

12. Explain how you can implement a looping mechanism in ADF.

Answer: ADF supports looping mechanisms using ForEach Activities. You can use these activities to iterate over a collection of items and execute specified activities for each item.

Use Case: Looping over a list of CSV files in a Blob Storage container to process each file and load its data into a corresponding SQL table.

13. How do you secure data in ADF?

Answer: Data security in ADF can be ensured by:

• Using Azure Key Vault to manage and retrieve secrets (like connection strings and passwords).
• Enabling encryption for data in transit and at rest.
• Implementing role-based access control (RBAC) to restrict access to ADF resources.
• Using managed identities for secure authentication.

Use Case: Storing database connection strings in Azure Key Vault and configuring Linked Services in ADF to retrieve these secrets securely.

14. What is an Event-based Trigger, and how can it be used in ADF?

Answer: An Event-based Trigger in ADF runs pipelines in response to events such as the arrival or deletion of a file in Blob Storage or Data Lake Storage. It uses Azure Event Grid to detect changes and trigger the pipeline.

Use Case: Triggering a pipeline to process and load a new file into a database as soon as it is uploaded to an Azure Blob Storage container.

15. Describe the use of the Lookup Activity in ADF.

Answer: The Lookup Activity in ADF retrieves a dataset from a data source. It is typically used to look up configuration settings or reference data required by other activities in the pipeline.

Use Case: Using the Lookup Activity to fetch the latest configuration settings from a SQL database, which are then used to drive subsequent data processing steps.

16. How do you integrate ADF with other Azure services?

Answer: ADF integrates with various Azure services, such as:

• Azure SQL Database for data storage and querying.
• Azure Data Lake Storage for big data storage.
• Azure Databricks for advanced data transformations.
• Azure Functions for executing custom code.
• Power BI for data visualization.

Use Case: Creating a pipeline that transforms raw data using Azure Databricks and then loads the transformed data into Power BI for reporting.

17. What is a Mapping Data Flow in ADF, and how does it differ from a pipeline?

Answer: A Mapping Data Flow in ADF is used for visually designing data transformations, which are executed in a scalable Spark environment. Unlike pipelines, which orchestrate activities, Mapping Data Flows focus on the transformation logic within the data.

Use Case: Designing a Mapping Data Flow to merge, filter, and aggregate sales data from multiple sources before loading it into a data warehouse.

18. How can you handle large-scale data transformation in ADF?

Answer: Large-scale data transformations in ADF can be handled using Mapping Data Flows, which leverage the power of Spark for distributed data processing. Additionally, you can use Azure Databricks or HDInsight for more complex transformation needs.

Use Case: Transforming terabytes of log data using Mapping Data Flows to filter, aggregate, and enrich the data before loading it into an Azure Data Lake Storage.

19. Explain the role of Data Wrangling in ADF.

Answer: Data Wrangling in ADF is a feature that allows users to prepare and clean data interactively using a visual interface. It is built on the Power Query technology, enabling data shaping and transformation without writing code.

Use Case: Using Data Wrangling to clean and standardize customer data from various sources before it is used for analytics.

20. What is the purpose of Data Lineage in ADF, and how can you implement it?

Answer: Data Lineage in ADF helps track the flow of data from source to destination, providing visibility into data transformations and dependencies. This is critical for data governance and auditing.

Use Case: Implementing data lineage to track how raw sales data is transformed and loaded into a reporting database, ensuring transparency and traceability of data changes.