Top Azure Data Factory Interview Questions (2024)

1. What is Azure Data Factory?

2. Is Azure data Factory an ETL tool?

3. What is the difference between SSIS and Azure data Factory?

4. What is the use of Azure Data Lake in Azure data Factory (ADF)?

5. What is parameterization in Azure?

6. What is Global parameters in Azure Data Factory?

7. What is the use of dataset in Azure data Factory?

8. What is Copy activity in Azure Data Factory?

9. What are the types of Integration Runtimes in Azure Data Factory?

10. How to copy data from an Azure Blob Storage (text file) to an Azure SQL Database table?

Q: What is Azure Data Factory?
Ans:

Azure Data Factory (ADF) is a fully managed, serverless data integration solution for ingesting, preparing, and transforming all of data at scale. Enterprise Connectors to any Data Stores — Azure Data Factory allows businesses to ingest data from a wide range of data sources.

Q: Is Azure data Factory an ETL tool?
Ans:

Azure Data Factory is a cloud-based ETL and data integration service for creating data movement and transformation workflows. Data Factory allows you to design scheduled workflows (pipelines) without writing any code.

Q: What is the difference between SSIS and Azure data Factory?
Ans:

Azure Data Factory (ADF)	SQL Server Integration Services (SSIS)
ADF is a Extract-Load Tool	SSIS is an Extract-Transfer-Load tool
ADF is a cloud-based service (PAAS tool)	SSIS is a desktop tool (uses SSDT)
ADF is a pay-as-you-go Azure subscription.	SSIS is a licensed tool included with SQL Server.
ADF does not have error handling capabilities.	SSIS has error handling capabilities.
ADF uses JSON scripts for its orchestration (coding).	SSIS uses drag-and-drop actions (no coding).

Q: What is the use of Azure Data Lake in Azure data Factory (ADF)?
Ans:

Azure Data Lake Storage Gen2 is a set of features integrated into Azure Blob storage that are focused on big data analytics. It enables interaction with data through both the file system and the object storage models. Azure Data Factory (ADF) is a cloud-based data integration service that is fully managed.

Q: What is parameterization in Azure?
Ans:

It allows us to provide the server name, database name, credentials, and so on dynamically while executing the pipeline, allowing us to reuse rather than building one for each request. Parameterization in Azure Data Factory is important to successful design and reusability, as well as reduced solution maintenance costs.

Q: What is Global parameters in Azure Data Factory?
Ans:

Global parameters are constants which can be used by a pipeline in any expression throughout a data factory. It comes in handy when we have several pipelines with the same parameter names and values. We can override these parameters in each environment when promoting a data factory using the continuous integration and deployment process (CI/CD).

Any pipeline expression can make use of global parameters. If a pipeline refers to another resource, as in a dataset or data flow, the global parameter value can be passed down through that resource's parameters. Global parameters are represented as pipeline().globalParameters.

Q: What is the use of dataset in Azure data Factory?
Ans:

A dataset is a named view of data that clearly points to or references the data we could use as inputs and outputs in activities. Datasets are used to identify data in various data storage, such as tables, files, folders, and documents.

Q: What is Copy activity in Azure Data Factory?
Ans:

The Copy activity in Azure Data Factory can be used to copy data between on-premises and cloud data stores (supported data stores and formats) and to use the copied data in additional transformation or analysis tasks.

The Integration Runtime (IR) is used by Azure Data Factory as a secure compute architecture to run the copy activity throughout different network environments and ensure that it is executed in the region nearest to the data storage. We can consider it a link between the copy activity and the linked services.

Q: What are the types of Integration Runtimes in Azure Data Factory?
Ans:

Integration Runtimes in Azure Data Factory are classified into three types:

1. The Azure Integration Runtime is utilised when copying data between data stores that are publicly accessible via the internet.
2. Self-Hosted Integration Runtime for copying data from or to an on-premises data store or from a network with access control.
3. SSIS packages in the Data Factory are run using the Azure SSIS Integration Runtime.

Q: How to copy data from an Azure Blob Storage (text file) to an Azure SQL Database table?
Ans:

Set up the Data Factory pipeline which will be used to copy data from the blob storage to the Azure SQL Database.
1. Navigate to the Azure Portal and select the Author & Monitor option.
2. Click on the Create Pipeline / Copy Data option.
3. Enter a unique name for the copy activity and select whether to schedule or execute it once for the Copy Data option -> Click Next
4. Select the type of source data store to link to in order to construct a Linked Service (Azure Blob Storage) and click Continue
5. A new Linked Service window will open -> enter a unique name and other details -> test the connection and then click Create.
6. If you need to copy many files recursively, specify the input data file or folder (DataSet) and click Next.
7. Select Azure SQL Database from the list of New Linked Services, then click Continue to configure the new Linked service.
8. Fill out all of the information in the New Linked Service window -> Test Connection -> Create
9. Create the sink dataset with the destination database table specified.
10. Copy Data Tool -> Settings -> Next -> Review all the copy configuration from the Summary Window -> Next -> Finish

The Data Factory will create all pipeline components before running the pipeline. Executing the pipeline involves running the copy activity, which copies the data from the input file in Azure Blob Storage and writes it to the Azure SQL Database table.