Azure Data Factory Interview Questions and Answers 2025

There are two types of triggers in Azure Data Factory: Schedule-based triggers and Event-based triggers.

• Schedule-based triggers are based on a time schedule and can be set to run at specific intervals.

• Event-based triggers are triggered by events such as the completion of a pipeline run or the arrival of new data.

• Triggers can be used to automate the execution of pipelines in Azure Data Factory.

Delta load in ADF is achieved by comparing source and target data and only loading the changed data.

• Use a Lookup activity to retrieve the latest watermark or timestamp from the target table

• Use a Source activity to extract data from the source system based on the watermark or timestamp

• Use a Join activity to compare the source and target data and identify the changed records

• Use a Sink activity to load only the changed records into the target table

Get metadata activity is used to retrieve metadata of a specified data store or dataset in Azure Data Factory.

• Get metadata activity is used in Azure Data Factory to retrieve metadata of a specified data store or dataset.

• Parameters to pass include dataset, linked service, and optional folder path.

• The output of the activity includes information like schema, size, last modified timestamp, etc.

• Example: Get metadata of a SQL Server table using a linked service to the database.

Performance tuning in Azure Data Factory involves optimizing data flows and activities to improve efficiency and reduce processing time.

• Identify bottlenecks in data flows and activities

• Optimize data partitioning and distribution

• Use appropriate data integration patterns

• Leverage caching and parallel processing

• Monitor and analyze performance metrics

Use GetMetadata activity to get recent files and then copy them using Copy Data activity in ADF.

• Use GetMetadata activity in Azure Data Factory to retrieve information about recent files.

• Filter the files based on their last modified date to get the most recent ones.

• Use Copy Data activity to copy the recent files to the desired destination.

• Configure the Copy Data activity with the source and destination datasets and mappings.

The 7 layers in Azure Data Factory for pipelining data from on-premises to Azure Cloud

• 1. Ingestion Layer: Collects data from various sources such as on-premises databases, cloud storage, or IoT devices.

• 2. Storage Layer: Stores the ingested data in a data lake or data warehouse for processing.

• 3. Batch Layer: Processes data in batches using technologies like Azure Databricks or HDInsight.

• 4. Stream Layer: Processes real-time data streams using technologies like Azure Stream Anal...read more