Interview Questions and Answers

Azure Data Factory is a cloud-based data integration service that allows you to create, schedule, and manage data pipelines that can move data between various supported data stores.

A Linked Service is a named connection to an external data store, such as Azure SQL Database, Azure Blob Storage, or on-premises SQL Server.

Pipelines define the logical flow of data and activities in Azure Data Factory. They are used to orchestrate and automate data workflows.

A dataset represents the structure of the data that is being processed. It can be an input or output of an activity in a pipeline.

Dynamic content allows you to use expressions and system variables to parameterize values in your pipeline, making them more flexible and reusable.

Triggers in Azure Data Factory are used to automate the execution of pipelines based on a schedule or an event.

The Copy Data tool is a wizard-based tool in Azure Data Factory that helps you create, configure, and execute copy data activities for moving data between supported sources and destinations.

Key components include datasets, linked services, pipelines, activities, and data flows.

Data movement is achieved through activities in pipelines. Activities can be copy data activities, data flow activities, or custom activities.

A pipeline defines the overall process and workflow, while a data flow defines the data transformations within that process.

Data Flow is a cloud-based data transformation service that enables data transformations and manipulations using a visual interface.

Integration Runtimes define the compute infrastructure used by data factory for data movement and data transformation activities.

Azure Data Factory provides monitoring dashboards, logging, and integration with Azure Monitor for tracking and managing pipeline executions.

Azure Data Factory is primarily focused on data integration and ETL, while Azure Logic Apps are designed for workflow automation and business process integration.

Datasets can be parameterized using expressions and system variables to make them more dynamic and adaptable to changing requirements.

Data slicing is the division of data into time-based slices, which is often used in incremental data loading scenarios in data pipelines.

Azure Data Factory supports hybrid data scenarios through on-premises data gateways, which allow data movement between on-premises and cloud data stores.

Data Flow Debug Mode allows you to interactively debug and validate data flows during development to identify and fix issues.

Data lineage in Azure Data Factory provides a visual representation of the flow and transformation of data throughout the pipeline, helping in tracking and understanding data movements.

The REST API allows you to programmatically manage and monitor Azure Data Factory resources, such as pipelines, datasets, and activities.

Integration patterns in Azure Data Factory define how data is moved and transformed, providing flexibility and adaptability to different data integration scenarios.

Azure Data Factory supports data wrangling through the Data Flow feature, which provides a visual interface for designing and executing data transformations.

Linked services can be parameterized using dynamic content expressions, allowing for dynamic configuration based on runtime values.

Azure Data Factory supports Azure role-based access control (RBAC) to control access to resources. It also provides features like managed private endpoints for secure data movement.

Azure Data Factory provides fault tolerance through retry policies for activities, and it supports monitoring and logging for troubleshooting.

Managed private endpoints allow you to securely access data stores over a private connection, extending the data factory's network into the data store's virtual network.

Data partitioning is used to divide large datasets into smaller, more manageable partitions to improve processing efficiency and parallelism.

Schema drift is handled through mapping data flow transformations that dynamically adjust to changes in the source or destination schema during data movement.

Mapping Data Flow is a visual data transformation feature in Azure Data Factory that allows you to design and execute complex data transformations using a graphical interface.

Managed Virtual Network allows you to isolate the Azure Data Factory environment and control the network traffic for enhanced security and privacy.