Azure Data Engineer Course in Hyderabad - Visualpath

1. Introduction To ADF? What are the different types of Integration Runtimes Introduction to Azure Data Factory (ADF) Best Azure Data Engineer Online Training(ADF) is a cloud- based data integration service provided by Microsoft. It enables organizations to create, schedule, and orchestrate data workflows in the cloud, offering a comprehensive solution for data movement, transformation, and orchestration. ADF is designed to handle complex data pipelines and provides a platform for building scalable data solutions. Azure Data Engineer Course - India Key Features of Azure Data Factory •Data Integration: ADF allows seamless integration with various data sources, including on-premises and cloud-based databases, file systems, and SaaS applications. It supports over 90 built-in connectors.

2. •Data Transformation: With ADF, users can perform complex data transformations using data flows. It provides a rich set of transformation activities, including data cleansing, aggregation, and schema mapping. •Orchestration and Automation: ADF enables the orchestration of data workflows, allowing users to schedule and automate data pipelines. It supports triggers and event-based scheduling. •Scalability: ADF is designed to handle large-scale data workloads. It can scale out to meet the demands of big data processing and provides elasticity to adjust resources based on workload requirements. •Monitoring and Management: Users can track the status of data pipelines, view detailed logs, and set up alerts for pipeline failures or performance issues. Types of Integration Runtimes in Azure Data Factory Integration Runtimes (IRs) are the compute infrastructureused by ADF to perform data movement, transformation, and control activities. There are three main types of Integration Runtimes in ADF: Azure Integration Runtime: •Purpose: Primarily used for data movement and transformation within the Azure cloud. •Capabilities: Supports copying data between cloud data stores, performing data transformations, and executing activities within the cloud. •Use Cases: Ideal for cloud-to-cloud data integration and transformation scenarios. Best Azure Data Engineer Training Self-hosted Integration Runtime: •Purpose: Enables data integration between on-premises data sources and cloud data stores. •Capabilities: Facilitates data movement between on-premises and cloud environments, supports data encryption, and can be installed on-premises machines. •Use Cases: Suitable for hybrid data integration scenarios where data needs to be moved between on-premises and cloud environments.

3. Azure SSIS Integration Runtime: •Purpose: Provides a fully managed service to run SQL Server IntegrationServices (SSIS) packages in the cloud. •Capabilities: Supports lifting and shifting existing SSIS packages to Azure without any code changes, offering full compatibility with on-premises SSIS. •Use Cases: Ideal for organizations looking to migrate their existing SSIS workloads to the cloud for better scalability and management. Conclusion Azure Data Factory is a powerful and versatile data integration service that simplifies the process of building, managing, and orchestrating data pipelines in the cloud. With its robust set of features and support for various integration runtimes, ADF enables organizations to create efficient and scalable data workflows, making it a critical tool for modern data engineering and analytics solutions. Whether dealing with cloud-only, hybrid, or existing SSIS workloads, ADF provides the flexibility and capability needed to meet diverse data integration requirements.