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Azure Data Factory V2 Tutorial
Azure Data Factory V2 is a cloud-based data integration and transformation service from Microsoft that allows users to efficiently build, schedule, and orchestrate data workflows across various sources and destinations. With its intuitive visual interface, it enables businesses to easily ingest, transform, and load large volumes of data in a scalable and cost-effective manner. Its advanced features such as data lineage, monitoring, and automatic triggers make it a powerful tool for building complex data pipelines. This tutorial provides step-by-step guidance on how to use Azure Data Factory V2 to create end-to-end data workflows and gain insights from disparate data sources.
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1) Azure Data Factory V2 is a cloud-based data integration service offered by Microsoft Azure. This service allows users to create and manage data workflows and pipelines to ingest, transform, and load data from various sources to different destinations.
2) The training program for Azure Data Factory V2 aims to provide students with a comprehensive understanding of the service and its various features. This will help them develop skills in data integration and management, which are highly sought after in the current job market.
3) The program covers topics such as data ingestion, data transformation using Azure Data Factory, pipeline orchestration, and pipeline monitoring. It also includes hands-on exercises and projects to give students practical experience in using the service.
4) The training program is designed to cater to different levels of expertise, from beginners to advanced users. The curriculum is constantly updated to keep up with the latest features and best practices in data integration.
5) Upon completion of the training program, students will be equipped with the necessary skills and knowledge to design and implement data integration solutions using Azure Data Factory V2. This can open up opportunities for them in various industries, including data analytics, business intelligence, and data engineering.