#azuredatafactory #azure #dataengineeringessentials Telegram channel: https://t.me/techinteluguchannel In this classroom training video I'm going to cover fundamentals of Azure Data Factory. Azure Data Factory is a fully managed, serverless data integration service that enables you to create, schedule and orchestrate ETL/ELT workflows at scale wherever your data lives, in cloud or on-premises. It has four key components that work together to define input and output data, processing events, and the schedule and resources required to execute the desired data flow: Datasets represent data structures within the data stores. An input dataset represents the input for an activity in the pipeline. A pipeline is a group of activities. They are used to group activities into a unit that together performs a task. Activities define the actions to perform on your data. Linked services define the information needed for Azure Data Factory to connect to external resources. Azure Data Factory can help you manage all kinds of data with the help of data lake storage. You can then analyze the data and transform it using pipelines, and finally publish the organized data and visualize it with third-party applications like Apache Spark or Hadoop3. For my previous videos please check the following links. Previous Episodes: Azure Databricks Introduction: https://youtu.be/RW-F-sJzpPk Azure Databricks Episode 1: https://youtu.be/Omf_i45b8Pc Episode 1: PySpark Tutorial తెలుగులో (PySpark introduction & installation in Windows 10) https://youtu.be/4sxlriCqzgQ Episode 2: PySpark Tutorial తెలుగులో - Spark Architecture & Features (స్పార్క్ ఆర్కిటెక్చర్ & ఫీచర్స్) https://youtu.be/-UPouanDfQk EP 03. Writing your first PySpark Program|(PySpark లో మొదటి ప్రోగ్రామ్) | PySpark Tutorial తెలుగులో https://youtu.be/ngbeREkf5sk Big Data Introduction: https://www.youtube.com/playlist?list=PLhR9Ed-_R-8Ux4N6pYvhLUqr7M2vj_ZuE