Azure Data Factory is a cloud-managed service used to control the data copying process between various non-relational and relational data sources that are hosted in clouds or within your datacenters and then transform it in accordance with business needs. It's a large data service that handles the cloud-based Extract Transform and Load (ETL) as well as Extract and Transform (ELT) as well as the data ingestion procedures with scheduled workflows based on data. If you're interested in finding out the details about Microsoft Azure underwater datacenter you can get more information by obtaining the Data Engineer Course.
Utilizing Azure Data Factory You can create workflows that connects to an cloud or on-premises data source using the 90+ connectors built-in that are available within Azure Data Factory and transform it with ADF mapping activities for data flow that do not require you to have a background in spark clustering and programming, and with the option of writing the custom program if the necessary transformation isn't available. Then, you can finally store the data transformed in an Azure cloud centralized storage unit to be processed by an cognitive or analysis service, or save it directly to data sources with relational properties like Azure SQL Database, Azure Data Warehouse and a non-relational data stores like Azure Cosmos DB for it to be processed and transformed in the future. If you're looking for more comprehensive understanding, it can be obtained through better preparation for the Data Engineer Course in Bangalore.
With Azure Data Factory you have the option of using Azure Monitor, PowerShell and the health tab of Azure portal. Azure portal to track the planned and developed workflows' status and monitor any issues with performance or failure that arise when copying or transforming data.
Additionally, Azure Data Factory provides you with the possibility to make use of it with the Azure DevOps along with GitHub repositories for continuous integration and delivery (CI/CD) part of the ETL or ELT workflows as you are creating it, and also before it is released to the production environment. This, in turn, lets you shift it slowly between different environments making use of Azure Resource Manager Templates. Azure Resource Manager Templates as the storage for your configuration.
Imagine the time and effort you'll need to invest in designing a custom data transfer solution that is able to perform tasks of data copying and transformation along with the difficulties and complexity you'll face when trying to connect it to other data sources and systems!