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How does Azure Data Factory work?

Data Factory contains a series of interconnected systems that provide a complete end-to-end platform for data engineers.

  1. Connect and Collect: The first step in building an information production system is to connect to all the required sources of data and processing, such as software-as-a-service (SaaS) services, databases, file shares, and FTP web services. The next step is to move the data as needed to a centralized location for subsequent processing.

Without Data Factory, enterprises must build custom data movement components or write custom services to integrate these data sources and processing. It's expensive and hard to integrate and maintain such systems. In addition, they often lack the enterprise-grade monitoring, alerting, and the controls that a fully managed service can offer.

2.Transform and enrich: After data is present in a centralized data store in the cloud, process or transform the collected data by using ADF mapping data flows. Data flows enable data engineers to build and maintain data transformation graphs that execute on Spark without needing to understand Spark clusters or Spark programming.

If you prefer to code transformations by hand, ADF supports external activities for executing your transformations on compute services such as HDInsight Hadoop, Spark, Data Lake Analytics, and Machine Learning.

3.CI/CD and publish:
Data Factory offers full support for CI/CD of your data pipelines using Azure DevOps and GitHub. This allows you to incrementally develop and deliver your ETL processes before publishing the finished product. After the raw data has been refined into a business-ready consumable form, load the data into Azure Data Warehouse, Azure SQL Database, Azure CosmosDB, or whichever analytics engine your business users can point to from their business intelligence tools.

4.Monitor:After you have successfully built and deployed your data integration pipeline, providing business value from refined data, monitor the scheduled activities and pipelines for success and failure rates. Azure Data Factory has built-in support for pipeline monitoring via Azure Monitor, API, PowerShell, Azure Monitor logs, and health panels on the Azure portal.

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