Azure Data Factory is a Microsoft cloud service offering that provides data integration from various sources. It is part of the Azure platform. ADF is a great option for creating hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration pipelines. In simple terms, an ETL tool collects data from various sources, transforms it into useful information, and transfers it to destinations such as data lakes, data warehouses, etc.
How does ADF work?
Combine and connect: Gather and combine data from various sources. The data can be structured, semi-structured, or unstructured.
Centralize and store: Transfer and store data from on-premises storage to a centralized location, such as a cloud-based store.
Transform and analyze: After storing data in centralized cloud storage, use computing services such as HDInsight Hadoop, Spark, Data Lake Analytics, and Machine Learning to process or transform the data collected.
Publish: After refining the data and converting it into consumable form, publish it to cloud stores like Azure Data Lake, Azure Datawarehouse, and Azure Cosmos DB, whichever analytics engine your business users can point from to their BI apps.
Visualize and Monitor: For further analysis, visualize the output data using third-party apps like Tableau, Microsoft Power BI, Sisense, etc.
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