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What is the difference between ADF Pipeline and ADF Data flow? Why are sinks/sources supported in Pipeline and Data flow different? Is it possible to create a pipeline that reads data from source, filter, use joins and store data to a sink without a data flow? Please let me know.
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Mapping data flows are operationalized within ADF pipelines using the data flow activity. All a user has to do is specify which integration runtime to use and pass in parameter values. For more information, learn about the Azure integration runtime.
Data Flow is for data transformation. In ADF, Data Flows are built on Spark using data that is in Azure (blob, adls, SQL, synapse, cosmosdb). Connectors in pipelines are for copying data and job orchestration. There are 90+ connectors available there that stretch across on-prem and other clouds.
Azure Data Factory Data Flow or ADF-DF (as it shall now be known) is a cloud native graphical data transformation tool that sits within our Azure Data Factory platform as a service product. What’s more, ADF-DF can be considered as a firm Azure equivalent for our on premises SSIS package data flow engine.
Please let me know. Pipelines are for process orchestration. Data Flow is for data transformation. In ADF, Data Flows are built on Spark using data that is in Azure (blob, adls, SQL, synapse, cosmosdb). Connectors in pipelines are for copying data and job orchestration.
Pipelines are for process orchestration. Data Flow is for data transformation.
In ADF, Data Flows are built on Spark using data that is in Azure (blob, adls, SQL, synapse, cosmosdb).
Connectors in pipelines are for copying data and job orchestration. There are 90+ connectors available there that stretch across on-prem and other clouds.
We are always incrementally adding more connectors to data flows.
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