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I do not understand the difference between dataflow and pipeline in Azure Data Factory.
I have read and see DataFlow can Transform Data without writing any line of code.
But I have made a pipeline and this is exactly the same thing.
Thanks
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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.
Dataflow uses your pipeline code to create an execution graph that represents your pipeline's PCollection s and transforms, and optimizes the graph for the most efficient performance and resource usage. Dataflow also automatically optimizes potentially costly operations, such as data aggregations.
Modern data pipelines often perform real-time processing with streaming computation. This allows the data to be continuously updated and thereby support real-time analytics and reporting and triggering other systems. ETL pipelines usually move data to the target system in batches on a regular schedule.
Data flows are created from the factory resources pane like pipelines and datasets. To create a data flow, select the plus sign next to Factory Resources, and then select Data Flow. This action takes you to the data flow canvas, where you can create your transformation logic.
A Pipeline is an orchestrator and does not transform data. It manages a series of one or more activities, such as Copy Data or Execute Stored Procedure. Data Flow is one of these activity types and is very different from a Pipeline.
Data Flow performs row and column level transformations, such as parsing values, calculations, adding/renaming/deleting columns, even adding or removing rows. At runtime a Data Flow is executed in a Spark environment, not the Data Factory execution runtime.
A Pipeline can run without a Data Flow, but a Data Flow cannot run without a Pipeline.
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Firstly, dataflow activity need to be executed in the pipeline. So I suspect that you are talking about the copy activity and dataflow activity as both of them are used for transferring data from source to sink.
I have read and see DataFlow can Transform Data without writing any line of code.
Your could see the overview of Data Flow. Data flow allows data engineers to develop graphical data transformation logic without writing code. All data transfer steps are based on visual interfaces.
I have made a pipeline and this is exactly the same thing.
Copy activity could be used for data transmission. However, it has many limitations with column mapping. So,if you just need simple and pure data transmission, Copy Activity could be used. In order to further meet the personalized needs, you could find many built-in features in the Data Flow Activity. For example, Derived column, Aggregate,Sort etc.
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