What is the difference between ADF Pipeline and ADF Data flow?

Question

Can you explain the difference between ADF Pipeline and ADF Data flow?

Answer 1

@Sobaba

Thanks for reaching out to Microsoft Q&A.

The difference between ADF Pipeline and ADF Data Flow in Azure Data Factory (ADF)!

ADF Pipeline

• Purpose: ADF Pipelines are primarily used for orchestration and scheduling. They allow you to manage and coordinate the execution of various activities, such as copying data, running stored procedures, or executing data flows.
• Components: Pipelines can include a variety of activities, such as data movement, data transformation, and control flow activities (e.g., conditional statements, loops).
• Use Case: Ideal for managing the overall workflow and dependencies between different tasks in your data integration process.

ADF Data Flow

• Purpose: ADF Data Flows are designed for data transformation. They enable you to perform complex data transformations without writing code, using a visual interface.
• Components: Data Flows include transformations like joins, aggregations, and filtering. They run on scaled-out Apache Spark clusters managed by ADF.
• Use Case: Best suited for transforming large datasets and implementing data transformation logic visually.

In summary, ADF Pipelines are for orchestrating and managing workflows, while ADF Data Flows are for transforming data within those workflows.

Hope this helps. Do let us know if you any further queries.

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Answer 2

In Azure Data Factory (ADF), Pipelines and Data Flows serve different purposes but are both essential components for orchestrating and transforming data:

ADF Pipeline:

• A Pipeline is a logical container that defines the overall workflow or sequence of data processing activities. It allows you to orchestrate data movement, transformation, and processing by chaining together activities like copy operations, data transformations, and external executions.
  • Pipelines can include a variety of activities such as data movement (e.g., copy data from one source to another), execution of Azure Functions, Databricks jobs, or even triggering Data Flows.
    • Pipelines are more about control flow and orchestration of tasks, helping manage dependencies between steps.
    ADF Data Flow:

    - Data Flow is specifically for **data transformation** within Azure Data Factory. It is used for performing data transformation operations like joins, aggregations, filtering, and row manipulations.
    
       - Data Flow operates on big data via Spark, making it powerful for ETL/ELT transformations at scale.
    
          - You design Data Flows visually using a drag-and-drop interface without needing to write code. It processes data in-memory using Azure's compute resources, which can then be integrated into a Pipeline for execution.
    

Key Difference:

• ADF Pipelines orchestrate and control the flow of activities, whereas Data Flows focus on transforming and manipulating data within a pipeline.

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hth

Marcin

Answer 3

@Sobaba Following up to see if the above answer was helpful. If this answers your query, do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.

Answer 4

ADF Pipelines are orchestration and data movement whereas dataflow is all about transformation