Azure Data Factory Pipelines Overview

This article provides an overview of pipelines in Azure Data Factory, a fully managed, serverless data integration service that enables you to orchestrate and automate the movement and transformation of data. You can use Azure Data Factory to build ETL (extract, transform, and load) and ELT (extract, load, and transform) data processing workflows.

What are Pipelines?

A pipeline in Azure Data Factory is a logical grouping of activities that together perform a task. For example, a pipeline might copy data from a SQL database to a blob storage container, and then run a Hive script on an Azure HDInsight cluster to process the data.

Pipelines are author, schedule, and orchestrate data movement and transformation in a structured manner. They can be run on-demand, on a schedule, or in response to an event.

Key Components of a Pipeline

• Activities: Activities represent a processing step in a pipeline. For example, a Copy data activity is used to move data, a Data flow activity is used for visual data transformation, and a Stored procedure activity is used to execute SQL logic.
• Datasets: Datasets represent data structures within the data stores, which point to or encapsulate the data you want to use in your activities as inputs or outputs.
• Linked Services: Linked services define connection information needed for Data Factory to connect to external resources. Think of them as connection strings.
• Triggers: Triggers define when a pipeline execution needs to be kicked off. This can be based on a schedule, an event, or manual execution.
• Integration Runtimes: Integration runtimes (IR) provide the compute infrastructure used by Data Factory to perform data integration activities across different network environments.

Creating a Pipeline

You can create pipelines using the Azure Data Factory UI in the Azure portal, or programmatically using Azure PowerShell, .NET, or REST APIs. The visual designer allows you to drag and drop activities and connect them to build complex workflows.

Example Pipeline Structure

Consider a pipeline that:

1. Extracts data from an on-premises SQL Server.
2. Loads the data into Azure Blob Storage.
3. Transforms the data using a Data Flow or a Databricks notebook.
4. Loads the transformed data into Azure Synapse Analytics.

Pipeline Orchestration Patterns

Azure Data Factory supports various orchestration patterns:

• Control Flow: This includes sequential activities, parallel branches, loops, conditional execution, and parameterization.
• Data Flow: For visual, code-free data transformation, you can use Mapping Data Flows, which are managed by Data Factory compute.
• Activity Chaining: Define dependencies between activities to control their execution order.
• Parameterization: Make your pipelines reusable by using parameters for dynamic values.

Monitoring Pipelines

Azure Data Factory provides comprehensive monitoring tools to track pipeline runs, activity runs, and identify any failures. You can view the status, duration, and error messages of your data integration processes.

Use Cases

Pipelines are essential for a variety of data scenarios, including:

• Migrating data from on-premises to the cloud.
• Orchestrating complex ETL/ELT processes.
• Processing streaming data.
• Automating data ingestion and transformation for BI and analytics.

By leveraging the power of Azure Data Factory pipelines, you can build robust, scalable, and efficient data integration solutions.