Azure Data Factory Tutorial
Introduction
Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create, schedule, and orchestrate data workflows. It enables you to move data between various data sources and transform it as needed. In this tutorial, we will cover the basics of Azure Data Factory, from creating your first data pipeline to scheduling and monitoring it.
Getting Started with Azure Data Factory
Before we dive into creating data pipelines, let's set up Azure Data Factory in your Azure account.
Step 1: Create an Azure Data Factory
1. Log in to the Azure Portal.
2. In the left-hand menu, select "Create a resource".
3. Search for "Data Factory" and select it.
4. Click "Create" and fill in the required details:
- Subscription: Select your Azure subscription.
- Resource group: Create a new resource group or select an existing one.
- Region: Choose the region closest to your data sources.
- Name: Provide a unique name for your Data Factory instance.
5. Click "Review + create" and then "Create".
Creating Your First Pipeline
Once you have your Data Factory instance set up, you can create your first data pipeline.
Step 2: Create a Pipeline
1. In the Data Factory dashboard, click on "Author & Monitor".
2. Click on the "Author" icon and select "Pipeline" from the menu.
3. Click the "+" icon and select "Pipeline" to create a new pipeline.
4. Give your pipeline a name (e.g., "MyFirstPipeline").
Adding Activities to Your Pipeline
Activities are the tasks that you want your pipeline to perform. Let's add a simple copy activity.
Step 3: Add a Copy Activity
1. In the pipeline editor, click on the "Activities" tab on the left-hand side.
2. Drag and drop the "Copy data" activity into the pipeline canvas.
3. Configure the source and destination data stores by clicking on the "Source" and "Sink" tabs, respectively.
4. Click "Publish All" to save and publish your pipeline.
Scheduling and Monitoring Pipelines
Once your pipeline is created, you can schedule it to run at specific times and monitor its execution.
Step 4: Schedule a Pipeline
1. In the Data Factory dashboard, click on "Manage".
2. Under "Author" select "Triggers" and click the "+" icon to create a new trigger.
3. Configure the trigger with the desired schedule (e.g., daily at a specific time).
4. Associate the trigger with your pipeline by selecting the pipeline and clicking "Add Trigger".
Step 5: Monitor Pipeline Runs
1. In the Data Factory dashboard, click on "Monitor".
2. Here, you can see the status of your pipeline runs, including any errors that may have occurred.
3. Click on a specific run to view detailed information, including input/output data and logs.
Conclusion
In this tutorial, you have learned the basics of Azure Data Factory, from setting it up to creating, scheduling, and monitoring data pipelines. Azure Data Factory is a powerful tool for data integration and transformation, enabling you to build complex data workflows with ease.