Get started with Azure Data Factory

Getting started with Azure Data Factory is relatively straightforward. Here are the general steps to follow:

Create an Azure Data Factory instance

Log in to the Azure portal (https://portal.azure.com/).
Click on the “Create a resource” button on the left-hand menu.
Search for “Data Factory” in the search bar and select “Data Factory” from the list of results.
Click on the “Create” button.
Fill in the required details such as the subscription, resource group, and name for your Data Factory instance.
Select the version of Azure Data Factory you want to use.
Choose the location where you want to deploy your Data Factory instance.
Review and accept the terms and conditions, then click on “Create” to create your new Data Factory instance.

Connect to data sources

In the Azure portal, open your newly created Data Factory instance.
Click on the “Author & Monitor” button to launch the Azure Data Factory UI.
Click on the “Connections” button on the left-hand menu.
Click on the “New” button to create a new linked service.
Select the type of data store you want to connect to, such as Azure Blob Storage, Azure SQL Database, or on-premises SQL Server.
Fill in the connection details for the data store, such as the server name, database name, and authentication method.
Test the connection to make sure it is working correctly, then click on “Create” to create the new linked service.

Create datasets

In the Azure Data Factory UI, click on the “Author & Monitor” button to launch the Azure Data Factory UI.
Click on the “Datasets” button on the left-hand menu.
Click on the “New” button to create a new dataset.
Select the type of data store you want to use for the dataset.
Choose the file format and data schema for the dataset, such as CSV or JSON.
Fill in the details for the dataset, such as the file path or database table name.
Test the dataset to make sure it is working correctly, then click on “Create” to create the new dataset.

Create pipelines

In the Azure Data Factory UI, click on the “Author & Monitor” button to launch the Azure Data Factory UI.
Click on the “Pipelines” button on the left-hand menu.
Click on the “New” button to create a new pipeline.
Drag and drop the activities you want to use in your pipeline, such as “Copy Data” or “Execute SQL Script”.
Configure the activities by selecting the linked services and datasets you want to use, and specifying any transformation logic.
Test the pipeline to make sure it is working correctly, then click on “Publish All” to publish the pipeline.

Monitor and manage pipelines

In the Azure Data Factory UI, click on the “Author & Monitor” button to launch the Azure Data Factory UI.
Click on the “Monitor & Manage” button on the left-hand menu.
Click on the “Authoring” tab to see a list of your pipelines.
Click on a pipeline to view its status and monitor its progress.
Use the “Trigger” button to manually start a pipeline run, or set up triggers to automatically start pipeline runs based on a schedule or event.
Use the “Alerts” button to set up alerts that notify you when a pipeline fails or when certain conditions are met.

Scale resources

In the Azure portal, open your Data Factory instance.
Click on the “Scale & Performance” button on the left-hand menu.
Choose the resource you want to scale, such as the number of parallel pipeline executions or the number of integration runtime nodes.
Adjust the slider to the desired level of scale.
Review the estimated cost impact of the scale operation.
Click on “Save” to apply the changes.

Monitor and troubleshoot pipeline runs

In the Azure portal, open your Data Factory instance.
Click on the “Monitor & Manage” button on the left-hand menu.
Click on the “Authoring” tab to see a list of your pipelines.
Click on a pipeline to view its status and monitor its progress.
Click on a pipeline run to view its details, including the start time, duration, and any error messages.
Use the “Output” and “Logs” tabs to view the output and logs generated by the pipeline activities.
Use the “Diagnose and Solve Problems” feature to troubleshoot any issues with your pipeline runs.

Use Azure Data Factory templates

In the Azure Data Factory UI, click on the “Author & Monitor” button to launch the Azure Data Factory UI.
Click on the “Templates” button on the left-hand menu.
Browse the list of available templates or search for a specific template.
Select a template and follow the prompts to customize it for your data integration scenario.
Click on “Publish All” to publish the template as a new pipeline.

Use Azure Data Factory Integration Runtimes

In the Azure Data Factory UI, click on the “Author & Monitor” button to launch the Azure Data Factory UI.
Click on the “Integration Runtimes” button on the left-hand menu.
Click on the “New” button to create a new integration runtime.
Choose the type of integration runtime you want to use, such as Azure or self-hosted.
Follow the prompts to configure the integration runtime, including setting up any required connectivity and authentication.
Use the integration runtime in your pipelines by selecting it as the execution environment for your pipeline activities.

Use Azure Data Factory pipeline parameters

In the Azure Data Factory UI, click on the “Author & Monitor” button to launch the Azure Data Factory UI.
Click on the “Pipelines” button on the left-hand menu.
Click on a pipeline to edit it.
Click on the “Parameters” tab to define pipeline parameters.
Use the pipeline parameters to make your pipelines more flexible and reusable.
Use the “Debug” feature to test your pipelines with different parameter values.

These are the main steps to get started with Azure Data Factory. However, there is much more to explore, such as using Azure Data Factory with Azure Databricks, creating data flows, and using Azure DevOps for continuous integration and deployment.

It is important to note that Azure Data Factory provides a wide range of documentation, tutorials, and samples to help you get started. You can also explore the Azure Marketplace for pre-built templates and connectors that can help you quickly set up pipelines for common scenarios.