Amazon Web Services (AWS) Data Pipeline is a fully managed extract, transform, and load (ETL) service that makes it easy for you to move data between data stores. It is designed to process and transform data for a wide range of data processing scenarios, such as batch data processing, data migration, and log analysis. 

With AWS Data Pipeline, you can define data-driven workflows, schedule recurring data movement and data processing tasks, and monitor the progress and success of tasks. You can also use Data Pipeline to move data between on-premises data stores and AWS data stores or between different AWS data stores. 

AWS Data Pipeline is a serverless service, which means you do not need to provision any infrastructure or worry about managing resources. You only pay for the resources you consume while running your tasks. 

Here are some key features of AWS Data Pipeline: 

• Data transformation: You can use Data Pipeline to transform data using AWS Glue, Amazon EMR, or custom scripts. 
• Data scheduling: You can schedule data processing tasks to run on a regular basis, such as daily or hourly. 
• Data orchestration:You can use Data Pipeline to orchestrate complex data processing workflows that involve multiple tasks and data stores. 
• Data integration:You can use Data Pipeline to integrate data from various sources, such as databases, file systems, and cloud storage. 
• Data security:Data Pipeline uses Amazon S3 and Amazon EMR for data storage and processing, which provides secure data storage and processing capabilities
• Data monitoring: You can monitor the progress and success of your data processing tasks using the AWS Management Console, the AWS CLI, or the Data Pipeline API. 

Overall, AWS Data Pipeline is useful for ETL tasks and data processing workflows. It can help you move, transform, and process data efficiently and securely, without the need to manage any infrastructure. 

Let us a use case of AWS Data Pipeline: 

Here is a step-by-step tutorial on how to use AWS Data Pipeline to move data from an Amazon S3 bucket to an Amazon Redshift cluster: 

1. Sign in to the AWS Management Console and navigate to the AWS Data Pipeline console. 
2. Click on the "Create new pipeline" button. 
3. Give your pipeline a name and a unique identifier. 
4. Choose "S3 to Redshift" as the template for your pipeline. 
5. In the "S3 location" field, enter the URL of the S3 bucket that contains the data you want to move.
6. In the "Redshift cluster" field, select the Redshift cluster where you want to load the data. 
7. In the "Table name" field, enter the name of the table in your Redshift cluster where you want to load the data. 
8. To proceed to the next step click on the "Continue" button. 
9. In the "Schedule" section, choose how often you want the pipeline to run. You can choose to run it once, or on a recurring basis. 
10.Click on the "Activate" button to activate the pipeline. 
11.Wait for the pipeline to complete. You can monitor the progress of the pipeline in the AWS Data Pipeline console. 

That's it! Your data should now be transferred from the S3 bucket to the Redshift cluster according to the schedule you specified. 

You can also use AWS Data Pipeline to transform and process your data using AWS Glue or Amazon EMR before loading it into your target data store. To do this, you can use the "Custom" template when creating your pipeline, and specify your own data processing logic using a script or a custom component.