AWS Data Pipeline is a web service that enables the automation of data workflows. It allows users to process and move data between different AWS compute and storage services as well as on-premises data sources. The service is designed to handle large-scale data processing, offering scheduling, dependency tracking, and error handling capabilities.

Here are some key features and limitations of AWS Data Pipeline:

### Features:
1. **Data Integrations**: It integrates well with other AWS services such as Amazon S3, Amazon RDS, DynamoDB, Amazon EMR, and Redshift.
2. **Scheduling**: It provides a flexible, time-based scheduling mechanism to automate tasks.
3. **Retry Logic**: Offers built-in retry logic in case of failures to ensure robustness in data processing workflows.
4. **Monitoring**: Users can monitor the execution of the pipelines and view logs using AWS Management Console.
5. **Complex Workflows**: Supports complex workflows with conditions and branching based on task success/failure.

### Limitations:
1. **Complexity**: Defining and managing complex workflows in AWS Data Pipeline can be intricate due to its JSON-based definition format.
2. **Limited Integration**: While it integrates well within AWS services, Data Pipeline lacks robust native connectors for external applications compared to newer tools.
3. **Scaling**: It might require manual intervention for scaling, which can be cumbersome when dealing with dynamically changing workloads.
4. **UI/UX**: The user interface is considered less intuitive by some users compared to newer AWS services such as Step Functions.
5. **Upkeep**: AWS Data Pipeline is not being actively developed, which means new features or updates are unlikely, pushing users to consider alternative AWS services.

### Migration Strategies to Step Functions/MWAA:

#### Migrating to AWS Step Functions:
AWS Step Functions is a serverless function orchestration service that enables you to build and execute multi-step workflows that are scalable and easy to maintain.

1. **Assessment**: Evaluate your current pipelines and identify tasks that can be converted into AWS Lambda. Step Functions are optimized for serverless architecture.
2. **State Machine Design**: Redesign your workflows using the visual workflow designer in Step Functions. This involves defining states and transitions using Amazon States Language.
3. **Integrations**: Leverage built-in service integrations in Step Functions to connect your workflows with AWS services like S3, Batch, Lambda, SNS, etc.
4. **Testing**: Utilize the Step Functions console to test workflows with mock data to ensure the migration has preserved functionality.
5. **Execution History and Monitoring**: Step Functions provides detailed visual execution history that will help in monitoring and debugging migrated pipelines.

#### Migrating to Managed Workflows for Apache Airflow (MWAA):
MWAA is a managed service for Apache Airflow that simplifies running and managing workflows within AWS.

1. **Analysis**: Examine the current pipelines to understand their components (tasks, dependencies, schedules).
2. **DAG Conversion**: Convert data pipelines into Directed Acyclic Graphs (DAGs), which are core to Airflow workflows. Each task in Data Pipeline should be translated into an Airflow task.
3. **Operator Mapping**: Utilize pre-built Airflow operators that correspond to AWS services (e.g., `S3ToRedshiftOperator`) to streamline the workflow translation.
4. **Configuration**: Configure MWAA environments including defining the DAG deployment strategy, Airflow variables, connections, and IAM roles.
5. **Validation and Testing**: Run the converted DAGs in MWAA to ensure they behave as expected with the appropriate schedules and dependencies.

By choosing Step Functions or MWAA for migration, organizations can leverage modern orchestration tools offering more features, better scalability, and enhanced user experience in building and managing workflows.