To create an Extract, Transform, Load (ETL) process using Microsoft Azure, you can leverage several Azure services and tools. Here's a high-level overview of the steps involved:

1. Define the ETL requirements: Clearly define the data sources, transformation logic, and target destination for your ETL process. Understand the data formats, volume, frequency, and any specific business rules or requirements.
2. Choose the appropriate Azure services: Azure offers various services that can be combined to build an ETL pipeline. Commonly used services include Azure Data Factory, Azure Databricks, Azure SQL Database, Azure Blob Storage, and Azure Data Lake Storage.
3. Set up the data storage: Create the necessary storage resources in Azure to hold the source and target data. Azure Blob Storage or Azure Data Lake Storage can be used to store the raw or intermediate data.
4. Create an Azure Data Factory (ADF) pipeline: Azure Data Factory is a fully managed data integration service that allows you to create, schedule, and manage data pipelines. Start by creating an ADF pipeline and define the data sources, transformations, and target destinations in a visual interface or using JSON-based code.
5. Define the data sources: Configure the data sources in Azure Data Factory to extract data from various sources such as databases, files, APIs, or streaming services. Azure Data Factory supports a wide range of connectors to connect to different data sources.
6. Transform the data: Use Azure Data Factory's data transformation capabilities or integrate Azure Databricks for complex data transformations. Azure Databricks provides a scalable Apache Spark-based analytics platform to perform advanced data transformations and computations.
7. Load the data: Define the target destinations where the transformed data will be loaded. It could be Azure SQL Database, Azure Data Lake Storage, Azure Blob Storage, or any other compatible data stores. Configure the appropriate connectors and credentials to load the data.
8. Schedule and monitor the ETL process: Set up a schedule for your ETL pipeline in Azure Data Factory to run at regular intervals or trigger it based on specific events. Monitor the pipeline's execution, track data quality, and configure alerts or notifications for any failures or anomalies.
9. Error handling and retries: Implement error handling and retry mechanisms within your ETL process to handle any data loading failures or exceptions. Azure Data Factory provides built-in error handling features like retries, logging, and notifications.
10. Testing and deployment: Thoroughly test your ETL pipeline to ensure it meets the desired outcomes. Validate the data transformation, monitor performance, and adjust the pipeline as needed. Finally, deploy the ETL pipeline to the production environment.

Remember to consider security, data privacy, and compliance requirements while designing and implementing your ETL process in Azure. Azure provides various security features and compliance certifications to help you meet your organization's requirements.

It's important to note that the above steps provide a general guideline, and the specific implementation details may vary based on your data sources, transformations, and target destinations. Azure documentation and tutorials can provide more detailed guidance on using specific Azure services for your ETL needs.