When designing workflows for data recovery, data engineers need to consider various methods and tools based on their preference and the data system's requirements. To guide the process, some general steps and principles should be followed. Data engineers must identify the data sources to be recovered, such as databases, files, or streams, and their respective destinations such as data warehouses, data lakes, or pipelines. It is also important to consider the formats, schemas, and metadata of the sources and destinations, as well as how they can be matched or transformed. Additionally, a data recovery strategy must be defined; this could include full recovery, incremental recovery, or differential recovery depending on the data volume, frequency, and availability. Furthermore, criteria such as time range, data quality, or completeness should be established to determine when and how the data recovery process is completed. The workflow tasks and steps that implement the strategy must also be designed; this could include extracting, transforming, loading, validating or cleaning the data. Additionally, workflow logic and dependencies that control the execution order of tasks and steps must also be established. Finally, suitable workflow tools and platforms need to be chosen; these could range from simple scripts or code to specialized frameworks or services like Apache Airflow or Google Cloud Composer. These tools provide features and functions that facilitate workflow creation, execution monitoring and debugging.