What is a best practice for data sharing across playbooks?

• A. Use the py-postgresq1 module to directly save the data in the Postgres database.
• B. Call the child playbooks getter function.
• C. Create artifacts using one playbook and collect those artifacts in another playbook.
• D. Use the Handle method to pass data directly between playbooks.

Answer: (C)

Creating artifacts in one playbook and collecting those artifacts in another playbook is a best practice for data sharing across playbooks. This approach encapsulates the data generated during the execution of a playbook in a structured format, which can then be easily retrieved and utilized by subsequent playbooks. Artifacts are essential for maintaining context and ensuring that the necessary information flows smoothly between different automated processes.

When next playbooks are triggered, they can access these artifacts to continue the workflow seamlessly, thus enhancing the modularity and reusability of playbooks. This structure also promotes better management of data as it allows for organizing and categorizing information in a way that is understandable and logical.

Utilizing other methods, such as calling a child playbook’s getter function or passing data through the Handle method, can sometimes lead to complications or reduced clarity in data management. These approaches might not be as standardized or manageable as the artifact creation and collection practice, potentially leading to inefficiencies or difficulties in tracking data across multiple workflows.