Data Factory reference documentation

This section groups together the core reference documentation for the Product-Live Data Factory platform. It explains how jobs are defined, how they run, which tasks are available, and how to configure cross-cutting concerns such as variables and error handling.

Use the following pages as building blocks when designing and operating your Data Factory workflows:

• Jobs: Define what runs. A job describes a set of tasks executed in a specific order to implement a business process (import, transformation, export, etc.).
• Job executions: Inspect how it runs. A job execution is a concrete run of a job, with its status, inputs, outputs, and task history.
• Periodic jobs: Automate job schedules. Configure cron expressions to run jobs automatically at recurring intervals, from every 15 minutes to once a year.
• Tasks: Use available building blocks. Each task is a single operation (data transformation, import, export, HTTP call, notification, etc.) that can be orchestrated inside a job.
• Variables: Share and reuse values. Variables (global and local) let you store configuration and intermediate results across tasks and jobs.
• Handling failures: Control timeouts and errors. Learn how to configure job-level timeouts and react to task failures within a job.
• Best practices: Apply recommended patterns. Naming conventions and design guidance to keep your Data Factory setup maintainable and consistent.
• Specialized processing: Extend the platform. Delegate specific task processing to your own infrastructure when you need advanced customization, security, or performance.