12 Workflows

Workflows in Oracle AI Data Platform provide a powerful and flexible way to automate data processing tasks. With workflows, users can define and orchestrate complex data pipelines that can run on-demand and based on a pre defined schedule. Workflows can be composed of multiple tasks, each performing a specific action, and can include advanced features such as dependencies, triggers, and error handling.

Topics:

Key Features of AI Data Platform Workflows

  • Automation: Automate complex data tasks and processes.
  • Orchestration: Define the sequence and dependencies of tasks in a pipeline.
  • Scheduling: Run workflows on a schedule or trigger based on specific events.
  • Monitoring: Track workflow status, logs, and execution history.
  • Parameterization: Pass parameters to customize the behavior of workflows and tasks.

Core Concepts

  • Job: A collection of tasks executed in sequence or parallel to complete a data processing job.
  • Task: The individual steps that make up a workflow. Tasks can include actions like running Python code, executing a notebook, if-else task or running another job task.
  • Job Run: An instance of a job execution. A job can be triggered multiple times, each time representing a new job run.
  • Trigger: Defines the conditions under which a workflow is executed, such as on a schedule, or if it's manually triggered.
  • Dependencies: Define the order of task execution or specify conditions under which certain tasks run.
  • Parameters: Values passed to workflows or tasks to customize their execution. Parameters can be defined at the job, task, or runtime level.

Benefits/Use Cases of Using Workflows

  • Streamlined Automation - Simplify the execution of recurring data tasks by automating them through workflows.
  • Parallel Processing - Speed up data processing by running tasks in parallel.
  • Customizable Execution - Modify workflows at runtime with parameters to meet specific needs.
  • Improved Efficiency - Reduce manual interventions and errors, enabling smoother operations.

Workflows in AI Data Platform enable a wide range of use cases, including automated ETL pipelines, data integration from multiple sources, and advanced analytics. Users can automate data quality monitoring, machine learning model training, and deployment. These capabilities drive efficiency and scalability for modern data-driven workflows.

Best Practices

  • Task Modularization - Break down workflows into reusable tasks to simplify management and improve maintainability.
  • Efficient Resource Allocation - Optimize workflows for better performance by running tasks in parallel when appropriate.
  • Error Handling - Use retries, error notifications, and fallback mechanisms to ensure workflows run reliably.
  • Compute Assignment - Assign specific compute resources to each task based on workload size, optimizing performance and cost.

By following these best practices, you can design workflows that are scalable, reliable, and efficient, ensuring optimal performance and easier management in Oracle AI Data Platform.