Concepts

Pipeline

The Pipeline entity represents a series of data processing steps organized into a coherent workflow within the data platform. It typically involves a sequence of transformations, data movements, and other processing tasks, structured to accomplish a specific data management goal. Pipelines are fundamental in orchestrating the flow of data from source to destination, ensuring that each step is executed in the correct order and manner.

Properties

  • ID: A unique identifier for the pipeline.

  • Name: A descriptive name for the pipeline, indicating its purpose or the type of data processing it performs.

  • Schedule: (Optional) If the pipeline is scheduled to run automatically, details of the scheduling (e.g., frequency, time).

Usage

  • Data Processing Workflows: Pipelines automate and manage complex workflows involving multiple steps of data processing.

  • Error Handling and Recovery: They include mechanisms to handle failures in individual steps and provide options for recovery and reruns.

  • Monitoring and Optimization: Pipelines are monitored for performance and can be optimized for efficiency, speed, and resource utilization.

Best Practices

  • Modular Design: Design pipeline steps to be modular and reusable, facilitating maintenance and scalability.

  • Documentation: Maintain clear documentation for each pipeline step, including its purpose, input, output, and any special considerations.

Properties

  • ID: A unique identifier for the pipeline.

  • Name: A descriptive name for the pipeline, indicating its purpose or the type of data processing it performs.

  • Schedule: (Optional) If the pipeline is scheduled to run automatically, details of the scheduling (e.g., frequency, time).

Usage

  • Data Processing Workflows: Pipelines automate and manage complex workflows involving multiple steps of data processing.

  • Error Handling and Recovery: They include mechanisms to handle failures in individual steps and provide options for recovery and reruns.

  • Monitoring and Optimization: Pipelines are monitored for performance and can be optimized for efficiency, speed, and resource utilization.

Best Practices

  • Modular Design: Design pipeline steps to be modular and reusable, facilitating maintenance and scalability.

  • Documentation: Maintain clear documentation for each pipeline step, including its purpose, input, output, and any special considerations.

Properties

  • ID: A unique identifier for the pipeline.

  • Name: A descriptive name for the pipeline, indicating its purpose or the type of data processing it performs.

  • Schedule: (Optional) If the pipeline is scheduled to run automatically, details of the scheduling (e.g., frequency, time).

Usage

  • Data Processing Workflows: Pipelines automate and manage complex workflows involving multiple steps of data processing.

  • Error Handling and Recovery: They include mechanisms to handle failures in individual steps and provide options for recovery and reruns.

  • Monitoring and Optimization: Pipelines are monitored for performance and can be optimized for efficiency, speed, and resource utilization.

Best Practices

  • Modular Design: Design pipeline steps to be modular and reusable, facilitating maintenance and scalability.

  • Documentation: Maintain clear documentation for each pipeline step, including its purpose, input, output, and any special considerations.

Properties

  • ID: A unique identifier for the pipeline.

  • Name: A descriptive name for the pipeline, indicating its purpose or the type of data processing it performs.

  • Schedule: (Optional) If the pipeline is scheduled to run automatically, details of the scheduling (e.g., frequency, time).

Usage

  • Data Processing Workflows: Pipelines automate and manage complex workflows involving multiple steps of data processing.

  • Error Handling and Recovery: They include mechanisms to handle failures in individual steps and provide options for recovery and reruns.

  • Monitoring and Optimization: Pipelines are monitored for performance and can be optimized for efficiency, speed, and resource utilization.

Best Practices

  • Modular Design: Design pipeline steps to be modular and reusable, facilitating maintenance and scalability.

  • Documentation: Maintain clear documentation for each pipeline step, including its purpose, input, output, and any special considerations.

Properties

  • ID: A unique identifier for the pipeline.

  • Name: A descriptive name for the pipeline, indicating its purpose or the type of data processing it performs.

  • Schedule: (Optional) If the pipeline is scheduled to run automatically, details of the scheduling (e.g., frequency, time).

Usage

  • Data Processing Workflows: Pipelines automate and manage complex workflows involving multiple steps of data processing.

  • Error Handling and Recovery: They include mechanisms to handle failures in individual steps and provide options for recovery and reruns.

  • Monitoring and Optimization: Pipelines are monitored for performance and can be optimized for efficiency, speed, and resource utilization.

Best Practices

  • Modular Design: Design pipeline steps to be modular and reusable, facilitating maintenance and scalability.

  • Documentation: Maintain clear documentation for each pipeline step, including its purpose, input, output, and any special considerations.

Transform

Execution

© Copyright 2023. All rights reserved.

Concepts

Pipeline

The Pipeline entity represents a series of data processing steps organized into a coherent workflow within the data platform. It typically involves a sequence of transformations, data movements, and other processing tasks, structured to accomplish a specific data management goal. Pipelines are fundamental in orchestrating the flow of data from source to destination, ensuring that each step is executed in the correct order and manner.

Properties

  • ID: A unique identifier for the pipeline.

  • Name: A descriptive name for the pipeline, indicating its purpose or the type of data processing it performs.

  • Schedule: (Optional) If the pipeline is scheduled to run automatically, details of the scheduling (e.g., frequency, time).

Usage

  • Data Processing Workflows: Pipelines automate and manage complex workflows involving multiple steps of data processing.

  • Error Handling and Recovery: They include mechanisms to handle failures in individual steps and provide options for recovery and reruns.

  • Monitoring and Optimization: Pipelines are monitored for performance and can be optimized for efficiency, speed, and resource utilization.

Best Practices

  • Modular Design: Design pipeline steps to be modular and reusable, facilitating maintenance and scalability.

  • Documentation: Maintain clear documentation for each pipeline step, including its purpose, input, output, and any special considerations.

Transform

Execution

© Copyright 2023. All rights reserved.