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The Pipelines module of YData Fabric is a general-purpose job orchestrator with built-in scalability and modularity plus reporting and experiment tracking capabilities. With automatic hardware provisioning, on-demand or scheduled execution, run fingerprinting and a UI interface for review and configuration, Pipelines equip the Fabric with operational capabilities for interfacing with up/downstream systems (for instance to automate data ingestion, synthesis and transfer workflows) and with the ability to experiment at scale (crucial during the iterative development process required to discover the data improvement pipeline yielding the highest quality datasets).

YData Fabric's Pipelines are based on Kubeflow Pipelines and can be created via an interactive interface in Labs with Jupyter Lab as the IDE (recommended) or via Kubeflow Pipeline’s Python SDK.

With its full integration with Fabric's scalable architecture and the ability to leverage Fabric’s Python interface, Pipelines are the recommended tool to scale up notebook work to experiment at scale or move from experimentation to production.


Using Pipelines for data preparation offers several benefits, particularly in the context of data engineering, machine learning, and data science workflows. Here are some key advantages:

  • Modularity: they allow to break down data preparation into discrete, reusable steps. Each step can be independently developed, tested, and maintained, enhancing code modularity and readability.
  • Automation: they automate the data preparation process, reducing the need for manual intervention and ensuring that data is consistently processed. This leads to more efficient workflows and saves time.
  • Scalability: Fabric's distributed infrastructure combined with kubernetes based pipelines allows to handle large volumes of data efficiently, making them suitable for big data environments.
  • Reproducibility: By defining a series of steps that transform raw data into a ready-to-use format, pipelines ensure that the same transformations are applied every time. This reproducibility is crucial for maintaining data integrity and for validating results. Maintainability:
  • Versioning: support versioning of the data preparation steps. This versioning is crucial for tracking changes, auditing processes, and rolling back to previous versions if needed.
  • Flexibility: and above all they can be customized to fit specific requirements of different projects. They can be adapted to include various preprocessing techniques, feature engineering steps, and data validation processes.