Parametric Data Pipeline

Last updated Feb 16, 2025

Once the data is normalized into the unified model, the next component is the parametric pipeline. This pipeline is designed to transform the normalized data into datasets that enable meaningful insights, metrics, and KPIs. The term “parametric” refers to the ability of the pipeline to adapt to various configurations and customizations through parameters, while adapting the underlying logic.

For example, a parametric pipeline could compute common SaaS metrics like MRR, churn, and user engagement. However, rather than hard-coding the logic for each use case, the pipeline would allow for parameters such as timeframes, custom segments, or business-specific rules (e.g., handling customer upgrades/downgrades differently). This ensures that while the pipeline is standardized, it can also be flexible enough to accommodate a wide range of business scenarios and analytics needs.

Parametric pipelines also offer scalability. Instead of reinventing metrics or transformations each time the data model or source changes, businesses can rely on the same pipelines, adjusting parameters to account for their specific requirements. For instance, you might use the same pipeline to compute retention metrics for different product lines or segments of users by simply changing the input parameters. Why Data Teams Keep Reinventing the Wheel: The Struggle for Code Reuse in the Data Transformation Layer by Maxime Beauchemin

References to: book, reusability.

Another key question around the foundation is whether to adopt a parametric approach or a template-and-fork strategy. A purely parametric approach can create a complex black box that’s hard to modify. On the other hand, a template-oriented approach means that once you fork, you’re largely on your own. The best solution might be a blend of both: a solid foundation with parametric capabilities, while allowing for forking when needed.

Origin: Why Data Teams Keep Reinventing the Wheel: The Struggle for Code Reuse in the Data Transformation Layer | Preset
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Created 2024-10-04