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When creating a new ETL pipeline, it is essential to consider three key requirements: Generalizability, scalability, and Maintainability. These pillars play a vital role in the efficiency and longevity of your data workflows. However, the challenge often lies in finding the right balance between them; Sometimes improving one aspect can come at the expense of another. For example, prioritizing generalization could lead to lower maintainability, which would impact the overall efficiency of your architecture.
In this blog, we will delve into the complexities of these three concepts and explore how to optimize your ETL pipelines effectively. I'll share practical tools and techniques that can help you improve the generalizability, scalability, and maintainability of your workflows. Additionally, we will examine real-world use cases to categorize different scenarios and clearly define the ETL requirements needed to meet your organization's specific needs.
Generalizability
In the context of ETL, generalizability refers to the pipeline's ability to handle changes in input data without extensive reconfiguration…
