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Data Transformation

What is data transformation?

‍A data transformation is a function that is applied to some input data that changes the data in such a way that the data is easier to consume by downstream applications or users (often business intelligence or ML). Data transformations can take many forms, from simple operations such as filtering or sorting, to complex algorithms such as grouped aggregations, binning, dimensionality reduction, or data cleaning. 

‍Why are Data Transformations important?

Data transformations are important because they allow raw data to be cleaned, processed, and standardized, making it easier to work with and analyze. By transforming data into a format that is compatible with downstream applications and systems, data transformations can help improve the accuracy and efficiency of machine learning models and other data-driven applications.

‍Important classes of data transformations used for ML systems include 

  • Stateless transformations - they can be implemented as idempotent functions, making them easy to re-execute in case of failures, 
  • Partitionable - the transformation can be run in parallel over many workers, enabling scalable/faster transformations,  
  • Window-based aggregations - streaming pipelines accumulate state in windows over time and fixed-sized window aggregations can be computed by batch pipelines, 
  • ML transformations that are based on a reference window (such as encoding of numerical/categorical features for data compatibility or performance).

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