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Data Transformation & Validation
A curated collection of the best tools for transforming, cleaning and validating scraped records, including dataframe operations, SQL models, schemas and data-quality checks.
Quick answer
Turn extracted records into normalized, checked datasets. Polars handles dataframe operations, dbt transforms data already loaded into a warehouse, Pydantic validates record schemas, and Great Expectations checks dataset quality. These tools process supplied data; they do not fetch the source website or automatically provide a connector-based loading pipeline.
Frequently asked questions
What do you mean by data transformation?
Data transformation changes data into a form suitable for its next use. Examples include removing duplicate records, converting dates or units, mapping fields and aggregating values. In ETL it happens before loading; in ELT, transformation happens after loading into the destination.
Sources (checked ): AWS ETL overview