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dbt is more than a tool for engineers. It’s a framework for building reliable, production-grade data pipelines—a critical skill for the modern data scientist.
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This blog post provides practical tips and tricks for data cleaning, covering methods to handle missing values, standardize formats, remove duplicates, deal with outliers, clean text, and automate the cleaning process. It emphasizes the importance of proper data cleaning as a foundation for meaningful data analysis.