etl

The applied data-wrangling screen: here is a grubby database, turn out a clean analysis-ready table. The signal is profiling before transforming, doing set-based cleaning in SQL and row-level fixes in Python, joining without fanning out rows, and validating the output rather than trusting it.

Raw clickstream lands in object storage and somehow turns into clean tables, dashboards, and ML features. The design questions are table format, the bronze-silver-gold layering, batch versus streaming, and how you handle schema drift and late data. Here is the lakehouse blueprint.