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Schema Evolution: Handling Changing Data Structures
How to handle changing data structures, using a real public dataset whose schema changed three ways in three years: detecting an added column, a renamed column and five type changes, why a strict merge refuses and a permissive one keeps both spellings, reading by name, compatibility rules, and safe practices.
Slowly Changing Dimensions (SCD) Types Explained
The slowly changing dimension types every data engineer uses: Type 0, 1, 2 and 3 and when each fits, a working example on the real Chinook database where a support-rep reassignment makes Type 1 misattribute revenue while Type 2 keeps it right, how to load Type 2 rows, and the mistakes that corrupt history.
Slowly Changing Dimensions in ADF Data Flows
A slowly changing dimension keeps history when an attribute such as a customer's city changes. This guide builds the Type 2 load as an Azure Data Factory data flow, stream by stream, reproduces it on real data over four loads, and covers surrogate keys, sink order, deletes and safe reruns.