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Transform.

Load.

In practice, enterprise ecommerce data is rarely clean enough for that process to be simple.

Product information may contain inconsistent attributes.

Customer records may be duplicated.

Order histories may span several schemas.

Legacy platforms may contain fields nobody understands anymore.

Pricing and promotion rules can be especially complicated.

The real work often includes:

• data profiling
• mapping
• cleansing
• normalization
• deduplication
• transformation
• validation
• reconciliation

This work should begin early.

Discovering data problems immediately before launch is one of the easiest ways to delay a migration.