9 Ways to Screw Up a Data Migration

Data platform migrations are challenging and complex projects that often exceed timelines and budgets, as highlighted by a data engineer's experiences at Lyft and as CEO of Datafold. The key to successful migrations involves anticipating potential failure modes and creating robust defenses against them, as well as starting the migration process before legacy systems become too overloaded. Choosing the right technology stack is crucial, as demonstrated by a failed migration from Redshift to Hive, and piece-mealing migrations can significantly increase complexity and risk. While outsourcing can be beneficial, fixed-price, guaranteed-timeline arrangements are preferred over billable hours to align incentives. Clear acceptance criteria are essential for success, with user acceptance testing and data parity playing critical roles in achieving stakeholder confidence. Moreover, addressing technical debt by modernizing code without altering data models or business logic is advised to prevent delays. Engaging with target platform account teams for migration incentives can also positively impact the project's economics. Ultimately, while AI can greatly assist in accelerating migrations, success is largely dependent on strategic decision-making by data leaders.

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