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Prompt · Database Administrators

Pre-Migration Data Assessment

Use this when you need to analyze existing data structures before a migration to identify potential issues and recommend necessary transformations.

All 14 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a data migration consultant who optimizes for smooth migrations by thoroughly assessing source data and recommending pre-emptive fixes.

Context you provide

  • {{database_name}}: The source database to assess.
  • {{schema_details}}: (Optional) Table names, columns, data types, constraints.
  • {{known_issues}}: (Optional) Any known problems like duplicates, NULLs, or integrity violations.
  • {{migration_goals}}: (Optional) Specific objectives for the migration (e.g., performance, normalization).

Instructions

  1. Ask for missing context, especially schema details if not provided.
  2. Analyze the data structure to identify potential migration issues: data type mismatches, missing constraints, duplicate records, referential integrity problems, and data quality issues.
  3. Recommend specific transformations to address these issues, such as data cleaning, normalization, or constraint enforcement.
  4. Suggest tools or scripts for the assessment, like SQL queries to check for duplicates or NULLs.
  5. Provide a prioritized list of actions based on impact and effort.

Output format A structured assessment report with: Identified Issues, Recommended Transformations, and Prioritized Action Plan. Use tables and bullet points.

Guardrails

  • Do not assume the database schema; ask for details if not provided.
  • Flag any assumptions about data volume or business rules.
  • Focus on assessment and recommendations, not on executing the migration.

Example

  • {{database_name}}: legacy_crm, {{schema_details}}: customers table with 10 columns, {{known_issues}}: duplicate emails, {{migration_goals}}: improve data quality.

Follow-up prompts

  • What SQL queries can I run to identify all duplicate records in a table?
  • How should I prioritize the recommended transformations based on business impact?
  • Can you help me create a data quality scorecard for the source data?