ETL’s Final Frontier: Post-Migration Cleanup Strategies
Migrating data from one system to another sounds simple—until you realize how much can go wrong. From mismatched formats to missing records, the journey from source to destination is riddled with potential pitfalls. That’s why validation and cleanup aren’t just technical steps—they’re the backbone of a successful migration.
🚀 Why Data Migration Matters
Whether you’re upgrading systems, consolidating platforms, or moving to the cloud, data migration is a critical process. But without proper validation and cleanup, you risk:
• Corrupted data
• Inaccurate reporting
• Compliance issues
• Frustrated users
✅ Step 1: Validation—Trust, But Verify
Validation ensures that the data you’ve moved is accurate, complete, and usable. Here’s how to do it right:
• Schema Matching: Confirm that fields in the source match those in the destination.
• Record Counts: Compare the number of records before and after migration.
• Data Sampling: Manually inspect a subset of records for accuracy.
• Automated Scripts: Use SQL or ETL tools to run validation checks at scale.
💡 Pro Tip: Build validation into your migration pipeline—not as an afterthought.
🧼 Step 2: Cleanup—Polishing the Final Product
Even validated data can be messy. Cleanup ensures that your migrated data is not just correct, but clean and consistent.
• Remove Duplicates: Identify and eliminate redundant records.
• Standardize Formats: Dates, phone numbers, and addresses should follow a consistent format.
• Fix Nulls and Blanks: Fill in missing values or flag them for review.
• Audit Trails: Keep logs of changes for transparency and compliance.
Validation SQL Scripts
Compare the number of records between source and target tables.
- Source table
SELECT COUNT(*) AS source_count FROM source_table;
-- Target table
SELECT COUNT(*) AS target_count FROM target_table;
Use a checksum to verify data integrity across tables.
-- Source checksum
SELECT CHECKSUM_AGG(CHECKSUM(*)) AS source_checksum FROM source_table;
-- Target checksum
SELECT CHECKSUM_AGG(CHECKSUM(*)) AS target_checksum FROM target_table;
Compare specific fields between source and target.
SELECT s.id, s.name AS source_name, t.name AS target_name
FROM source_table s
JOIN target_table t ON s.id = t.id
WHERE s.name <> t.name;
Cleanup SQL Scripts
Identify and delete duplicate rows based on key fields.
DELETE FROM target_table
WHERE id NOT IN (
SELECT MIN(id)
FROM target_table
GROUP BY name, email
);
Convert inconsistent date formats to a standard one.
UPDATE target_table
SET date_field = TO_DATE(date_field, 'YYYY-MM-DD')
WHERE date_field IS NOT NULL;
Replace NULLs with default values.
UPDATE target_table
SET phone_number = 'N/A'
WHERE phone_number IS NULL;
📈 The Payoff
Clean, validated data leads to:
• Better decision-making
• Higher user trust
• Easier audits and compliance
• Fewer support tickets
Conclusion
Data migration isn’t just about moving bytes—it’s about preserving meaning. By investing time in validation and cleanup, you ensure that your data remains a reliable asset, not a liability.
