Your guide to legendary data migrations
Success stories, lessons learned, and tools to turn your migration into a milestone achievement.
Validate and Prove Parity—the Modern Way
Every superhero story has a critical moment of truth—a test that proves whether the hero can rise to the challenge. In data migrations, that moment is validation. It’s not just about checking boxes or running tests—it’s about proving that your new system works as intended under real-world conditions.
This is often misunderstood as a purely technical step, but it’s far more than that. Validation determines whether stakeholders trust the migration, the new system, and your team’s ability to deliver. Without trust, even the most sophisticated systems will struggle to gain adoption.
What makes validation so challenging? It combines the highest stakes with the greatest complexity. To validate effectively, you must navigate:
- Cross-database differences in how databases handle queries, transformations, and data types.
- The scale of modern datasets, where manual validation is inefficient and impractical.
- Subtle, hard-to-detect edge cases that only surface during real-world use.
Traditional methods are manual, brittle, and incomplete, leaving critical discrepancies undetected in large-scale, complex migrations. This is why many migrations falter in the critical final stretch.
To succeed, data practitioners must carefully define the right validation methodology and leverage better tools that can handle the complexity and scale of modern systems.
Why traditional validation checks fall short
Many teams today still continue to rely on a combination of manual checks and basic aggregate comparisons. While these methods might work for small-scale migrations or simpler datasets, they quickly break down under the scale and complexity of modern data systems.
Why migrations are different today
Modern data migrations are fundamentally different from those of the past. They’re larger, more complex, and deeply intertwined with business-critical workflows. What’s driving this shift?
Massive datasets
Billions of rows and petabytes of data make manual or scripted validation impractical.
Cross-database differences
Migrations now frequently involve moving from legacy on-prem systems to modern cloud platforms like Snowflake, BigQuery, or Databricks. These systems differ in:
- Data types (e.g., handling of nulls or rounding).
- Query execution (e.g., join order, aggregation behavior).
- Transformations (e.g., implicit type casting or custom functions).
Business-critical workflows
Data isn’t just a byproduct—it’s a foundational asset for decision-making, analytics, and operations. Small discrepancies can cascade into significant business impacts, making precise validation non-negotiable.
How to validate like a hero
Modern migrations require modern approaches to validation. Here’s what teams need to do:
1. Establish baseline metrics
Before migration begins, define and document key metrics that will serve as benchmarks for success. These include:
- Row counts: Ensuring no records are lost or duplicated.
- Aggregates: Verifying sums, averages, and critical KPIs.
- Column-specific metrics: Checking fill rates, uniqueness, and cardinality to ensure data quality.
💡: Incorporate business logic into your baseline metrics, such as revenue splits by geography or churn rates by cohort. This ensures parity not just at the data level but in terms of the insights and decisions derived from it.
2. Use cross-database diffing
Row counts and aggregates only tell part of the story. The heart of modern validation lies in cross-database diffing—a granular, row-level comparison of data between the legacy and new systems.
- Catch subtle errors: Identify discrepancies caused by mismatches in null handling, type casting, or query logic.
- Resolve issues quickly: Pinpoint the exact rows or columns causing discrepancies, saving weeks of manual troubleshooting.
- Prove parity: Demonstrate that data integrity has been maintained, building trust across stakeholders.
💡: The Datafold Migration Agent automates cross-database diffing, comparing every row and column across massive datasets with pinpoint accuracy.
3. Validate iteratively
Validation isn’t a one-time task—it’s an ongoing process that should happen at multiple stages:
- During parallel runs: Validate while both systems are active to catch issues early, reducing risk before cutover.
- Post-migration updates: Re-validate whenever workflows are adjusted or optimized to ensure ongoing accuracy.
💡: The Datafold Migration Agent integrates validation into the entire migration workflow. Automated, scheduled validation ensures consistency without manual intervention, allowing teams to catch and fix issues iteratively.
Proving parity is about earning trust
Validation is too often misunderstood as a technical exercise—it’s a trust-building process. Stakeholders need clear, accessible proof that the new system is delivering results they can rely on.
A well-structured validation strategy turns skeptics into advocates by showing exactly where the new system aligns with the legacy one—and where it improves.
What makes a great validation report?
- High-level summaries: Show aggregate metrics to give a quick snapshot of parity.
- Granular insights: Include row-level discrepancies and root cause analysis so teams can quickly identify where issues arise and take corrective action.
- Trends over time: Highlight ongoing validation progress to reassure stakeholders at every stage.
The Datafold advantage
Validation is the hero’s trial of every migration. Modern migrations require more than traditional validation methods. With Datafold’s Migration Agent, you can:
- Validate massive datasets at scale with automated cross-database diffing.
- Catch subtle discrepancies that traditional methods miss.
- Build trust with clear, actionable reports that prove parity.
By integrating DMA into your migration process, you can confidently deliver a system that’s not just functional—but fully trusted by every stakeholder.
