How we helped a Tier 1 financial institution safely migrate 12 billion+ records to the cloud.

When a major financial institution decided to modernise its post-trade regulatory reporting systems, it faced a task of enormous scale and sensitivity. The goal was to migrate more than 12 billion transaction records from legacy SQL infrastructure to a cloud platform, without disrupting reporting obligations or compromising data integrity. With regulatory requirements under MiFID II, the margin for error was almost non-existent. This is the story of how we partnered with them to deliver a smooth, secure migration, while laying the groundwork for a more agile, analytics-ready future.

1. The Hidden Costs of Assumption-Driven Migrations

Most large-scale data migrations fail quietly. Not with a dramatic system crash, but with thousands of silent mismatches, broken dependencies, and blind spots, all rooted in assumptions.

Assuming:

  • Source data is clean enough.
  • Target systems will behave predictably.
  • Sampling will be enough to verify accuracy.

When you’re working with regulatory datasets, like the 12+ billion transaction records we helped migrate, these assumptions can cost you millions — or even result in compliance breaches.

The takeaway? Assurance isn’t a step. It’s an infrastructure.

“Assurance isn’t a step. It’s an infrastructure.”

In high-stakes migrations, assuming your data is clean or that counts match just isn’t enough. Real confidence comes from engineering assurance into the foundation. That means designing your migration process to validate, reconcile, and monitor data continuously — not just at the end. When regulatory scrutiny is high and data volumes are massive, you can’t afford to “check it later.” Assurance must be built into every layer: from field-level comparisons to automated audit trails and real-time diagnostics. It’s not a task to tick off, it’s the backbone of a successful transformation.

2. Why Volume Checks Aren’t Enough

Traditional data reconciliation often stops at volume metrics:

  • “Did we migrate all 12 billion rows?”
  • “Do the record counts match?”

But for post-trade regulatory data governed by frameworks like MiFID II, that’s not even close to enough. Every single field (timestamp, trade ID, price, counterparty) must retain fidelity through the transformation process.

That’s where attribute-level validation comes in.

 3. The Power of Attribute-Level Validation

Our client needed to prove that the migrated data was not just complete in terms of volume, but precise down to the last decimal point and timestamp, across five years of historical records.

We engineered a framework to:

  • Reconcile every field across billions of records
  • Spot even subtle schema mismatches
  • Identify corruption patterns (e.g., character encoding, format shifts)
  • Track lineage between the old SQL structure and Snowflake’s new model

This gave the client real-time confidence and allowed their internal teams to fix issues before they became systemic.

 

“Volume checks tell you what moved. Attribute-level validation tells you what survived.”

When every timestamp and trade ID matters, precision at the field level is the difference between compliance and chaos. In regulatory environments, record counts alone offer a false sense of security. You need to know that every field, from instrument identifiers to counterparty names, has retained its accuracy and meaning across systems. Without this level of detail, reporting errors can quietly accumulate, triggering costly downstream consequences. Attribute-level validation brings clarity to complexity, surfacing hidden mismatches before they become critical failures. It's the only way to turn a high-risk migration into a resilient, audit-ready outcome.

4. Tooling That Sees Everything: Why We Used Data360 Analyze

Precisely’s Data360 Analyze was the powerhouse behind this success, but it wasn’t just the tool, it was how we implemented it.

As long-standing implementation partners of Precisely, we know how to unlock its full capabilities for financial services environments. For this migration, we tailored Data360 Analyze to:

  • Automate granular data comparisons
  • Build audit trails across source and target environments
  • Prototype migration logic before full execution
  • Enable root cause diagnostics and iterative correction workflows

This approach reduced manual QA to near-zero and accelerated the entire migration timeline.

Lessons You Can Apply to Your Next Migration

Design for assurance, not just execution
QA shouldn't be bolted on at the end. Make it foundational.

Granularity is everything
Record-level assurance helps avoid nasty surprises after go-live.

Use purpose-built tooling and experts
Generic ETL tools aren’t built for this. Domain expertise + powerful validation tooling = success.

Want to see how we delivered this assurance framework at scale?

Read or Download the full anonymised Use Case here.
Or contact us to discuss how we can support your next high-risk data transformation.

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