Living in Both Houses for a While

Opening Scene

A family transitioning to a new home sometimes keeps the old one briefly available, splitting time between both, specifically to confirm the new home genuinely works as expected before fully committing and giving up the old one entirely. This deliberate overlap period, while temporarily more effortful, provides real confidence before an irreversible commitment. A parallel run in data migration provides this exact same valuable, confirming overlap.

In Plain English

A parallel run means operating both the source and destination systems simultaneously for a defined period, comparing their outputs to confirm the new system produces genuinely correct, equivalent results before fully cutting over and decommissioning the old system. This is more operationally effortful than an immediate cutover, but it provides genuine confidence that the migration actually preserved correctness, caught through direct comparison rather than assumption.

The Old Way

Before parallel runs were a well-established, deliberately practiced validation technique, migrations often cut over without this direct comparison:

Migrations sometimes cut over directly from the old system to the new one, without a period of parallel operation to directly compare outputs.
There wasn’t yet a well-established practice of treating parallel operation as a deliberate validation step, rather than an unnecessary, temporary inefficiency to avoid.
Correctness problems in the new system were sometimes discovered only after the old system had already been decommissioned, when reverting was considerably harder.

Cutting over without a period of parallel comparison is what disciplined parallel run practice directly addresses.

What’s Changing (and Why AI Is the Reason)

Organizations increasingly run source and destination systems in parallel for genuinely critical migrations, directly comparing outputs before fully committing to the new system.
This connects directly to the phased migration approach covered in Article 8, since a parallel run is often the specific validation technique used at each phase of an incremental migration.
As AI model outputs can be genuinely difficult to validate through simple, deterministic comparison, parallel runs for AI-dependent systems sometimes require deliberately designed comparison metrics specifically suited to evaluating whether outputs remain acceptably consistent, not just identical.

The Metaphor, Fully Extended

Moving Day	Data Platform Migration Concept
Keeping the old home briefly available during the transition	Operating both source and destination systems simultaneously
Splitting time between both to confirm the new home works	Comparing outputs to confirm the new system produces correct results
More effortful, but providing real confidence	More operationally effortful, but providing genuine confidence
Confirming correctness before an irreversible commitment	Confirming correctness before decommissioning the old system

For Beginners: What to Actually Do

Practice imagining what it would mean to compare the outputs of two systems processing the same data simultaneously.
Learn to recognize parallel runs as a deliberate validation technique, not an inefficiency to avoid.
Get comfortable with the idea that this extra effort provides genuine confidence before an otherwise hard-to-reverse commitment.

For Practitioners and Leaders: The Deeper Layer

Run genuinely critical migrations in parallel for a defined period, directly comparing source and destination outputs before full cutover.
Connect parallel run planning directly to the phased migration approach covered in Article 8.
Design deliberately suited comparison metrics for AI-dependent systems, where outputs may need to be acceptably consistent rather than strictly identical.

Quick Recap

A parallel run operates old and new systems simultaneously, comparing outputs to confirm correctness before cutover.
This is more operationally effortful than an immediate cutover, but provides genuine confidence.
Discovering correctness problems after decommissioning the old system is considerably harder to address.
AI-dependent systems may need specifically designed comparison metrics rather than simple output matching.

Where This Fits in the Series

Article 9 covered validating a migration through direct, parallel comparison. Article 10 turns to a related safeguard: the moving company’s insurance policy.