DATA PLATFORM MODERNIZATION

Parallel run with row-level reconciliation, then cutover on clean evidence rather than on a calendar date.

Both systems will be run against the same inputs for 4-8 weeks, while outputs are compared row by row and not spot-checked. Users transfer only when the reconciliation is clean and not on a scheduled date.

Pipeline complexity, amount of retirement allowed, source system accessibility, regulations, number of consumers, how quickly the data needs to be settled, and the ownership of the data.

Data volume isn’t on that list. Rather, the main cause is pipeline count and unwritten code. It is easier to manage a hundred terabytes of data moved by twelve clean pipelines than it is to move two terabytes of data by four hundred.

Down over a three-year view in most cases, but up first, because you run both platforms during migration, typically for three to nine months.

That interval and the associated cost should be described in the business case during the first pass. Cloud compute pricing is consumption based, so fixed-license teams experience sticker shock on the first bill. For both of these examples, we provide modeling and cost guardrails before launching the system.

The route depends on your deadline, how well the current architecture fits the business, and how much disruption you can absorb.

Pure lift-and-shift works best with an immovable deadline, however, the architecture will be just as fragmented as before, but this time on a more expensive infrastructure. We always let our clients know where phase two begins and what it entails.

• Pilot domain: 6–10 weeks
• Lift-and-shift: 3–5 months
• Re-platform: 5–9 months
• Full re-architecture: 9–18 months

We suggest almost always starting with a pilot. This allows for testing the methodology with real data prior to incurring the full cost. Speed of decisions with respect to data ownership moves the timeline more than the technical issues do.

There will be a planned decommissioning date. The final plan will include a read-only archive and license cancellation.

This is the most likely step in a project plan to be skipped if there is a delay in implementation. Because of this, there are a lot of “successful” platform modernizations that run two active platforms for several years. Modernization at this step is only half done if the plan ends at the cutover.

Access Controls, Audit Trails and Lineage move with the data from Day 1. Governance is not a phase-two add-on.

Governance is implemented on the target platform in conjunction with the build. Because of this, all migrated workloads have built in traceability. Both platforms continue to enforce obligations associated with GDPR, CCPA, and HIPAA during the parallel run.

Yes. Modernization does actually help. Most legacy warehouses only serve aggregated, BI-ready data. Models need raw data, and a lot of it.

That gap is typically the reason why most AI initiatives on legacy platforms begin with an unplanned data project. A lakehouse with open table formats makes that data queryable. While modernization helps build AI movement and doesn’t build a working AI team by itself, it does build the foundation for AI.