Migration is what we do best, on-prem or cloud, proven at petabyte scale. Productized audits and sequenced migration cohorts move legacy estates to a modern lakehouse without the stall. Our Migration Suite accelerators, ProbeX, KodeX, ReconX, and SynthX, make every cohort faster, auditable, and repeatable.
The honest take
Eighteen months in, you have three data estates running in parallel, double-licensing costs, and a backlog that grew faster than the migration. We don't sell "lift and shift." We engineer the path that actually ends.
What stalls migrations
What we engineer instead
The six modernization patterns
Industry-standard six R's of cloud modernization, but with workload-specific judgment. We don't pre-decide. The 4-week assessment maps each workload to its right pattern.
For workloads that work fine where they are.
Some on-prem workloads should stay on-prem. Latency, data residency, regulatory, or cost reasons. We're honest about it, not every workload is a cloud workload. We map what stays.
"Lift and shift", fastest, but limited.
Workload moves to cloud infrastructure with minimal change. Fast (8-12 weeks per cohort) but doesn't unlock cloud-native capabilities. We use this selectively, mostly for legacy apps with strict change controls.
Most common · most value · most workloads.
Workloads move to cloud-native services without re-architecting the application: SQL Server to Azure SQL, Hadoop to Databricks, Teradata to Snowflake, capturing most of the value at managed cost.
When the architecture is the blocker.
Workload is re-architected for cloud-native. Monolithic ELT becomes streaming + DLT. Batch warehouse becomes lakehouse. Higher cost, longer timeline, but unlocks AI-readiness and FinOps optimization.
When SaaS does it better.
The custom workload should be a SaaS subscription. Reporting tools, ELT platforms, monitoring, often best moved to managed offerings rather than rebuilt.
Many workloads aren't worth migrating.
Honest assessment: some workloads should just be turned off. Duplicated reports, unused data marts, legacy ELT feeding decommissioned systems. The Migration Suite audit catalogs every workload so the retire decision is informed.
Migration source & target
The technologies we migrate from, and the modern platforms we land on. On-prem warehouses, Hadoop estates, and legacy ELT move to a lakehouse-native cloud, or a governed hybrid, without losing lineage or control.
The accelerators that run the migration
Our Migration Suite carries each phase of a modernization, assess, build, cut over, and safeguard, so the journey stays fast, auditable, and low-risk from first scan to production.
Scans the whole estate and maps every workload to the right move, so the plan starts from facts, not guesswork.
Auto-rewrites legacy pipelines into cloud-native code, taking the heavy lifting out of every migration cohort.
Proves the new platform matches the old, down to the cell, so the business can sign off the cutover with confidence.
Stands up safe test data and rollback paths, so validation and go-live never put real data at risk.
How engagements start
Most cloud-modernization conversations start with one of three productized engagement shapes. Each lands in 4-16 weeks with a measurable deliverable, not a 6-month consulting discovery.
A full estate scan with workload-by-workload R-pattern mapping, a TCO baseline and target, risk and complexity scoring, and a migration roadmap with named cohorts.
Reference architecture design and a first migration cohort of 5-10 workloads, with the migration toolchain stood up, a FinOps framework and governance baseline in place, production cutover for the pilot workloads, and a cohort playbook for the next phase.
End-to-end migration of a workload cohort (100-250 ELT jobs, 5-25 TB). Pipeline inventory, automated rewrites, cell-level reconciliation, synthetic-data fallback. Production cutover + source decommissioning. 99.9%+ recon.
Let's talk.
Tell us what's in your data and AI stack, what's stalled, and what would change if it worked. We'll share what we've shipped against similar patterns in production, and what makes sense as a first step.
Our Hyperscaler & Strategic Partners
