Dependency map
Sources, transformations, models, reports, schedules, and consumers identified before movement.
Architecture & platforms
Modernize the data platform without losing the business record
Move legacy storage, transformations, and reporting logic to a maintainable cloud architecture with controlled reconciliation.
The problem
Legacy environments hold years of business meaning alongside performance constraints, fragile jobs, and undocumented dependencies. A platform move that ignores that record simply relocates the risk.
Common signals
When this work becomes useful
- A warehouse platform is reaching end of life
- Refresh and maintenance windows no longer fit
- The organization is consolidating systems
- Cloud modernization has stalled between prototype and migration
Concrete output
What the engagement can produce
Target data layers
A right-sized warehouse or lakehouse design with clear history and serving patterns.
Phased migration
Workloads moved in useful groups with reconciliation and fallback at every material boundary.
Retirement path
Old jobs, reports, and infrastructure removed deliberately after replacement is proven.
Scope variants
Shape the engagement around the need
Modernization blueprint
Inventory, target architecture, workload groups, sequencing, effort, and risk controls.
Pilot migration
One representative data product moved end to end to validate the target approach.
Phased program
Delivery across ingestion, transformation, models, reporting, reconciliation, and retirement.
Delivery approach
From current state to clear ownership
Understand
Map the business need, current system, owners, constraints, and material failure modes.
Define
Agree on the first useful outcome, delivery boundary, evidence, and responsibilities.
Build
Implement in reviewable increments with validation close to the points where meaning changes.
Establish
Document, release, monitor, and leave the system with clear ownership and next decisions.
Technical context
Platforms and disciplines
- Snowflake
- Microsoft Fabric
- Azure
- SQL
- ELT
- Power BI
- Reconciliation
Engagement fit
Clear boundaries make better projects.
Often a good fit
- Organizations with legacy warehouses
- Teams consolidating after growth or acquisition
- Businesses moving recurring reporting to the cloud
- Leaders needing a migration plan grounded in dependencies
Probably not the right fit
- A big-bang migration without fallback
- A lift-and-shift with no operating improvement
- Retiring reports without accountable business review
Anonymized proof
Representative modernization work has preserved governed measures and recurring review while improving the underlying path from operational sources to reporting.Explore representative work
Questions
What teams often ask first
Can the old and new systems run in parallel?
Yes. Controlled parallel operation and reconciliation are often appropriate for consequential reporting.
Do lakehouses replace warehouses?
Not categorically. The right serving and storage patterns depend on data shape, consumers, skills, governance, and workload economics.
Compare scope, prerequisites, investment guidance, and ownership before deciding.
The packaged engagements and delivery models make the commercial boundary visible without forcing every project into the same shape.