Data migrations planned to succeed, not just to happen.
Cyber Samurai delivers data migrations to Microsoft Azure, Azure SQL and Microsoft Fabric - with the discovery, testing and cutover planning needed to move critical data with confidence.
Problems we solve
What can be achieved
Move critical data to modern platforms with confidence and without surprises.
What's available
The technologies involved
Data migration draws on many technologies. These are the ones we use most.
- Azure SQL and SQL Server - database migrations and upgrades
- Microsoft Fabric - lakehouse and warehouse migrations
- ERP and finance systems - such as Sage 200 and Dynamics
- Data profiling and quality - finding issues before they move
- ETL and pipelines - Azure Data Factory and Fabric pipelines
- Reconciliation - automated checks that totals and records match
- Data observability - monitoring before, during and after cutover
How we work
Migration works best in expert hands
Moving data looks simple until the totals do not match. Experienced migration engineers know where legacy data hides its problems, how to reconcile millions of records and when a cutover is genuinely safe. AI can generate mapping and transformation code quickly, but only an expert can tell whether it handles the edge cases correctly. That is why every migration we deliver is planned, tested and signed off by specialists.
Cut your costs
How to reduce the cost of a data migration project
Migration budgets rarely overrun because of the technology chosen. They overrun because the programme discovers late that the source data is unstable: feeds arrive late, volumes swing and schemas change mid-build. Every one of those surprises becomes analyst days, extra test cycles and change requests. Our Data Observability as a Service removes that fuel before the migration starts.
See the problems before the build
We watch the source data for freshness, volumes, schema changes and missing values before a single mapping is written. Issues surface in week one, as baseline findings, not at dress rehearsal priced at rush rates.
Cleanse and prepare the data first
Data is cleaned and prepared before it moves, with problems fixed at source where possible. That means fewer false defects blamed on the migration, stable mappings and reconcile packs that pass.
Fewer migration cycles
Test cycles fail far less often when the source behaves. That removes full re-runs, repeated reloads and the dual-run extension that noisy acceptance evidence causes.
Fewer analysts, fewer days
Every cycle removed takes specialist days with it. You either need fewer data migration analysts or the same team spends far less time transforming and re-testing the data.
Delivered as Data Observability as a Service, so you can buy the baseline without building a platform team first.
Explore Data Observability as a ServiceCollaboration
You bring the software. We bring the data.
Software vendors often use their own partners to set up and configure the application, but those partners rarely get their hands dirty with the data - that work gets left to the customer, who may have neither the team nor the time. We slot into the middle of that arrangement and own the data migration workstream, so the partner can stay on configuration and the customer is not left holding a go-live they cannot complete. We have done this on several occasions.
The software vendor
You keep the customer relationship, the licence and the delivery timeline. Data migration is covered by a team that does it every week, rather than becoming a reason the rollout slips.
The implementation partner
Your partner sets up and configures the application, which is what they are good at. Data profiling, mapping, transformation and reconciliation sit outside their scope and with us.
The customer
You get a data team that understands source systems, finance records and sign-off evidence, without hiring one or asking your vendor to do something outside its offering.
One plan, one owner for the data, and clear handovers between everyone involved.
Talk to us about your projectOutcomes
What good migration looks like
Our experience
Planning
In data migration, failing to plan really is planning to fail.
Strategy
Decide the migration approach - department by department, static then transactional, big-bang or phased.
Scope
How many records, which entities, transactional history or just balances? Scope drives every downstream decision.
Roles and responsibilities
Who's the solution architect? Who signs off? What does the SI, our team and the client own throughout?
Our experience
Implementation
Preparation and iteration are what separate smooth cutovers from painful ones.
Mapping
Map source to destination - tables, columns, data types and lengths. Precision here saves weeks later.
Cleansing
Cleanse at source where you can, translate in flight where you can't.
Migration cycles
Most projects need around five full cycles - iterate, don't innovate. Each cycle proves reconciliation works.
Insights
Related reading
Silent Data Failures: The Incidents That Never Trigger an Alert
A silent data pipeline failure produces no error, no crash, and no alert, just wrong numbers. What causes them, and why "it would have paged someone" is a myth.
Read article
Observability → Migration → Trust: The Sequence That De-Risks Major Change
Observability before migration, migration with reconciliation, then earned trust, the sequence that de-risks major data change for mid-market companies.
Read article
What a Data Migration Readiness Assessment Should Cover
What a data migration readiness assessment should cover: scope, quality, mapping complexity, cutover options, risks and costed paths before you sign the SOW.
Read articleQuestions
Frequently asked
- Yes - SQL Server to Azure SQL, Managed Instance and IaaS are common migration paths.
Start the conversation
Plan a migration that lands cleanly.
What you'll get
- 30–45 min consultation
- Clear priority map
- Recommended first engagement
- No obligation to proceed
Not ready to talk? Download the SQL Server health check guide or score your data health .