Service · Remote-first senior specialists
Migrations fail on the things nobody mapped. MV.tech moves legacy databases such as SQL Server and Oracle, along with the manual reporting that grew around them, into Snowflake, BigQuery or Amazon Redshift — starting with metadata profiling and dependency analysis, reconciling at every stage, and cutting over on evidence rather than on a date.
The starting point
What we deliver
Catalogue the current estate from the information schema, DDL, query logs and job schedules: tables, volumes, refresh frequencies, objects nothing reads and the handful that carry the business. Profile the data itself for types, null rates, key uniqueness and history depth, so the target design reflects what is there rather than what the documentation claims.
Trace what reads each object — views, stored procedures, scheduled jobs, extracts, semantic models and dashboards — and flag hardcoded rules, cross-database joins and platform-specific SQL that will not survive the move. This is the analysis that decides whether a migration is a planned project or a sequence of surprises.
A written mapping per object: source, target table and layer, data types, transformation rules, keys, grain, historical scope and the check that proves it landed correctly. It is the document both sides sign off and the basis for every test that follows.
Design Snowflake, BigQuery or Redshift with separated layers: raw as it arrived, staging conformed, curated facts and dimensions, and reporting shapes that BI tools read. Partitioning, clustering and incremental loads are chosen against real query patterns, which is where the 60–75% reductions in query scan size on past engagements came from.
Automated comparison between old and new at each pipeline stage: record counts, date coverage, nulls, duplicates, control totals and metric-level comparison of the figures people actually report. Differences are explained rather than rounded away — several times they have revealed a defect in the legacy logic. This work has cut manual reconciliation effort by more than 70% on past migrations.
Repoint Power BI, Tableau, Looker Studio or Analysis Services models at the new layers, or rebuild them where the old model was itself the problem. Report-level differences are reconciled visual by visual before a dashboard is called migrated.
Plan the sequence: what runs in parallel and for how long, the freeze window, who signs off each domain, what is decommissioned when, and how to roll back. Handover includes the mappings, ER diagrams, validation results, schedules, ownership and operating runbooks.
A useful first project
Pick one reporting domain — finance, sales, operations — and take it the whole way: discovery of its sources, dependency analysis, source-to-target mapping, warehouse layers, reconciliation against the existing numbers and a repointed dashboard. It proves the target design, the validation approach and the cutover process on something small enough to correct cheaply, and it produces patterns the following domains reuse. It also gives an estimate for the rest of the estate based on your data rather than on a template.
See our delivery processWorking together
Profile the source estate and its dependencies, agree the target architecture, and write the source-to-target mapping and migration sequence.
Load and model domain by domain, comparing each stage with the legacy system on counts, coverage, totals and reported metrics.
Run in parallel, repoint reporting on evidence, take written sign-off and retire legacy objects in a documented order.
Teams our engineers have worked with
Before we begin
Not answered here? Ask us directly or read the full FAQ.
It depends far more on dependencies than on data volume. Discovery and dependency analysis on a mid-sized estate typically takes weeks; the migration is then scoped domain by domain, each reconciled before the next begins. We would rather give an estimate after discovery than before it, and discovery can be bought as a standalone piece of work.
By comparing it with the old one, automatically and repeatedly. Record counts and control totals per load, date coverage and gap checks, null and duplicate profiles, key uniqueness, and metric-level comparison of reported figures. Every difference is investigated and then either fixed or documented as a deliberate change, including the cases where the legacy number turns out to be the wrong one.
That is what the dependency analysis is for. Before anything moves we trace which reports, extracts and semantic models read which objects, and flag the business logic hiding inside them. Dashboards are then repointed and reconciled visual by visual, usually while both platforms run in parallel, so the old estate is switched off only after the new numbers have been signed off.
Usually a mix, decided per domain. Lifting and shifting is faster and keeps reconciliation simple, but it carries forward the modelling problems and often the cost profile. Redesigning is right where the grain is wrong, where legacy logic is unmaintainable, or where query patterns will change on the new platform. Discovery is what tells you which objects belong in which category.
Yes, and it is a common one. The shape of the work is the same: find the sources behind the spreadsheets, document the rules that live in formulas and in people’s heads, model them in the warehouse and reconcile the output against the last few months of manual figures. The difference is that more of the discovery happens in conversation than in the information schema.
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ExploreYour next step
Bring your goal, current tools and preferred working hours. We use the first 30-minute conversation to clarify fit and an initial scope, and you leave with a written next step.
Remote from Ahmedabad, India · Overlap with any timezone · No obligation
