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    MigrationStudio for AI-Driven Data Migration

    Agent-driven data platform migration tool (AWS to Databricks to Snowflake to Fabric) that provides roughly 35% or better reduction in migration time and cost.

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    MigrationStudio

    AI-powered platform migration, features to match your requirements

    Cloud Data Migration tool for AI-era data estates
    Command centre
    See the health of your entire migration in one glance.

    With the migration command centre, see:

    • A live status panel breaking down jobs by source, target, and promotion stage
    • 10+ live metrics: jobs scanned, orphans flagged, tables mapped, bundles promoted
    • A built-in rules engine surfacing the jobs that need a human decision first
    Migration Studio
    Straight answers
    Not every source-to-target pair is proven yet. We tell you which ones are.

    A path-by-path status view, not a blanket "any-to-any" claim:

    • Named paths validated end to end are marked proven, with the test bed behind the claim
    • Paths in active development are marked as such, with what's still being built
    • Everything else is marked "architecture supports it," so nobody scopes a SOW on a guess
    Migration Studio
    Before you move anything
    Which jobs are actually worth migrating?

    Job and table assessment, before conversion starts:

    • Orphaned job detection: no run in 90+ days gets flagged, not silently carried over
    • Table-level activity read, so dormant tables don't inflate the scope of the move
    • A risk flag per job, so the team prioritises review time on what actually needs it
    Migration Studio
    What the engine is built to do to your migration timeline.
    • 35%
      Target reduction in migration time and cost
    • 1000's
      Of Tables migrated from one platform to other
    • 70%
      Of a typical migration that's repeatable, now productised
    Most migration tools translate syntax. This one translates intent.

    Transliteration maps dialect to dialect and produces code that runs but reads like a machine wrote it. Semantic re-platforming understands the job first.

    Most migration tools translate syntax Most migration tools translate syntax
    *Illustrative example, not a client's literal source code.

    Same job, two outcomes. One is a working copy of the inefficiency you started with. The other is an engineer's read of what the job was actually for, expressed the way someone building it fresh in the target platform would write it.

    The Studio family, for before and after the move.
    Two minutes to understand how MigrationStudio works
    Your next migration doesn't have to be a black box.

    See MigrationStudio scan your estate. One demo, no obligations.

    Book a free 30 min demo
    MigrationStudio
    Most migration tools get code onto the new platform. That's not the same as getting it right.
    CAPABILITY

    Typical migration tooling

    Transliteration accelerators, hand scripts

    MigrationStudio

    Semantic re-platforming engine

    How it converts code
    Maps syntax dialect to dialect. Runs on the new platform, reads like the old one.
    ✓Classifies what each job is for first, through a canonical intermediate representation, then writes idiomatic target code.
    Inefficiency in the source
    Faithfully reproduced. A full-overwrite job stays a full-overwrite job.
    ✓Optimised while converting. A full-overwrite becomes incremental where the logic supports it.
    Orphaned jobs
    Migrated along with everything else. Nobody re-checks what's actually running.
    ✓Flagged at the scan stage, before conversion, so dead weight doesn't have to cross platforms.
    Deployment
    Often a scripted, one-shot cutover. Hard to review, harder to roll back cleanly.
    ✓Git-based promotion. Every bundle opens as a pull request an engineer reviews before it merges.
    Source-to-target coverage
    Often marketed as "any-to-any" without saying which pairs are actually proven.
    ✓Named paths marked proven or in development. Everything else labeled "architecture supports it," not sold as done.
    Where the engineering hours go
    Billed for the full estate, including the repeatable, mechanical share of the work.
    ✓Productises the repeatable share of a migration, so services time concentrates on the judgment calls and edge cases.
    FAQs on MigrationStudio & Cloud Data Migration

    Transliteration maps syntax from one dialect to another. It produces code that runs on the new platform but performs and reads like the old one. Semantic re-platforming works out what a job is for, then writes idiomatic code for the target platform, optimising the logic instead of faithfully reproducing whatever inefficiency had built up over the years.

    The scan stage flags orphaned jobs, meaning anything that hasn't actually run recently, before conversion starts. You decide what gets migrated and what gets retired. Dead weight doesn't have to cross platforms just because it crossed platforms last time.

    Job-intent classification instead of syntax mapping, automated orphan detection before anything gets converted, and Git-based promotion so every change is reviewable rather than a black-box deploy.

    No. A successful migration also involves redesigning data models, pipelines, security, governance, orchestration, and downstream workloads for the target platform. A simple lift-and-shift can carry unnecessary complexity and technical debt into the new environment.

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