# Data Warehouse Migration - Modernise Your Legacy Data Platform | Rittman Analytics

URL: https://rittmananalytics.com/services/data-warehouse-migration
Description: A like-for-like legacy warehouse migration means months manually translating SQL, pipelines, syncs, and BI content. We use our AI-powered Wire Framework to automate this translation, delivering your modernized platform faster, cheaper, and more reliably.

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Migrating it is holding you hostage.  
We can help.

A like-for-like legacy warehouse migration means months manually translating SQL, pipelines, syncs, and BI content. We use our AI-powered Wire Framework to automate this translation, delivering your modernized platform faster, cheaper, and more reliably.

[Assess your migration](https://mark-9o3ijqpd.scoreapp.com/)[See client results ›](https://rittmananalytics.com/case-studies)

Free. 15 questions. About 3 minutes.

## The Decision to Migrate Was Easy.  
The Execution is a Nightmare.

You know you need to move to a modern data stack like BigQuery or Snowflake, but a like-for-like migration is notoriously slow and risky. To execute a flawless cutover without paralyzing your business, you need a partner with a fundamentally smarter blueprint. We are that partner.

### Your Challenges:

#### The Infinite Timeline

Manually translating SQL dialects, rerouting ingestion pipelines, and rebuilding BI dashboards piece-by-piece takes months. Deadlines inevitably slip, budgets balloon, and stakeholder patience vanishes long before the finish line.

#### Roadmap Paralysis

A major migration forces you to pull your best engineers and analysts off value-generating work. Instead of delivering new strategic insights or features, your smartest people are trapped doing repetitive, mechanical code translation.

#### The Sunk-Cost Rollback

Untangling deeply embedded business logic and downstream syncs gets so complex that momentum stalls. Teams get overwhelmed, often resulting in paying for two redundant platforms in parallel, or worse, abandoning the migration entirely.

### Our Solution: AI-Accelerated Platform Migration

We use our AI-powered Wire Framework to automate the heavy lifting of translating your SQL, pipelines, and BI content. We deliver your fully modernized, like-for-like platform faster, cheaper, and with guaranteed reliability, ensuring a flawless cutover that keeps your internal team focused on building the business.

#### Proof in Practice:

See how Carwow accelerated its migration from Snowflake to Google BigQuery with Wire, translating around 1,700 dbt models in weeks while its data team kept the existing platform running.

[Read More →](https://rittmananalytics.com/case-studies/carwow)

![Carwow vehicle presenter](https://rittmananalytics.com/__l5e/assets-v1/34616742-f8f1-4d30-8069-4bf503c0e5f1/carwow-image-hero.webp)

Carwow

## Blueprint Your Cutover in Hours, Not Weeks

Large-scale data platform migrations are less about writing SQL than managing risk. Thousands of models, connectors, orchestration workflows, semantic objects, dashboards and reverse ETL syncs all need to be tracked, migrated, regression tested and reassembled into a platform that behaves exactly like the one it replaces.

Wire automatically deploys specialist AI-powered agents to discover and catalog all of your legacy platform components, identifying migration tasks and assessing their complexity, storing details in its migration inventory.

Wire

dispatches one agent per layer

run concurrently

each layer’s audit is independent of the others

Audit agentsone per platform layerin parallel

db-object-audit-generatedatabase objects

every database and schema

every table and view, with size and rows

stored procedures and UDFs

masking policies and tagged columns

the full RBAC graph of roles and grants

security-audit-generatethe same connection, different questionssecurity

which roles hold which grants

which columns carry PII tags

Findings determine which IAM bindings and BigQuery policy tags target-setup creates.

dbt auditdbt models

assigns every model a complexity tier

triviallowmediumhighblocked

ingestion pipeline agentingestion pipelines

details of every connection

details of every pipeline

reverse ETL agentreverse ETL

details of every sync

details of every destination

…and one agent for every further layereach independent until results are assembled

**MCP**  legacy DW server · account · role · warehouse

Legacy DW platform

the system being migrated from, typically thousands of objects

**Databases & schemas**

**Tables & views**  
size and row count

**Stored procedures & UDFs**

**Masking policies**  
and their tagged columns

**Roles & grants**  
the full RBAC graph

MCP

Ingestion pipeline platform

connectionspipelines

MCP

Reverse ETL platform

syncsdestinations

Migration inventory

a single file, a tabular record of every item, one row per object, typically thousands

pendingtranslateddeferredmigratedfailed

objecttypesize · rowsstatecomplexity

tablemigrated—

UDFpending—

dbt modeldeferredmedium

pipelinetranslated—

syncfailed—

⋮  one row per object

dispatch

back-and-forth over MCP

assemble

1

2

3

## Evidence, Not Assurances

We eliminate the guesswork from re-platforming. Our process ensures zero silent errors through rigorous, step-by-step validation:

-   Full Dependency Mapping: Complete snapshot of your existing architecture.
    
-   Deep Translation: Precise dialect translation handling complex edge cases (e.g., type coercion, division by zero).
    
-   Data Reconciliation: Automated auditing of row counts and value totals in a secure sandbox prior to PR.
    
-   Transparent Handover: Clean, manageable PRs backed by an auditable register of differences.
    

Consultancy delivery workspace · stages 1-6

Client repository & CI

The only path into production code, client-reviewed, client-merged.

Target production warehouse

Only the client's scheduler ever writes here.

Development sandbox

isolated project on the target warehouse

Migration inventory

Tabular record of every item to be migrated, drives waves, sequencing and daily status.

Item · type Wave orders\_daily  
dbt model2 pii\_row\_mask  
security rule1 crm\_contacts  
reverse ETL sync3 events\_load  
ingestion pipeline1

… and the rest of the estate

1Snapshot & audit

The client's **legacy dbt package**, written in the legacy dialect, is mirrored into the delivery workspace.

An audit maps every model's features and dependencies, and batches the estate into **waves**.

2Translation

SQL and configuration rewritten to the target dialect under the agreed **translation guide**, explicit casts on joins, safe division, JSON handling, materialisation rules.

Translated files live in the workspace, not the client repo.

3Validation & lint gate

Every translated model must parse and compile in the target dialect, and pass the engagement's lint rules.

**Nothing proceeds on translation alone.**

4Development build

Built into a scratch dataset in the sandbox. A **cost screen** runs first: an estimated-scan check holds unusually expensive builds for authorisation.

Proves the model runs against real data without touching production.

deferred inputs, reads real production tables, **read-only**

5Equivalence test

Compares the **migrated package's sandbox output** with the **source warehouse's contents**, as much data as the sandbox holds: row counts, window endpoints, sums, distinct counts.

Four exits

**Exact pass**, proceed to release

**Qualified pass**, read-timing only, proceed

**Availability gap**, data request; model waits

**Defect**, back to translation, re-run 3-5

6Release assembly

Passing models are grouped onto a branch of the client's repo. Four gates before release:

1Whole project parses with the new models in place

2Local replica of the client's CI passes

3Branch rebuild against the production graph, sandbox writes only

4Branch vs legacy re-check on every model

A defect here pulls the model from the branch.

External feeds & exports release on exact verdicts only

7Review & merge

The client reviews the pull request and their CI runs independently. Merge places the model in the production codebase.

8Production build

The client's scheduler builds the merged models into production on its normal cadence.

9Post-merge verification

Compares the **production model's output in the target warehouse** with the same model's output in **legacy production**. Internal models **ship then verify**, fixes go forward via a new pull request.

Verdicts land in the **tracking register** → daily status reporting.

Numbered data requests

Availability gaps, source history missing on the target, become numbered requests to the client. The model **waits**; resolution re-runs stages 4-5. Gaps found anywhere in the flow land on this list.

Legacy dbt packagethe client's existing model code

Legacy warehouseread-only throughout

× many models in parallel

pass

pull request

client CI · merge

contents mirrored in

**Single pooled runner**, every legacy read serialises through one connection

legacy DW contents

sandbox output

branch re-check

legacy production output

target production output

defect, fix the translation, re-run 3-5

fix forward, post-merge differences return as a new pull request

availability gap, model waits

resolved, re-run 4-5

## Machine Speed. Human Judgment.

We don't throw armies of junior consultants at a migration. We deploy an orchestrated swarm of AI agents, executing around the clock, strictly governed by a senior human director.

The Orchestrator: A central AI translates human strategy into parallel workflows, resolving dependencies and routing data models to where they need to go next.

Specialized Swarms: Dedicated agent lanes work 24/7 with hard safety limits. They specialize entirely on discrete tasks: translating SQL dialects, building sandboxes, debugging data discrepancies, or assembling release branches.

Automated Rework: Errors don't wait for a sprint review. Defective models are instantly routed back for translation rework, then gated and re-tested until row counts and outputs match the legacy source perfectly.

Absolute Transparency: Machines do the mechanical heavy lifting; humans make the commercial and strategic judgment calls. Every agent action is audited, checked against real data, and logged in a transparent register before your team reviews a single line of code.

Tier 01

Release director

One human

Client communications

Rulings and waivers

Approval gates

Judgment catches

Fleet-size and budget decisions

Rulings, waivers and approval gates

Judgment catches

Tier 02

Orchestrating session

One Fable-class session

Dispatches and monitors lanes

Owns the register (single writer)

Consolidation and backstop passes

Assembles PR evidence

Amends process docs the day a ruling lands

Dispatch

Monitoring and PR evidence

Lane 01

Translate wave models

Lane 02

Validate and lint

Lane 03

Build playground copies

Lane 04

Run equivalence battery

Lane 05

Verify post-merge

Lane 06

Drill count divergence

Lane 07

Rebuild sync audit

Lane 08

Check branch copies

Tier 03

Lane agents

6–12 concurrent

Stage-scoped work: translation, validation and evidence, one stage at a time.

Selected Work

## AI-Powered Data Migration in-Practice

0

TB

of data migrated and verified in production

0

dbt models

translated from legacy to target SQL dialect

0

days

to complete the platform migration