Cloud Data Platform Migration

AWS Or GCP Data Migration
Move Your Data Platform to AWS or Google Cloud, Without Downtime

Numlytics delivers expert AWS data migration and Google Cloud data migration for enterprises across the US, UK, Australia & UAE. Data warehouses, lakes, and pipelines migrated to Amazon Redshift, S3, Google BigQuery, and Google Cloud Storage - with parallel-run validation, zero-downtime cutover, and every downstream report verified before the old platform is decommissioned.

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Zero-downtime cutover with parallel-run validation period
Redshift, BigQuery, S3 & GCS migration specialists
Every pipeline, dashboard & report validated before old platform retires
Up to 50% lower cost vs US/UK cloud migration consultancies
Migration Outcomes
0
Unplanned downtime -
parallel-run cutover
100%
Reports & pipelines
validated before cutover
35%
Avg. infrastructure
cost reduction
50%
Lower cost vs US/UK
migration consultancies
We migrate to & from
Amazon Redshift
Amazon S3
Google BigQuery
Google Cloud Storage
AWS Glue
Dataflow / Dataproc
dbt
Apache Airflow / Composer
What We Build

Move Your Data Platform to AWS or GCP Without a Single Broken Report

Migrating your data warehouse, lake, or pipeline estate to Amazon Web Services or Google Cloud Platform is rarely about the destination platform - Redshift and BigQuery are both mature, well-documented systems. The risk is in everything connected to your current platform: the pipelines that load it, the dashboards that query it, the scheduled jobs that depend on it, and the business processes that assume it will always be there with the same data, in the same shape, at the same time.

An AWS & GCP data migration that breaks even one critical report on cutover day destroys confidence in the new platform before it has a chance to prove itself - regardless of how much better the underlying architecture is.

Our migration approach runs the legacy and target platforms in parallel for a defined validation period - every pipeline migrated, every report's output compared row-for-row against the legacy system, and every discrepancy resolved before a single user is cut over. The old platform is decommissioned only once the new one has proven itself in production, side by side, with real data and real usage.

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Why Organisations Migrate to AWS or GCP
"Our current platform's licensing costs keep increasing"
Legacy on-premise warehouses (Teradata, Netezza, Oracle Exadata) or expensive proprietary cloud platforms carry licensing and support costs that scale poorly. Redshift and BigQuery's consumption-based pricing typically reduces total cost of ownership by 30–50%.
"Our application stack is already on AWS / GCP - our data platform isn't"
When application infrastructure runs on AWS but the data warehouse sits elsewhere on-premise, Azure, or a third-party platform, every data movement crosses cloud boundaries, adding latency, egress costs, and operational complexity that consolidation eliminates.
"We need elastic compute that scales with our data volumes"
Fixed-capacity legacy warehouses force a choice between over-provisioning for peak loads or queuing during them. Redshift Serverless and BigQuery's on-demand pricing scale compute automatically paying only for what queries actually use.
"We want native access to AWS or GCP's ML and AI ecosystem"
BigQuery ML, SageMaker, and Vertex AI integrate directly with data already in BigQuery or S3 eliminating the data movement and integration overhead of running ML workloads against a separate data platform.
What We Deliver

Six Components of an AWS or GCP Data Migration

From discovery and architecture design through to parallel-run validation and cutover, the complete migration lifecycle for moving to AWS or GCP.

Discovery & Migration Assessment

A complete inventory of your current platform - schemas, pipelines, scheduled jobs, BI connections, and downstream dependencies. We map every object that needs to move and identify the high-risk dependencies that determine migration sequencing and timeline.

Schema, pipeline & dependency inventory
Redshift vs BigQuery platform recommendation
Migration sequencing & risk assessment
Target Architecture Design

The target platform architecture on Amazon Redshift or Google BigQuery - schema design adapted to the target platform's optimisation patterns (distribution keys for Redshift, partitioning and clustering for BigQuery), storage layer (S3 or GCS), and orchestration approach.

Redshift / BigQuery schema design
S3 / GCS data lake structure
Orchestration architecture (Airflow / Composer)
Data & Pipeline Migration

Historical data migration to the target platform bulk load via AWS DMS, Google Transfer Service, or custom export/load processes, and rebuild of every ETL pipeline on the new platform's native tooling (AWS Glue, Dataflow, or dbt on Redshift/BigQuery).

Historical data bulk migration
Pipeline rebuild on AWS Glue / Dataflow
dbt model migration & SQL dialect conversion
Parallel-Run Validation

Both platforms run in production simultaneously for a defined validation period. Every migrated pipeline's output is compared row-for-row against the legacy system. Every BI report is reproduced on the new platform and checked against the legacy version before any user is switched over.

Row-level data reconciliation
Report-by-report output comparison
Discrepancy resolution before cutover
BI & Application Cutover

Power BI, Tableau, and any application connections repointed to the new Redshift or BigQuery platform staged by user group so issues affect a small group first, not the whole organisation. Rollback plan maintained until the validation period confirms stability.

Staged BI connection cutover
Application connection string migration
Rollback plan maintained through validation
Decommission & Cost Optimisation

Legacy platform decommissioned only after the validation period confirms the new platform's stability and accuracy. Post-cutover, we tune Redshift cluster sizing or BigQuery slot reservations and storage lifecycle policies to capture the full cost savings of the migration.

Legacy platform decommissioning
Redshift cluster / BigQuery slot tuning
Storage lifecycle & cost optimisation
How We Deliver It

From Discovery to Decommission in 4 Phases

Zero unplanned downtime. The legacy platform stays live and serving users throughout until the new platform has proven itself.

Discovery & Architecture

Complete inventory of schemas, pipelines, and dependencies. Target architecture designed on Redshift or BigQuery schema design, storage layer, and orchestration. A migration runbook sequencing every object by priority and risk.

⏱ Weeks 1–3
Data & Pipeline Migration

Historical data bulk-loaded to the target platform. Pipelines rebuilt on AWS Glue, Dataflow, or dbt, with SQL dialect conversion handled systematically. The legacy platform continues running unaffected throughout this phase.

⏱ Weeks 3–8
Parallel-Run Validation

Both platforms run side by side. Every pipeline's output reconciled row-for-row. Every report reproduced and compared. Discrepancies investigated and resolved. This phase ends only when the new platform has matched the old one exactly, repeatedly.

⏱ Weeks 8–11
Cutover & Decommission

Staged cutover by user group, with the legacy platform kept available as rollback throughout. Once stability is confirmed across all users and reporting cycles, the legacy platform is decommissioned and cost optimisation tuning begins on the new platform.

⏱ Weeks 11–14

AWS & GCP Data Migration — Key Points

  • A successful data migration validates every report before cutover, not after.
  • Our AWS data migration and GCP data migration projects run parallel environments throughout.
  • A data migration timeline of 10–14 weeks is typical for a mid-sized estate.
  • Every data migration includes SQL dialect conversion for Redshift or BigQuery.
  • Post-data migration cost optimisation typically saves 30–50% on infrastructure.
  • A data migration to AWS or GCP unlocks native ML and AI ecosystem access.
Why Numlytics

Why Choose Numlytics for AWS Or GCP Data Migration

We've migrated data platforms to AWS and Google Cloud for organisations across the US, UK, and Australia - engineers who specialise in zero-disruption cutover.

Parallel-Run, Not Big-Bang
We never recommend a big-bang cutover switch everything over a weekend and hope. Both platforms run in production simultaneously until the new platform has proven itself with real data and real usage. The legacy platform is your safety net until it's no longer needed.
Every Report Reconciled Before Cutover
We don't consider a migration complete until every BI report's output on the new platform matches the legacy system, row for row. This reconciliation process catches the subtle issues, timezone differences, rounding, SQL dialect quirks before users ever see them.
Redshift & BigQuery Specialists
Deep platform-specific expertise - Redshift distribution keys, sort keys, and WLM configuration; BigQuery partitioning, clustering, and slot reservation strategy. The performance and cost difference between a naive migration and an optimised one is often 2-3x.
SQL Dialect Conversion Expertise
Redshift and BigQuery each have SQL dialect differences from Teradata, Oracle, SQL Server, and Snowflake - date functions, window function syntax, data type handling. We've converted dbt models and stored procedures across these dialects systematically, not query by query under time pressure.
Cost Optimisation Included
A migration that lifts-and-shifts without optimisation often costs more than the legacy platform. We tune Redshift cluster sizing or BigQuery slot reservations, and configure storage lifecycle policies, as part of the engagement — not a separate project afterwards.
Up to 50% Lower Cost
Senior cloud migration engineers from India, same AWS and GCP migration expertise as US or UK consultancies at up to 50% lower cost. Full timezone overlap, daily standups, and Slack access throughout the engagement.
★★★★★

"We were running a Teradata warehouse that had been in place for over a decade license renewal was approaching and the quote had increased significantly again. Our application stack was already entirely on AWS, so Redshift was the obvious target, but we'd heard horror stories about migrations breaking finance reports for weeks. Numlytics ran a 12-week migration with both platforms live throughout. Every one of our 140 scheduled reports was reconciled against Teradata before cutover, they found and fixed three SQL dialect issues we would never have caught until a report was wrong in front of the finance team. We cut over department by department over three weeks with zero incidents, decommissioned Teradata, and our infrastructure costs dropped by 42% in the first quarter on Redshift."

DH
David H.
Director of Data Engineering · Logistics Company, United States

AWS or GCP migration sits alongside platform-specific migration paths and downstream BI work.

Microsoft Fabric Migration
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A cloud-agnostic alternative that runs on AWS, Azure, or GCP infrastructure
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Lakehouse architecture that runs natively on AWS or GCP
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Power BI Consulting
Reconnect dashboards to Redshift or BigQuery post-migration
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FAQ

AWS & GCP Migration FAQs

Common questions before starting an AWS or GCP data migration with Numlytics.

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A typical AWS data migration or GCP data migration runs 10–14 weeks for a mid-sized data estate discovery, architecture design, data and pipeline migration, parallel-run validation, and cutover. Larger estates with hundreds of pipelines may run 4–6 months. The parallel-run validation phase is deliberately not compressed, it's what prevents broken reports on cutover day.
The choice depends on your existing cloud commitment and workload patterns. If your application infrastructure already runs on AWS, Redshift offers tighter integration. If you have variable, bursty analytical workloads, BigQuery's serverless on-demand pricing can be more cost-efficient. We assess your workload patterns during discovery and provide cost projections for both options before recommending a direction.
No planned downtime. Our AWS data migration approach runs the legacy and target platforms in parallel throughout, the legacy platform continues serving users unaffected while the new one is built and validated. Cutover happens in stages by user group, with a rollback plan maintained until validation confirms the new platform matches the old one exactly.
Existing pipelines are rebuilt on the target platform's native tooling - AWS Glue or Dataflow for orchestration - and dbt models are migrated with SQL dialect conversion, since Redshift and BigQuery each have different SQL syntax than most legacy platforms. Each migrated pipeline's output is reconciled row-for-row against the legacy version during validation. See our ETL pipeline development service for ongoing pipeline work.
Organisations migrating from legacy on-premise warehouses or expensive proprietary platforms to Redshift or BigQuery typically see 30–50% reductions in total infrastructure cost - driven by consumption-based pricing, elimination of licensing fees, and elastic compute. The exact figure depends on your current platform's cost structure, which we model during discovery before the migration begins.
Ready to Start?

Move to AWS or GCP - Without a Single Broken Report

Get a complete AWS data migration or GCP data migration discovery, architecture, pipeline migration, parallel-run validation, and staged cutover. Zero unplanned downtime. US, UK, Australia & UAE.

Get Free Consultation ← All Cloud Data Platform Migration Services
Other Cloud Data Platform Migration Services
Microsoft Fabric Migration Snowflake Implementation Databricks Consulting Azure Synapse & DataOps Data Engineering

Data Migration — Common Questions

How do you scope an AWS or GCP data migration?

Every data migration begins with a discovery phase inventorying schemas, pipelines, and dependencies. This scoping determines the data migration timeline, sequencing, and risk areas before any commitment is made to a target platform or migration approach.

What makes a data migration high-risk?

A data migration is high-risk when downstream dependencies - BI reports, scheduled jobs, application connections are poorly documented. Our data migration discovery phase maps every dependency so the migration sequence avoids breaking critical reports midway through.

How do you validate a data migration before cutover?

Every data migration we deliver includes a parallel-run validation phase both platforms running simultaneously while every pipeline and report is reconciled row-for-row. A data migration is not considered complete until this validation confirms an exact match.

Can a data migration be done in phases?

Yes, a phased data migration moves lower-risk datasets and pipelines first, building confidence and process maturity before migrating mission-critical reporting. Most of our AWS and GCP data migration engagements use this phased approach rather than a single cutover event.

What documentation do we receive after the data migration?

Every data migration concludes with full documentation, the new architecture, pipeline runbooks, and a data migration completion report showing the validation results for every reconciled object. Your team receives everything needed to operate the new platform independently.