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Move your data to AWS without losing a single record

Structured, repeatable database migrations from on-premise and legacy systems to AWS — with full auditability, near-zero downtime, and a cutover you can actually predict.

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The challenge

Legacy databases don't migrate themselves

Database migrations fail for predictable reasons: scope is underestimated, schema incompatibilities surface late, and the cutover window is too tight. A weekend migration turns into a two-week incident.

We've migrated SQL Server, Oracle, MySQL, and PostgreSQL workloads to Amazon RDS, Aurora, and Redshift across clients in regulated industries — betting operators, financial services, and logistics. Every migration follows the same discipline: assess first, automate the repetitive work, validate continuously, and rehearse the cutover before you run it for real.

The result is a migration that completes on schedule, produces a byte-for-byte auditable record, and leaves your team running on AWS infrastructure the day after.

5+ Database engines migrated: SQL Server, Oracle, MySQL, PostgreSQL, and more
100% Data integrity validation on every migration — byte-for-byte auditable records
<4h Typical cutover window for production migrations to Amazon RDS or Aurora

What we deliver

End-to-end migration, not just tooling

Migration assessment & scoping

Schema analysis, dependency mapping, data volume profiling, and compatibility checks before a single byte moves. You get a fixed scope, a risk register, and a realistic timeline before we start.

AWS DMS full-load & CDC

Initial full-load via AWS Database Migration Service followed by ongoing CDC replication to keep source and target in sync — so you can run parallel for as long as needed before cutover.

Schema conversion & remediation

AWS SCT-assisted schema conversion with manual remediation for stored procedures, triggers, and dialect-specific constructs. We document every conversion decision for your audit trail.

Data validation & reconciliation

Row-count checks, hash-based record comparison, and business-rule validation between source and target after every load cycle. Nothing is declared complete until the numbers match.

Cutover planning & rehearsal

Step-by-step cutover runbook with timed dry-runs in staging. We rehearse the cutover at least twice so the production window runs to a known playbook — not guesswork.

Post-migration stabilisation

30-day hypercare period post-cutover — performance monitoring, query plan review, index tuning, and a rollback path that stays open until you sign off on the new environment.

How we work

A migration in five phases, no surprises

1

Discover & assess

We profile the source environment — schema complexity, data volumes, object counts, stored procedure logic, and dependent applications. AWS SCT generates a compatibility report; we review and extend it manually. Output is a migration scope document and a signed-off effort estimate.

AWS SCT Schema analysis Dependency mapping
2

Convert & build target

Schema conversion, stored procedure remediation, and target database provisioning on AWS. RDS or Aurora instances are sized based on source profiling data — not guesswork. Parameter groups, security groups, and subnet configuration are set up to match your network topology.

Amazon RDS Amazon Aurora Stored procedure conversion
3

Full-load & validate

DMS full-load task moves all historical data. Once complete, automated validation scripts compare row counts, checksums, and sample business records. Any discrepancies are investigated and resolved before we proceed — not noted and deferred.

AWS DMS Row-count validation Hash comparison
4

CDC replication & parallel run

Change Data Capture keeps source and target in continuous sync. Applications run against both environments in parallel — discrepancies surface before go-live, not after. This phase runs until your team is confident and the cutover rehearsal passes.

AWS DMS CDC Parallel run Lag monitoring
5

Cutover & hypercare

Production cutover runs to a rehearsed runbook with defined rollback triggers. Cloudwalker engineers are on call for 30 days post-cutover — monitoring replication lag, query performance, and application behaviour. Source system stays live as a warm standby until hypercare closes.

Cutover runbook Rollback plan 30-day hypercare CloudWatch monitoring

Migration patterns

Common source-to-target combinations we run

SQL Server to Amazon RDS Most common
SQL Server on-prem AWS SCT DMS full-load RDS SQL Server

Lift-and-shift for SQL Server workloads that stay on the same engine. Minimal schema changes, fast path to managed infrastructure with RDS automated backups and Multi-AZ.

SQL Server to Amazon Aurora PostgreSQL
SQL Server SCT conversion DMS CDC Aurora PG

Engine modernisation path — eliminates SQL Server licencing costs while gaining Aurora's performance and serverless scaling. Heaviest schema conversion effort; we handle the T-SQL to PL/pgSQL translation.

Oracle to Amazon Aurora PostgreSQL
Oracle on-prem SCT + manual DMS Aurora PG

High-complexity migrations with Oracle-specific constructs (PL/SQL packages, sequences, synonyms). SCT handles ~70%; the rest is manual remediation — we scope this accurately before you commit.

Any RDBMS to Amazon Redshift
Source DB DMS to S3 Glue / COPY Redshift

Historical data migration into a new analytical warehouse. Includes distribution key design, sort key selection, and compression encoding — so the warehouse performs from day one.

0
Data loss incidents across all production migrations
<4h
Average production cutover window for transactional databases
30+
Database migrations completed across the Balkans & CEE region
40%
Average reduction in database licensing costs post-migration

AWS services we use

AWS DMS AWS SCT Amazon RDS Amazon Aurora Amazon Redshift Amazon S3 AWS Glue AWS Secrets Manager Amazon CloudWatch AWS Migration Hub Amazon VPC AWS Direct Connect

Ready to leave legacy infrastructure behind?

Tell us what you're running and where you want to be. We'll scope the migration and tell you exactly what it takes.

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