We migrate on-premises, VMware and legacy database workloads to Amazon Web Services in tested waves. Then we modernize what's worth modernizing, onto containers, serverless and managed databases, so the move pays for itself.
AWS Advanced Tier Services PartnerAWS-certified migration engineers
Most teams know they should be on AWS. What stops them is risk, not intent.
Data-centre contracts, VMware licensing and commercial database costs keep rising, and each renewal locks you in for another term.
Revenue-critical systems can't go dark for a weekend, and nobody has a rehearsed plan for rolling back a cutover.
Workloads moved as-is keep the same bottlenecks and costs, and miss out on what managed AWS services offer.
We choose rehost, replatform or refactor for each workload based on cost, risk and business value. Not everything needs to be rewritten.
Discovery of servers, dependencies and databases, a 7 Rs decision for each workload, a TCO comparison and a wave plan, backed by an AWS Well-Architected review.
A multi-account AWS environment with guardrails, identity, networking and logging, defined in code before the first workload arrives.
Rehost and replatform with block-level replication and rehearsed cutovers, moving VMware and physical servers to Amazon EC2 with minimal downtime.
Oracle and SQL Server to Amazon Aurora PostgreSQL, and self-managed engines to Amazon RDS, with continuous replication and validated zero-downtime cutovers.
Monoliths decomposed onto containers and event-driven serverless, where the change reduces cost or speeds up delivery.
Overloaded warehouses and databases re-architected into lakehouses and streaming pipelines, paying only for the compute each job uses.
Continuous block-level replication lets us test a server on AWS before cutover, keeping the cutover window short and reversible.
Full load plus change data capture keeps source and target in sync until we switch, so a database migration needs no downtime window.
Converts Oracle and SQL Server schemas and code to PostgreSQL and flags what needs manual rework, early in the plan.
Managed engines with Multi-AZ failover and automated backups that remove commercial licence costs and patching work.
The right landing spot for rehosted workloads, right-sized from real utilization data rather than on-prem allocations.
Managed container platforms for modernized services, with no control plane or hosts for your team to run.
Event-driven compute for integrations and batch jobs that don't justify an always-on server.
A governed multi-account landing zone, so security and billing boundaries exist before the first workload moves.
We migrate in small waves grouped by dependency. Each wave is replicated, tested on AWS and rehearsed before the real cutover, with a rollback path ready at every step.
Re-architected ~900 Amazon Redshift transformation queries to Spark on Amazon EMR with Apache Iceberg on Amazon S3, and converted 350+ queries with a zero-downtime cutover.
Moved an IOPS-bound database workload to a serverless pipeline on Amazon Data Firehose, AWS Glue and Amazon Athena, ingesting 3.3 billion records a month.
We'll map your workloads, pick a strategy for each and give you a wave plan with a TCO on AWS, before you commit to anything.
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