I spent most of the last fifteen years on the inside — operating the data platforms that other people's businesses quietly run on. eBay, ActiveCampaign, Disney, McKesson, Wipro, and today RegEd. When you are the person holding the pager for a system that cannot go down, you develop a very specific sense of what "done" means. It is not a deployed schema. It is a system that behaves under load on the worst day of the year.
That is the perspective I bring to database & cloud consulting. The practice is small on purpose. It exists because the same handful of problems keep showing up at companies of wildly different sizes, and because those problems are usually cheaper to fix before the migration than after it.
What the practice actually is
An independent consultancy focused on the data layer: the database engines themselves, the cloud infrastructure they run on, and the operational discipline around both. Most of the work is SQL Server, PostgreSQL, MySQL, and AWS — including Amazon Aurora, RDS, S3, and ECS — with Liquibase, Terraform, Redis, and Docker in the toolchain.
It is deliberately not a body-shop arrangement. I take engagements where the scope ends in something measurable, because "we improved things" is not a result anyone can act on. Either a query returns faster, or a bill is smaller, or an outage stops happening, or it is not worth your budget.
Assess, Plan, then Execute and verify
Every engagement runs in the same three stages, regardless of size.
1 · Assess. A deep review of your current databases, workloads and pain points — with a clear, prioritized findings report. The point of this stage is not to sell you remediation work. It is to tell you, honestly, what is actually wrong and in what order it matters.
2 · Plan. A pragmatic roadmap with defined scope, milestones and measurable targets, so everyone knows what success looks like before any change is made. This is where the project either has a definition of done or quietly becomes an open-ended retainer.
3 · Execute & verify. Careful implementation with validation at every step — changes are proven, documented and handed back to your team. The handover matters as much as the fix. A platform your engineers cannot operate after I leave was not actually fixed.
Engagements are sized to the team on the other side, whether that is a one-off rescue or an ongoing partnership.
The six things I get called for
In practice, the work clusters into six areas:
- AWS database migration — moving production workloads onto AWS and, often, off an engine that has stopped earning its keep.
- Performance optimization and tuning — query plans, indexing strategy, and the schema decisions upstream of both.
- High availability and disaster recovery — multi-AZ and cross-region resilience, automated failover, and backups that have actually been restored in a test.
- PostgreSQL, MySQL and SQL Server administration — the day-to-day running of engines that are already in production.
- Data platform engineering — the pipelines, tooling, and automation around the database, not just the database.
- Cloud architecture — designing the infrastructure the data layer sits inside, rather than retrofitting it afterwards.
What the results look like
Concrete beats abstract, so here are three from the case studies.
On a 5 TB SQL Server database that had hit a performance ceiling, the work was a complete zero-downtime migration to PostgreSQL on AWS. The result was a 70% improvement in query response times, $1.2M in annual cost savings, and an infrastructure ready for global expansion — with no maintenance window and no downtime for users.
For a streaming platform serving 50M+ concurrent users, the fix was Aurora MySQL optimization with read replicas, Redis clusters, and ProxySQL in front. That produced 99.99% uptime, sub-second authentication, timeouts eliminated at peak traffic, and streaming performance up 60%.
And for an AI platform's core database, the work cut maintenance overhead by 40% and added self-healing database capabilities — the kind of change that shows up less as a headline number and more as a quieter on-call rotation.
Why the governance work belongs in the same conversation
People are sometimes surprised that the same person runs database consulting and builds deterministic AI governance. It is not a pivot; it is the same instinct pointed at a newer problem.
When an AI agent can initiate an action, the question "who is allowed to do this, and can you prove it after the fact?" is the same question database administrators have answered for decades with authentication, authorization, and an audit log. We never asked databases to be trustworthy on their own — we put access control in front of them. That is the architecture behind EVE CoreGuard, and it is the same reason I insist that any migration I hand back is documented and verifiable rather than simply working today.
How to start
If you have a database that is slow, a migration you keep postponing, or a bill that does not match the workload it is supposed to represent, the useful first step is a conversation about what is actually broken. I am based in Atlanta and work with clients remotely nationwide.
The services page covers the offerings in detail, and the case studies have the full metrics. To talk it through, you can book a 30-minute call or start at the contact page.
— Jamaurice Holt, Founder & CEO of ArkCloud Health LLC and Senior Database Administrator