Database
Queries that used to be fast start timing out. Connections run out. The answer becomes a bigger instance and a bigger bill.
Your Data Infrastructure Partner.
Data Infrastructure Engineering for teams whose databases, pipelines, and data platforms keep slowing down, costing more, or breaking. We trace the root cause, fix it, and stay on as your data infrastructure partner, not a one-time vendor call.
- Slow queries, actually fixedDatabase Engineering18msp99 latency
- Pipelines that ship on timeData Engineering62runs/day
- Catches silent data failuresData Reliability14issues caught
- Events acted on in secondsReal-Time Data Engineering42msevent lag
- Cloud spend under controlData Cost Optimization68%compute usage
- Numbers your team can act onData Analytics3.2kqueries/day
Monitoring Activity480 checks/hr
[OK] postgres: autovacuum completed on orders
[INF] pipeline: nightly dbt run finished, 0 failures
[OK] backup: full + WAL chain verified
Analytics and AI are only as good as the layer underneath.
A database doesn't become slow overnight. Small architectural compromises accumulate for months until tuning stops helping and more cloud spend stops helping. The problem isn't performance. It's the decisions stacked underneath it.
Platform
Pipelines run green while data arrives late or incomplete. Every new dataset waits in a queue behind one overloaded team.
Decisions
Dashboards disagree, forecasts miss, and AI projects stall because nobody fully trusts the numbers underneath.
We start at the bottom layer. That's usually where the problem started.
Eight places infrastructure decisions compound
Each service starts from a problem your team can already see, not from a tool we want to sell.
Your data infrastructure decides four things about your business
We measure every fix against them months after it ships, not on launch day.
Growth
More customers and more data without the system becoming the reason plans slow down.
Cost efficiency
Infrastructure spend that grows slower than revenue, with a known reason behind every major line.
Reliability
Systems that stay up and numbers that stay correct, with recovery planned before anything fails.
AI readiness
Data clean, connected and governed enough that AI projects reach production, not just a pilot.
What changed once the root cause was found
Each engagement started with a diagnosis, not a tool recommendation.
Diagnosis first. Then a plan. Then a partner who stays.
Every engagement begins with the same five steps, so you know what you'll have in hand before you commit to the next one.
- 01
Discovery Call
In 30 minutes, you describe what's breaking: slow queries, rising spend, pipelines that fail quietly, or numbers nobody trusts. We ask how the system got here.
What you receive: A straight answer on whether we're the right team, and what the diagnostic would cover.
- 02
Diagnose
We go through your actual setup: query plans, configuration, pipelines, and the access patterns behind them. Quoted before you commit.
What you receive: A traced root cause, not a list of generic best practices.
- 03
Prioritize
A written report with the root cause, risk scores, and fixes ranked by impact and effort.
What you receive: A roadmap ranked by impact and effort, agreed with you before any work starts.
- 04
Fix
We ship the fixes, document the reasoning behind every change, and walk your team through it.
What you receive: Working infrastructure and an engineering team that understands it.
- 05
Stay
We stay on as your data infrastructure partner: watching the systems we changed, tuning as your data grows, and working alongside your team on what comes next.
What you receive: A partner who already knows your infrastructure and is there when something shifts.
How your data infrastructure partner actually works
Engagements run in the open, inside the tools your engineers already use.
One accountable team, start to finish
The team that finds the cause fixes it and stays on to support it. Nothing gets lost between a delivery team and a support desk, and you always know who is accountable.
A shared channel with your team
Questions, findings, and decisions happen in a shared Slack or Teams channel for the length of the engagement, not a ticket queue you have to follow up on.
Written findings, not slide decks
Every recommendation comes with the evidence behind it: query plans, metrics, and the reasoning for the tradeoff we chose, not just the conclusion.
An incident war room when it matters
When there's an active incident, we join a live call with your engineers and work it in real time, not a status update the next morning.
Knowledge you keep
Runbooks, architecture notes, and a walkthrough, planned in from day one instead of rushed at the end, so your team understands every change while we stay on to support it.
The name comes from Dharma, doing what is right. For us that means telling you what the evidence shows, explaining every change so your team can own it, and staying after the fix so the system doesn't drift back.
Diagnose the cause. Engineer the fix. Keep the system reliable.
Four ways to fix a data infrastructure problem
Each option works for someone. Here's how they compare when the problem crosses the database, the pipelines, and the application.
DharmOps
Where it looksThe database, the pipeline, and the application code, together
Ships the fixYes, with the reasoning documented
Who's accountableOne team from diagnosis to ongoing support
What you keepYour code, accounts, and documentation, owned by you from day one
CommitmentScoped to the problem, not a headcount or a contract
Hiring a DBA
Where it looksThe database, not the pipeline or the application
Ships the fixYes, after weeks to hire and months to reach full speed
Who's accountableOne employee, covering everything alone
What you keepKnowledge that leaves when they do, most of it never written down
CommitmentA full-time salary, quiet quarter or not
Large consultancy
Where it looksSplit across separate practice teams
Ships the fixSometimes, as a second statement of work
Who's accountableAn engagement manager, with delivery and support split across teams
What you keepA slide deck and a follow-on proposal
CommitmentA multi-month statement of work, signed up front
Monitoring tool
Where it looksWhatever it's configured to watch
Ships the fixNo, it tells you something's slow, not why
Who's accountableNobody. It raises an alert and stops there
What you keepDashboards, alerts, and more noise over time
CommitmentAn annual license, renewing whether it caught anything or not
Data Infrastructure Engineering for 13 Industries
View All Industries
Companies we've worked with
"Our PostgreSQL queries were slowing down as our dataset grew. We'd already tried the obvious fixes ourselves. DharmOps got into our query plans, identified the root cause quickly, and tuned what mattered. The kind of deep database knowledge you can't easily find elsewhere."
Jasara Technology Inc.
PostgreSQL Query Optimization
How we think through the problems we see most
Practical write-ups your engineers can use today, whether or not we ever work together.
Questions engineering leaders ask first
Tell us what's breaking.
One call to walk through the symptoms. You'll leave knowing where to look first.
Book a Discovery CallOr email contact@dharmops.com