Slow websites are usually slow databases. We design data structures that stay fast as records grow, fix the ones that have already slowed down, move data between systems safely, and make sure your backups actually restore when you need them.
New Delhi, India | 50+ projects delivered | 10+ industries served
Database problems are almost always the delayed result of a decision that seemed fine at the time. The tables were designed for one feature and never revisited. Relationships were handled in the application code instead of the database, so incomplete and duplicate records collected quietly for months.
Backups are where it gets serious. They are configured, they report success, and nobody has ever restored one. The retention period is shorter than the time it would take to notice a problem. How much data you can afford to lose was never discussed, and that question ends up being answered for the first time during an actual failure.
Designing it right the first time, or fixing what is already in production without taking it offline.
Planning tables, relationships, data types, constraints and indexes for a new application, with room to grow built in from the start.
Explore DB architectureFinding why the system slowed down through query analysis and index review, then fixing it with results measured before and after.
Explore DB optimisationMoving data between systems, versions or hosting providers with field mapping, verification and a tested rollback if anything goes wrong.
Explore data migrationSetting up and running managed databases in the cloud with replication, automated backups, monitoring and sensible capacity planning.
Explore cloud DBFinding duplicates, orphaned records and inconsistent values, then correcting them with a report rather than silent changes.
Explore data syncBackup schedules, retention rules, offsite copies and a documented recovery procedure that we test by restoring it properly.
Explore ongoing supportNothing is applied to a live database without a tested way to undo it.
| Included | What it means for you |
|---|---|
| Current state review | Your existing structure documented, including what is undocumented today |
| Performance baseline | Current speeds measured so improvements can be proven, not claimed |
| Slow query report | The specific queries causing problems, ranked by impact |
| Index plan | Which indexes to add and which existing ones are slowing writes down |
| Structure design | Tables, relationships and constraints documented with the reasoning |
| Migration scripts | Version-controlled changes with a tested rollback for every one |
| Backup and recovery | Schedule, retention, offsite copies and a restore procedure we actually test |
| Security review | User permissions, encrypted connections and credential handling checked |
| Monitoring | Alerts for slow queries, storage limits, connection spikes and failed backups |
| Data dictionary | Plain-language explanation of every table and field for your team |
| Handover | Documentation, scripts and a runbook covering what to do when something breaks |
Findings come with the effort each fix requires, so you can prioritise against budget. If your own team is best placed to apply them, the report is written for that and no further work is needed from us.
Six stages, with production changes only ever made against a tested rollback.
We document the current structure, measure real speeds, check the backups by restoring one and review access rules.
You get: findings report with prioritiesThe target structure or fix list is defined and ordered by risk, with a rollback written for each change.
You get: change plan and rollback planEvery change is run on a copy of your real data first, so we know exactly how long it takes and what it locks.
You get: rehearsal results and timing estimateChanges go to production in a safe order, during a quiet window if needed, with speed measured after each one.
You get: change log and performance comparisonRecord counts are checked against the source, constraints confirmed and results compared to the original baseline.
You get: verification and reconciliation reportAlerts are set up, a full restore is tested, and documentation and access are handed to your team.
You get: monitoring, restore test result and runbookFor business data with orders, customers, records and relationships between them, a relational database is almost always the right answer. We recommend anything else only when there is a specific reason for it, and we explain that reason in writing rather than following whatever is currently fashionable.
Moving data between connected systems is covered under API development and integration.
Upgrading the server is the common answer. It is also the most expensive one long term.
| Consideration | Designed structure | Framework defaults | Upgrading the server |
|---|---|---|---|
| Data accuracy rules | Enforced by the database | Left to application code | Not addressed |
| Indexing | Based on measured queries | Defaults plus reactive additions | Not addressed |
| As records grow | Planned for | Slows without warning | Delayed, not solved |
| Reports affecting live users | Separated | Runs on the same database | Still competes for resources |
| Recovery ability | Tested by restoring | Not addressed | Backups exist, restore untested |
| Cost over time | Falls as efficiency improves | Rises with each upgrade | Rises every time |
| Best suited to | Anything holding real business data | Prototypes and small projects | A short-term emergency measure |
A bigger server buys you time and we sometimes recommend it as an immediate step. It does not fix a structural problem, and you pay for it again every single month.
How long records must be kept and who can see them usually shapes the design more than the data volume does.
Products, variants, orders and seasonal traffic peaks
Shipment histories, status events and high write volume
Student records, results and long retention periods
Patient records, access logs and controlled visibility
Inventory, specifications and dealer pricing structures
Transaction accuracy, audit trails and reconciliation
Listings, enquiries and search-heavy filtering
Client records, billing data and document relationships
An application can be rewritten in a few months. The data inside it cannot be recovered from a bad decision made two years ago. That is why we treat the database as the part of the system that deserves the most care and the least guesswork.
What businesses ask when the system has slowed down or data has gone wrong.
It usually comes down to one of five things: queries running without a supporting index, too many unnecessary indexes slowing down every save, reports running on the same database as live users, records locking each other under load, or a table structure that forces heavy joins for ordinary operations. We identify which one it is by analysing your actual queries rather than guessing.
In most cases yes. Many improvements, particularly indexing and query changes, can be applied while the system stays live. Larger structural changes are rehearsed on a copy of your real data first so we know exactly how long they take, and we schedule those for a quiet window with a rollback ready.
Often a large part of the improvement comes from the database alone through indexing, configuration and query changes. Where the application itself is generating inefficient queries, we document precisely what needs changing so your developers can apply it, or we can make those changes ourselves as agreed scope.
By restoring one, which is the only real test. We restore a backup into a separate environment and confirm the data is complete and usable. A backup job that reports success has not proved anything until someone has recovered from it.
Yes. We handle migration between hosts, versions and cloud providers, usually with little or no downtime by keeping the new location in sync while the old one continues serving traffic, then switching over quickly. Record counts are reconciled before the old system is retired.
For business data with customers, orders and relationships between records, a relational database such as MySQL or PostgreSQL is almost always the better choice. Document databases suit cases where the structure genuinely varies from record to record. Most projects that chose otherwise did so for reasons that did not survive contact with real reporting requirements.
Yes. We identify duplicates, records pointing at things that no longer exist and values that break the rules, then correct them in a controlled way with a report of every change. We do not silently fix records, because a quiet correction hides the cause that will produce the same problem again.
Not always. A useful review can often be done from the structure, query logs and monitoring output alone. Where live access is genuinely needed, we ask for limited permissions for a defined period, under a confidentiality agreement signed before the work begins.
A review is a defined piece of work priced separately from any fixes, so you can get an independent picture before committing to anything larger. The findings come with the effort each fix requires, and you are free to have your own team implement them.
Yes. Monthly support covers monitoring, backup verification and restore testing, capacity planning, version upgrades, index maintenance and response when something goes wrong. Any database holding orders, payments or customer records should have someone accountable for it.
Slow pages, wrong reports, duplicate records, a migration you are nervous about, or backups nobody has ever tested. Describe the symptom and we will review the structure, queries, backups and access, then send a written report with each issue and the effort to fix it.
If your own team can apply the fixes, the report is written so they can.
New Delhi, India
+91 8130428118 info@developerdesk.in nikhil@developerdesk.in Support and FAQCustom websites and web applications built on solid data structures.
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