Microsoft SQL Server
A great deal of enterprise data already lives in SQL Server, often behind a system nobody wants to replace. We work with that reality — tuning it, integrating with it, and being honest about when migrating is not worth it.
In place
Work with what you already run
Measured
Before-and-after query timings
Integrate
Without replacing the system
Both ways
SQL Server and PostgreSQL
In manufacturing, distribution and established enterprises, a large share of operational data sits in Microsoft SQL Server — frequently behind an older application that works, that people know, and that nobody has a business case to replace. A development partner who can only work with open-source databases either walks away from that work or proposes a migration nobody asked for.
We work with SQL Server as a first-class part of the estate. That means query and index tuning on databases that have grown past their original design, T-SQL and stored procedure work where the business logic legitimately lives in the database, reporting and extract layers that do not compete with transactional load, and building new applications that integrate with the existing schema rather than requiring it to change.
We also work extensively with PostgreSQL, which puts us in a position to give genuinely neutral advice about migration — including, frequently, that you should not.
Why Established SQL Server Systems Slow Down
Rarely the hardware. Usually a schema that has outlived the assumptions it was designed under.
Indexes for a data volume that no longer exists
Indexing designed when the table held tens of thousands of rows and now holds tens of millions. Queries that were instant have degraded gradually enough that nobody identified the moment it became a problem.
Reporting running against the transactional database
Month-end reports scanning large tables while the shop floor is writing to them, producing lock contention and timeouts that look like an application fault and are not.
Business logic buried in thousands of lines of T-SQL
Rules spread across stored procedures and triggers with no tests and no documentation. Every change carries real risk because nobody is confident what else depends on the procedure.
Cursors where set operations belong
Row-by-row processing in loops for work that a single set-based statement would complete in a fraction of the time. Common in procedures written under time pressure years ago and never revisited.
SQL Server Capabilities
Improving what exists, integrating around it, and migrating only when it genuinely pays.
Query & Index Tuning
Execution plan analysis, index design and rewriting of the specific queries causing pain — with measured before-and-after timings rather than general advice.
T-SQL & Stored Procedures
Writing, refactoring and documenting stored procedures, functions and triggers, including replacing cursor-based processing with set-based logic where it belongs.
Schema Design & Refactoring
Normalisation, partitioning of large tables, archival strategy and constraint design — applied incrementally so the running system is never taken offline for it.
Reporting & Analytics Layer
Read replicas, reporting schemas and materialised extracts so analytical queries stop competing with transactional work for locks and IO.
Integration & APIs
REST APIs over an existing SQL Server schema so modern applications, dashboards and mobile clients can use the data without the legacy system being touched.
ETL & Data Sync
Scheduled and event-driven synchronisation between SQL Server and other systems, with reconciliation checks so a silently failed sync is detected rather than assumed to have worked.
Security & Access Control
Role and permission review, least-privilege access, encryption at rest and in transit, and auditing of access to sensitive tables.
Backup, Restore & Recovery
Backup strategy matched to a stated recovery objective, and — the part usually missing — restores actually tested rather than assumed to work.
Migration Assessment
An honest evaluation of moving to PostgreSQL: what it would cost, what would break, what it would save, and whether the return justifies it. Frequently it does not.
Working With an Existing Database Estate
Measure before changing anything
Database performance work goes wrong when it starts from opinion. Adding indexes because a table is large, or rewriting a procedure because it looks inefficient, as often as not makes things worse — every index has a write cost, and the query that feels slow is frequently not the one consuming the resources.
So we start with evidence: execution plans for the queries that actually run, wait statistics showing what the server is really waiting on, and index usage data showing which indexes are used, which are ignored and which are being maintained for nothing. Changes are made one at a time with measured before-and-after timings. It is slower to start and it is the only way to know whether the work helped.
- Execution plans and wait statistics before any change
- Index usage reviewed — unused indexes cost writes for nothing
- One change at a time with measured before-and-after
- Findings written down so the reasoning survives the engagement
Separate reporting from transactions
A recurring pattern in established systems is a transactional database being used for heavy analytical queries. Month-end reporting scans large tables while operational work is writing to them, and the result is lock contention, timeouts and an application that appears to fail at exactly the busiest time.
The fix is usually straightforward: move analytical load off the transactional path, through a read replica, a reporting schema, or scheduled materialised extracts depending on how fresh the data genuinely needs to be. That last question is worth asking properly — reports that people assume need to be real-time are usually fine at hourly, and that difference changes what the solution costs by an order of magnitude.
- Analytical load moved off the transactional path
- Read replicas or materialised extracts based on real freshness needs
- Long-running reports never holding locks against operational writes
- “How fresh does this actually need to be?” asked before designing
Build around the legacy system, not through it
When a client needs a modern dashboard, a mobile app or a new module and the data lives in an older SQL Server application, replacing that application is rarely the right first move. It works, staff know it, and a replacement is a multi-year risk taken for benefits that have not been demonstrated.
We build an API layer over the existing schema instead, with clearly defined read and write boundaries, so new applications consume the data without the legacy system being modified. That delivers value in weeks rather than years, and it has a second effect: once the new layer carries real traffic, you have a realistic basis for deciding whether replacing the old system is worth it — which is a much better position than making that call from a slide deck.
- API layer over the existing schema, legacy system untouched
- Clear read and write boundaries so integrity is not compromised
- Value delivered in weeks rather than through a multi-year replacement
- A real basis for the eventual replace-or-keep decision
On migrating away from SQL Server
We work with PostgreSQL at least as much as with SQL Server, so we have no commercial reason to argue either way. Our honest position:
- Licensing cost alone is rarely a sufficient reason. Migration cost, retraining, rewriting stored procedures and the risk of subtle behaviour differences frequently exceed several years of licence savings.
- Heavy T-SQL logic makes migration expensive. If thousands of lines of business rules live in procedures with no tests, they must all be rewritten and revalidated. Cost that honestly before committing.
- Migrate when there is a real driver. Cloud strategy, a genuine feature need, or an application being rewritten anyway — those justify it. “Open source is better” does not.
- Fix the performance problem first. A migration will not fix bad indexing; it will carry it across. Tune what you have, then decide from a stable position.
What We Pair It With
Applications
Alternative
Integration
Platform
Quality
Frequently Asked Questions
Often not, and we say that as a team that works heavily with PostgreSQL. Licensing savings alone rarely cover migration cost, retraining, rewriting stored procedures and the risk of subtle behaviour differences. Migration makes sense when there is a real driver — a cloud strategy, a genuine feature requirement, or an application being rewritten anyway. We will give you a costed assessment rather than a recommendation shaped by what we would rather build.
Related Expertise
PostgreSQL
The other side of the comparison — and where we build new systems.
Python & Django
Building APIs and applications over existing schemas.
Angular Development
Enterprise front-ends over established data estates.
Manufacturing ERP
Where legacy plant databases are the norm rather than the exception.
Dashboards & Analytics
Reporting layers that do not fight transactional load.
Custom Software Development
Integrating with what you have instead of replacing it.
Data Locked in a System You'd Rather Not Replace?
Slow queries, reports fighting operations, or a modern app needed over an old schema. Tell us what you're running and we'll tell you what to fix, what to build around, and what to leave alone.
Serving startups, factories and enterprises across India, the US, UK, Australia & Europe.