Database Engineering
PostgreSQL, MySQL, and Microsoft SQL Server — schema design, the query that takes six minutes, and the migration you have been putting off.
SOLUTION · DATA
Data & Operations Platforms
One version of the numbers, available to everyone who needs them.
TARGET PROFILE
Who this architecture is engineered for.
Built specifically for operational teams experiencing structural friction at scale.
The profile
For a management team where two departments arrive at a meeting with different figures for the same month, both defensible, both produced by a spreadsheet somebody maintains personally — and where the first twenty minutes goes on reconciling rather than deciding.
The core bottleneck
Disagreement about numbers is rarely caused by bad data. It is caused by undefined data. Revenue means one thing to finance and another to sales, both are correct within their own definition, and neither definition is written down anywhere that the other would find. Each department then builds its own extract to answer its own question, and within two years there are nine of them, refreshed on different schedules, filtered by rules that live in a spreadsheet formula. The visible symptom is meetings that begin with reconciliation. The underlying cost is slower decisions and a gradual loss of confidence in reporting generally — at which point people go back to asking somebody, which is where the whole thing started.
SYSTEM MODULES
What we build and integrate.
Every component is engineered for multi-tenant security, auditability, and zero-downtime maintenance.
A metric layer where each definition is written once, with its owner and its exclusions stated
Ingestion from the source systems on a schedule matched to how the data is actually used
Transformations in version control, so a number can be traced to the code that produced it
Data quality assertions that stop a bad load rather than publishing it
Reporting interfaces for the people who need answers, not only for the people who write queries
Freshness indicators, so a viewer can see how current a figure is without asking
Access control appropriate to figures that are not for everyone
ARCHITECTURE
System composition & topology.
How the core modules, data boundaries, and integrations connect in production.
Node 01
Source systems
Finance, resource planning, the product database, the customer platform — read incrementally where possible, with an agreed contract for each rather than an assumption.
Connects to
- ingest
Node 02
Ingestion
Scheduled and event-driven loading with idempotent runs, handling for late-arriving records, and backfill as a supported operation rather than a manual repair.
Connects to
- raw
- quality
Node 03
Raw layer
Source data stored as received, unmodified and timestamped. Keeping it means a transformation bug is fixable by re-running rather than by re-extracting from a system that has since moved on.
Connects to
- transform
Node 04
Transformation
Version-controlled, tested transformations producing the modelled layer, with each run recording the code version so a past number can be reproduced exactly.
Connects to
- metrics
- quality
Node 05
Quality checks
Volume, distribution, freshness, and referential assertions that fail a load rather than publishing it. The dangerous case is not a broken pipeline but a successful one that produced nothing.
Connects to
- metrics
- monitoring
Node 06
Metric definitions
One definition per metric, with owner, exclusions, and the reason for each, referenced by every report. This is the component that actually ends the disagreement.
Connects to
- reporting
- api
Node 07
Dashboards and reports
The interfaces people use, each showing its own freshness, and each drawing from the metric layer rather than restating a calculation locally.
Connects to
- monitoring
Node 08
Programmatic access
Metrics available to applications and services, so a figure shown inside a product is the same figure shown in the board pack.
Connects to
- monitoring
Node 09
Pipeline monitoring
Run history, freshness alerting, and failure notification to somewhere a person watches — including the alert for data that simply stopped arriving.
Connects to
- ingest
TECHNOLOGY DEPTH
Technologies powering Data & Operations Platforms.
Standardized architecture frameworks and data components integrated in this solution.
Frontend
- TypeScript
- Next.js
Backend
- Python
Data
- PostgreSQL
- Microsoft SQL Server
- MySQL
Cloud
- Docker
- Azure
- AWS
QUESTIONS & ANSWERS
Questions regarding data & operations platforms.
Direct answers to common technical and engagement questions.
Not necessarily. At the volumes most mid-sized businesses work with, a well-modelled PostgreSQL or Microsoft SQL Server database handles reporting comfortably and is far cheaper to operate than a dedicated warehouse platform. We size that decision on your actual row counts and query patterns, not on the category the tooling belongs to.
You do — and the work is getting the decision made and recorded, which is usually the hardest part of the project and has nothing to do with technology. We facilitate it, write the definitions down with their owners, and build so that changing one is a reviewed change rather than an edit somebody makes in a spreadsheet.
Generally yes, and it is often the right call. The value is in the modelled layer and the metric definitions underneath; the interface on top matters much less. Pointing an existing tool at a properly modelled source usually resolves most of the disagreement on its own.
As current as the use case genuinely needs, which is a decision worth making explicitly. Daily is sufficient for most management reporting and dramatically cheaper than near-real-time. Where an operational view needs minutes, we build that path specifically rather than raising the frequency of everything.
Have something ambitious to build?
Tell us what you're working on. We'll tell you honestly whether we're the right team for it.