Cloud-native Architecture
Context
The situation before the platform
Why an organisation working with property and market data needed one analytics environment instead of exports, spreadsheets and fixed reports.
| The business |
A real estate company that needed analytical detail across American industries, regions, firms, occupations and demographic groups, serving executives, operations teams and analysts from the same data. |
| The starting point |
Data sat in disconnected systems. Teams manually gathered it, reshaped it into usable formats and cleaned inconsistencies before any analysis could begin, so effort went into preparation rather than insight. |
| The trigger |
Existing tools returned only surface-level, pre-defined reports. There was no way to dig deeper, ask new questions on the fly or see performance as it changed, which slowed decisions and reduced accuracy. |
| What they wanted |
One connected analytics environment: customizable dashboards, interactive visualizations, real-time reporting and shared insights, accessible to every department rather than to analysts alone. |
| Constraints |
Data exploration had to stay usable for non-technical users while remaining deep enough for analysts · multiple sources had to stay synchronised in real time without silos reappearing · role-based access had to protect data as adoption widened · the architecture had to absorb growing data volume and user load without losing responsiveness. |
System
What it runs at today
The platform as delivered, live and scaling on the web.
Subscription growth
35%
Growth in subscription after personalized dashboards and live analytics shipped
Core features
5
Unified data intelligence, real-time analytics, customizable dashboards, advanced visualization and collaboration
Engagement length
24+
Months as an enterprise engineering partnership, through build and scaling
Team size
5+
Design, frontend, backend, data and QA across the full product
The engineering problem
Four gaps that shaped the build from day one
Not vague pain points — the specific obstacles in managing, interpreting and using scattered data, each paired with what we did about it.
0
1
Preparation was consuming the time meant for analysis
Teams spent significant time gathering data from different systems, organising it into usable formats and cleaning inconsistencies before anything could be analysed. The repetition slowed workflows and increased the chance of human error, making the data itself less reliable.
What we did
A centralised data architecture with unified schemas and structured ETL pipelines, plus a real-time synchronisation framework and automated ingestion through RESTful APIs, so data arrives consistent rather than being reassembled by hand.
0
2
Pre-defined reports left no room to ask a new question
Existing tools produced surface-level insight through fixed reports and static outputs. There was little scope to explore patterns or follow a question where it led, so meaningful trends and hidden opportunities stayed out of reach.
What we did
Interactive visualization built on React.js — dynamic charts, graphs, heatmaps and geospatial maps — turning complex datasets into views users can explore themselves to spot patterns and anomalies.
0
3
The same fixed dashboard for every role
Reporting was rigid and standardised, giving every user identical dashboards regardless of their role or the metrics that mattered to them. Departments worked around the limitation, which created inefficiency and a less relevant view of critical data.
What we did
Customizable dashboards and reports with role-based reporting views, so leadership, operations and analysts each configure the KPIs they track without needing technical help.
0
4
No single source of truth to align on
Data scattered across disconnected systems created silos that limited visibility and transparency. Teams could not reach shared information or agree on one version of the numbers, which led to miscommunication, duplicated effort and slower decisions.
What we did
One unified analytics environment with shareable dashboards and insights, so every department reads the same live data and decisions rest on shared understanding rather than assumptions.
Architecture
How it fits together
Simplified — the shape rather than every service.
0
1
Clients
React.js · Turf.js · Responsive web
Component-based dashboards in React.js, with Turf.js behind the geospatial views, delivered as a responsive web experience so insight reads the same on a desktop or a smaller screen.
0
2
Gateway
RESTful APIs · Role-based access control · Automated ingestion
RESTful APIs connect every data source, automate ingestion and carry high volumes of concurrent requests, with role-based access control deciding what each user reaches.
0
3
Services
Laravel · Node.js · ETL pipelines · Real-time sync · Reporting engine
Laravel and Node.js handle the heavier data operations, with ETL pipelines, real-time synchronisation and automated reporting kept as separate concerns so each can be tuned independently.
0
4
Data & infra
MongoDB · MySQL · AWS · Auto-scaling & load balancing
A centralised data layer on MongoDB and MySQL, deployed on AWS with auto-scaling, load balancing and secure cloud storage for high availability and fault tolerance.
Unified schemas and structured ETL were treated as the foundation rather than a later clean-up: because every source resolves into one consistent model, real-time dashboards and automated reports read from the same numbers.
Solutions
Six systems doing the actual work
Not a features list — the specific things we built behind every number above.
Unified data ecosystem
All sources, departments and systems resolve into one centralised analytics environment, replacing multiple versions of the numbers with a single reliable source of truth.
Real-time data intelligence
Continuously refreshed pipelines mean every dashboard, chart and report reflects the latest data, so teams respond to changes as they happen rather than after the fact.
Customizable dashboards & reports
Each team configures its own views around the KPIs and workflows it owns, from leadership performance summaries to day-to-day operational metrics, without technical expertise.
Advanced data visualization
Complex data becomes interactive charts, graphs, heatmaps and geospatial maps, so patterns, anomalies and trends are legible to non-technical users as well as analysts.
Collaborative decision-making
Dashboards and insights are shared across teams, giving everyone the same view to discuss, align on and act from instead of trading exports between departments.
Scalable, secure infrastructure
A cloud-native architecture on AWS grows with data and user demand while role-based access control keeps each dataset reachable only by the right people.
Key features
What the platform does day to day
Five capabilities, each closing one of the gaps identified above.
| Capability | Runs | Refresh | What it does |
|---|---|---|---|
| Unified data intelligence platform | CENTRALISED | Continuous | Centralises data from multiple sources into one reliable environment, removing silos and giving unified access |
| Real-time analytics & insights | LIVE PIPELINE | Real time | Continuously processes and updates data to reflect live business performance and deliver timely insight |
| Customizable dashboards & reporting | USER - CONFIGURED | On demand | Lets teams tailor dashboards and reports to their own KPIs for personalized data views |
| Advanced data visualization | INTERACTIVE | On query | Converts complex datasets into interactive charts, graphs and maps that simplify trends and patterns |
| Collaborative decision-making environment | SHARED | On publish | Supports sharing of dashboards and insights, improving alignment, transparency and data-driven collaboration |
Integrations
How the moving parts plug in
Source systems, uploads and third-party data reach the platform through one API layer rather than sitting beside it as separate tools.
Connected capabilities
CSV & file uploads
User-supplied data source files
Experian data
External data feed into the model
Geospatial processing
Turf.js behind maps and regions
Cloud infrastructure
AWS hosting, storage and scaling
↓
THROUGH ONE RESTFUL API LAYER ↓
Platform integration layer
Laravel & Node.js APIs
One contract for every data source
ETL & real-time sync
Unified schemas, consistent data
Role-based access control
Who reaches which dataset
↓
INTO THE CORE SERVICES ↓
Core services
Dashboards
Visualizations
Automated reporting
Collaboration & sharing
Data governance
Because uploads, integrated feeds and internal systems all resolve against the same schemas, a user loading a CSV, charting it and sharing the result are three steps in one workspace rather than three separate tools.
Security
What protects the data and who can see it
The platform holds organisational data used across every department, so access control and infrastructure reliability were designed in rather than added later.
Role-based access control
Data is reachable only by the users whose role requires it, with role-based reporting views built into the dashboards rather than applied as a separate permission layer.
Data governance
Governance was implemented alongside access control, so ownership and visibility of each dataset stay defined as more departments adopt the platform.
Secure cloud storage
Critical data sits in secure cloud storage on AWS, with the deployment configured for high availability and fault tolerance at enterprise level.
Reliability under load
Auto-scaling and load balancing absorb varying demand, keeping performance consistent as data volume and concurrent user activity grow.
Process
How we got there
Five stages, starting with the reporting gaps people actually hit rather than a feature list.
0
1
Discover
Stakeholder interviews, workflow analysis and a close study of the existing data ecosystem identified the reporting gaps, usability problems and operational inefficiencies to design around.
0
2
Define
User journeys were mapped for each role that touches data, analytics goals were set, and customizable dashboards, real-time reporting and collaborative sharing were prioritised.
0
3
Design
Wireframes and dashboard concepts established how information would be presented, then interactive prototypes tested charts, graphs and maps for clarity across devices.
0
4
Develop
Multiple data sources were integrated into one system with real-time processing, and dynamic visualizations were built on an architecture designed to scale with data and user growth.
0
5
Validate
End-to-end testing across pipelines, dashboards and APIs, plus usability testing, performance optimisation and user acceptance testing, closed with a documented handoff.
Business impact
What changed for the business
Beyond the headline numbers, three things teams and leadership noticed first.
One analytics environment
Fragmented workflows were replaced by a single reliable environment, with centralised access to real-time dashboards, interactive visualizations and automated reporting for every department.
Decisions that move faster
Manual data handling disappeared from the reporting path, so teams spend their time interpreting current numbers rather than assembling them.
A continuous view for leadership
Leadership gained an ongoing picture of organisational performance, supporting smarter forecasting, proactive planning and measurable operational improvement.
Commercial outcome
What the engineering choices are worth in operating terms
Every headline number traces back to a specific decision, not a vague platform effect.
| Engineering decision | Operating outcome | Measured effect |
|---|---|---|
| Centralised data architecture & ETL pipelines | One consistent model instead of manual gathering and cleaning | 37% faster reporting turnaround |
| Real-time processing & interactive visualization | Current data explored directly rather than read from static reports | 48% more data-driven decisions |
| Shared dashboards & collaborative reporting | Every department works from the same numbers | 29% better cross-department visibility |
| Intuitive interface & customizable views | Non-technical users reach a useful view without analyst support | 94% stakeholder adoption |
Stack
What it's built on
The actual technologies, not feature names with icons attached.
Frontend
- React.js
- Turf.js
Backend
- Laravel
- Node.js
Data & infra
- MongoDB
- MySQL
- AWS
Integrations
- Experian
- RESTful APIs
- CSV & data file uploads
Full case study
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