Analytics Data Architecture Design
Designing a Scalable, Governed Analytics Foundation
Overview
Analytics systems often fail not because of poor dashboards, but because the underlying data architecture is fragile, inconsistent, or difficult to govern. As organisations grow, ad-hoc tables and tightly coupled pipelines quickly become barriers to reliable analytics and decision-making.
This repository presents a reference analytics data architecture designed for small to mid-size data teams. It demonstrates how to structure data for long-term reliability using layered storage, star schema modelling, metadata documentation, and SQL-based validation.
The goal of this project is not to build dashboards, but to show how analytics systems should be designed as durable products, not one-off outputs.
Although implementations vary across organisations, these principles apply broadly to most data analytics environments.
Table of Contents
Architecture Overview
Layered Data Architecture
Star Schema Design
SQL Schema Implementation
Metadata & Data Catalog
Architecture Justification
Validation & How to Run
Limitations & Ethics
Reflection & Future Enhancements
Architecture Overview
This architecture follows a layered analytics design, separating ingestion, transformation, modelling, and consumption concerns.
High-level flow:
Source Systems ↓ Raw Data Layer ↓ Cleaned / Conformed Layer ↓ Analytics Model (Star Schema) ↓ Dashboards / ML / Reporting
This separation reduces coupling, improves data quality control, and allows analytics logic to evolve without breaking upstream systems.
See diagrams in: /diagrams/
Layered Data Architecture
- Raw Layer
Immutable ingestion of source data
No transformations applied
Preserves original structure for auditability
- Cleaned Layer
Standardised formats
Null handling and type corrections
De-duplication logic applied
- Analytics Layer
Business-friendly models
Optimised for querying and reporting
Enforces relationships and grain consistency
This approach mirrors modern lakehouse and warehouse-centric architectures used across the UK digital ecosystem.
Star Schema Design
The analytics layer uses a star schema, optimised for analytical queries and KPI consistency.
Dimensions
dim_customer — stable descriptive attributes
dim_date — calendar and reporting logic
dim_channel — interaction or acquisition source
Fact Table
fact_transactions — transactional or behavioural events
This design:
simplifies joins
improves query performance
ensures metrics are calculated consistently across teams
SQL definitions: /sql/
Diagram: /diagrams/star_schema.png
SQL Schema Implementation
All schemas are implemented using portable SQL, designed to run locally in SQLite for validation.
Example (dimension table):
CREATE TABLE dim_customer ( customer_key INTEGER PRIMARY KEY, customer_id TEXT, customer_segment TEXT, is_active INTEGER );
Foreign key constraints are used in the fact table to enforce referential integrity.
This ensures analytics outputs cannot silently drift due to broken joins or inconsistent keys.
Metadata & Data Catalog
A lightweight metadata catalog is included to document:
table purpose
column definitions
data ownership assumptions
refresh expectations
Location: /metadata/
This documentation layer supports:
onboarding of new analysts
governance reviews
future handover or scale-out
Metadata is treated as a first-class artefact, not an afterthought.
Validation & How to Run Locally
You can validate the full architecture locally using SQLite.
Steps
From the repository root:
sqlite3 analytics.db
Then execute schemas in order:
.read sql/dim_customer.sql .read sql/dim_date.sql .read sql/dim_channel.sql .read sql/fact_transactions.sql
To verify:
SELECT name FROM sqlite_master WHERE type='table';
Expected tables:
dim_customer
dim_date
dim_channel
fact_transactions
This sequential execution validates dependencies and referential integrity.
Limitations & Ethics
This project focuses on structural design, not live production data.
Sample schemas avoid sensitive personal attributes
No real customer or donor data is used
Privacy-impact considerations are documented but not enforced at runtime
In real environments, this architecture should be paired with:
access controls
PII masking
retention policies
audit logging
Responsible data architecture extends beyond schema design into governance and compliance.
Architecture Justification
This design was chosen to prioritise:
Scalability — supports growth without re-modelling
Consistency — single source of truth for metrics
Governance — clear ownership and documentation
Maintainability — changes isolated to specific layers
Rather than optimising for short-term delivery speed, the architecture optimises for long-term analytical reliability.
Reflection & Future Enhancements
Designing analytics architecture reinforced the importance of thinking beyond tools and focusing on system behaviour over time.
Future enhancements could include:
automated schema validation tests
data quality checks per layer
integration with a BI semantic layer
role-based access controls
Strong analytics systems are built intentionally, not incrementally.
Final note
This repository is intended as a reference design, demonstrating architectural thinking rather than a single use case.
Although implementations vary across organisations, these principles apply broadly to most data analytics environments.