Xtrable
Data architecture is where business decisions meet technical reality. Every organisation depends on data, but few have a clear picture of where their data comes from, how it transforms, and whether it can be trusted. Xtrable brings structured, model-driven data architecture to organisations that cannot afford ambiguity in their data estate.
If you cannot trace a number on a regulatory report back to its source system through every transformation, you do not have data governance. You have data hope.
Data Architecture Services
Data Modelling
Conceptual, logical, and physical data modelling using NeoArc Studio's built-in ERD editor with visual entity-relationship diagrams, column definitions, foreign key auto-fill, and model validation.
Data Lineage
End-to-end data lineage from source systems through transformation pipelines to consumption points. Explorable lineage graphs that show exactly how data moves and changes.
Data Governance
Governance frameworks that define ownership, quality standards, access controls, and retention policies. Practical governance that teams actually follow, not compliance theatre.
Data Platform Architecture
Architecture for data warehouses, data lakes, lakehouses, and streaming platforms. Technology selection, pipeline design, and scalability planning grounded in practical experience.
Analytics Architecture
Design for business intelligence, reporting, and analytics platforms. Semantic layers, data marts, OLAP structures, and self-service analytics architecture.
Data Integration
ETL/ELT pipeline architecture, CDC patterns, API-based data exchange, and real-time streaming architecture. Designs that handle volume, velocity, and variety.
Data Architecture with NeoArc Studio
NeoArc Studio includes purpose-built features for data architecture work. Data models are not separate artefacts. They are part of the same connected architecture model as your system diagrams, API definitions, and documentation.
| Feature | What It Provides |
|---|---|
| ERD Editor | Visual entity-relationship diagrams with columns, data types, keys, indexes, and referential integrity validation |
| Schema Definitions | Structured schema objects with field types, validation rules, and documentation connected to the architecture model |
| Data Model Views | Multiple visual perspectives on the same underlying data model. Filter by domain, by system, or by data classification |
| Lineage Graphs | Graph diagrams that show data flow between systems, transformations, and consumption points |
| Database Profiles | Type resolution and DDL generation for PostgreSQL, SQL Server, MySQL, Oracle, and other database engines |
| Search Profiles | Field mapping generation for search engines (Azure Cognitive Search, Elasticsearch) from your data model |
| Published Documentation | Data dictionaries, schema reference, and lineage documentation published as searchable websites |
Data Architecture by Sector
Banking
Data architecture for banking demands complete lineage and auditability. We design data platforms that satisfy BCBS 239 requirements, support regulatory reporting, and enable real-time risk calculations.
- Transaction data architecture with complete audit trails
- Regulatory data lineage for FCA and PRA reporting
- Customer data platforms with consent management
- Anti-money laundering data architecture
E-Commerce
E-commerce data architecture must handle high-volume transactions, real-time analytics, and personalisation at scale while maintaining data quality and privacy compliance.
- Product catalogue data architecture for millions of SKUs
- Customer behaviour and analytics pipelines
- Real-time inventory and pricing data flows
- GDPR-compliant customer data architecture
Legal
Legal data architecture centres on document management, access control, and information barriers. Every piece of data must be traceable and its access auditable.
- Document metadata and classification architectures
- Ethical wall and information barrier data controls
- Case data warehousing and analytics
- Client data segregation and confidentiality
Public Sector
Public sector data architecture requires transparency and clear governance. Data must be well-managed, well-documented, and accessible to the teams and systems that need it.
- Data platform architecture and governance
- Open data publication pipelines
- Cross-departmental data sharing frameworks
- Data quality governance for citizen-facing services
A good starting point
If you are unsure where to begin, a data architecture assessment maps your current state and identifies the gaps that matter most. From there, we can scope targeted work on modelling, lineage, governance, or platform design.