We design, engineer and govern modern data platforms across Microsoft Fabric and Azure. From architecture and ingestion to modelling, analytics and AI readiness.
Engineered with the Modern Enterprise Stack
From platform strategy and architecture to engineering, warehousing, semantic modelling, analytics and governance. We help organisations build data platforms designed for production, scale and long-term maintainability.
Define the target architecture, migration roadmap, operating model and technology strategy for a scalable modern data platform.
Assess existing pipelines, architecture, governance and organisational readiness to identify gaps and priorities.
Define a phased migration plan that manages dependencies, minimises disruption and addresses legacy constraints.
Review capacity, licensing, storage and workload costs to identify opportunities for more efficient platform operation.
Define the data quality, governance, metadata and semantic foundations needed to support AI adoption.
Design scalable lakehouse and warehouse architectures aligned with business domains, ownership and governance requirements.
Bronze, Silver and Gold tiered storage pipelines.
Design domain ownership and shared data products where a federated model is appropriate.
Zero-copy direct memory loading for reporting.
Elastic provisioning aligned to peak hours.
Reliable batch and real-time data pipelines with automated ingestion, transformation, orchestration, testing and monitoring.
Build maintainable ingestion and transformation workflows using Fabric pipelines, notebooks, PySpark and Delta tables.
Select ingestion patterns that match source capabilities, data volumes and business freshness requirements.
Implement version control, validation and controlled deployment across development, test and production environments.
Monitor pipeline health, data freshness and quality, with actionable alerts to support investigation and recovery.
High-performance lakehouse storage, dimensional warehousing, capacity allocation and compute tuning.
Design governed analytical storage and minimise unnecessary data duplication.
Optimise Delta table layout and maintenance to improve query performance and storage efficiency.
Modernise SQL and legacy analytical workloads for Fabric and Azure.
Throttling mitigation and capacity scheduling.
Dimensional models and governed semantic layers that create consistent, reusable business definitions across analytics.
Conformed dimensions and facts designed for analytical workloads.
Single source of truth across business units.
Point-in-time snapshots for audits.
Preventing conflicting KPI calculations.
High-performance semantic models, Direct Lake architectures, governed self-service analytics and executive reporting.
Optimise semantic models for high-performance analytics over OneLake.
Identify model, measure and query bottlenecks to improve report performance.
Endorsed datasets for confident reporting.
High-impact decision interfaces for leadership and operational teams.
Establish data ownership, cataloguing, access controls and lineage, with governed metadata and business definitions to support analytics and AI readiness.
Configure scanning and metadata discovery for supported data sources, with classification and ownership to improve visibility.
Design row-, object- and role-level security aligned with business responsibilities and enterprise access policies.
Establish traceability across supported ingestion, transformation and reporting assets to understand dependencies and assess change impacts.
Prepare governed business definitions and metadata to improve grounding for AI and Copilot experiences.
A structured delivery approach with clear milestones, validation and handover, from platform assessment through implementation and operational scale.
Review business priorities, platform health and data maturity to identify gaps and define a prioritised roadmap.
Define the target architecture, ingestion patterns, security controls and operating model aligned with business needs.
Engineer pipelines, analytical storage and semantic models, with automated testing and controlled deployment.
Establish data ownership, quality checks, access controls and lineage to support trusted platform operation.
Optimise performance and cost, enable internal teams and extend the platform for evolving analytics and AI needs.
securitySecurity, governance and quality are embedded throughout delivery, with each stage building on the previous one.
We combine platform strategy with hands-on engineering to deliver reliable, governed data platforms. We work alongside your teams, sharing knowledge and establishing the standards needed to operate and evolve the platform.
Experienced data platform practitioners remain directly involved throughout the engagement, from architecture and engineering to validation and handover.
Our practitioners bring experience designing and delivering data platforms across complex enterprise environments, with demanding security, governance and operational requirements.
Expertise spanning ingestion, lakehouse and warehouse engineering, dimensional modelling, semantic layers, analytics and governance.
Specialist capability across Microsoft Fabric, Azure and Power BI, covering data integration, analytical storage, semantic modelling and governance.
Maintainable architectures, deployment standards, documentation and knowledge transfer that equip your teams to operate and develop the platform.
Establish trusted data, governed metadata, lineage and consistent business definitions to support responsible adoption of Copilot, GenAI and enterprise AI solutions.
Learn about our services, technology expertise and approach to delivering your data platform.
We work with organisations modernising their data platforms, improving analytics or establishing stronger governance. We support both businesses building their foundations and enterprises managing complex data environments.
Yes. We provide platform assessments, architecture and strategic advice, alongside hands-on engineering and implementation. Engagements can cover a specific challenge or delivery from assessment through handover.
Our core focus is Microsoft Fabric, Azure and Power BI, including data integration, lakehouse and warehouse engineering, semantic modelling and governance. We select technologies and implementation patterns to suit your requirements.
We incorporate governance from the outset, covering ownership, access controls, data quality, metadata and lineage. We work with your business and IT teams to establish practical controls aligned with your policies and operational needs.
Timelines depend on scope, complexity, source access and delivery dependencies. Following an initial discovery discussion, we agree the deliverables, milestones and estimated timeline with you.
Yes. We can lead delivery or work alongside your engineering, architecture and analytics teams. We share knowledge, documentation and delivery standards to support long-term internal ownership.
Book a discovery call to discuss your current platform, priorities and challenges. We will help identify a suitable starting point, whether that is an assessment, advisory support or a defined delivery engagement.
Tell us about your data platform goals. We will be in touch within one business day.
Or email us directly at hello@tpfanalytics.com