Cloud Infra, Data & AI Platform
Data Analytics
The whole chain: ingestion and ETL, warehouse and lakehouse, semantic model, then the dashboard.
Implement analytics as one chain rather than one link of it: ingestion and ETL from the source systems, data warehouse and lakehouse design, the governed semantic model that fixes what a measure means, then the dashboards and reporting a decision is actually taken on. The approach is Cloud First on Microsoft Fabric and Azure, and on-premises deployment stays supported for the analytics estate that still holds most of the record.
Microsoft FabricDataThe Microsoft products behind it
What each product protects or produces, how it works, and the problem it answers.
- Microsoft Fabric
One platform for the whole analytics chain
One platform carrying the analytics chain from ingestion to dashboard, on OneLake as a single copy of the data.
- Lakehouse
- Data Warehouse
- OneLake
- +3 more
Azure Synapse AnalyticsLarge scale analytics on the Azure data warehouse
The data warehouse for large scale analytics on an Azure native estate, and the usual starting point for a later move to Microsoft Fabric.
- Data Warehouse
- Azure Analytics
- Large Scale Analytics
- +1 more
Azure Data FactoryOrchestrates data movement across every source
The ingestion and orchestration layer that moves data from ERP, databases, files, and APIs into the warehouse or the lakehouse.
- Ingest
- ETL
- Orchestration
- +1 more
Azure SQL DatabaseThe managed relational store under the platform
Managed relational storage inside the analytics chain, without a server to patch, back up, or size by hand.
- Managed Database
- Relational Storage
- Azure Data Platform
Six service stages, from source data to dashboard
The analytics chain is delivered whole rather than one link of it. These six stages are the order the work runs in, from raw data in the source systems to the dashboard a decision is actually taken on.
Data Ingestion and Integration
- Pulling data from a range of sources: ERP, databases, files, and APIs
- Fabric Data Factory and Azure Data Factory for the cloud path
- SSIS where the requirement is on-premises
Data Warehouse and Lakehouse Design
- Data model design, dimensional and medallion
- Warehouse build on Microsoft Fabric or Azure Synapse Analytics
- SQL Server warehouse for on-premises deployment
Semantic Model and Governance
- A single source of truth with DAX measures defined centrally
- Row-level security and calculation groups
- Metadata governance and documentation
Dashboard and Reporting
- Power BI in the cloud for interactive dashboards
- Power BI Report Server for on-premises deployment
- Paginated reports and reports for mobile devices
Enterprise Planning and AI
- Planning and forecasting on Fabric IQ
- Microsoft Copilot and natural language question and answer
- Advanced analytics
Migration and Modernisation
- Moving from other business intelligence platforms to Power BI
- Moving from other warehouses and Azure Synapse Analytics to Microsoft Fabric
- Upgrading an on-premises platform to the cloud
Two architecture paths
The default path is cloud first on Microsoft Fabric, with a single copy of the data in OneLake and the medallion layers on top of it. The on-premises path stays available in full, running the same chain of stages.
Cloud first on Microsoft Fabric
Default path- Source data
- Ingest (Data Factory)
- OneLake and Lakehouse
- Data Warehouse
- Semantic Model
- Power BI
Medallion layers
On-premises alternative
Alternative- Source data
- SSIS
- SQL Server Data Warehouse
- SSAS Tabular
- Power BI Report Server
This path is for organisations that have to keep the analytics estate on-premises, usually for regulatory reasons.
Product stack by deployment
The components used on both paths, grouped by where they are deployed. Components that need a licence or capacity of their own are marked, so the scope is clear from the start.
On-Premises
On-premises- SSIS
- Ingest and ETL, data integration on-premises
- SSAS Tabular
- Semantic model, analytical model in Tabular mode
- SSRS
- Reporting, paginated and operational reports
- Power BI Report Server
- Dashboard, the on-premises Power BI portal
Cloud: Azure
Cloud- Azure Data Factory
- Ingest, data orchestration and integration in the cloud
- Azure SQL Database
- Storage, managed relational database
- Azure Synapse Analytics
- Data warehouse, large scale analytics
- Azure Analysis Services
- Semantic model, managed analytical model
Cloud: Microsoft Fabric
Cloud- OneLake
- Foundation, unified storage holding a single copy of the data
- Data Factory
- Ingest, pipelines and data integration
- Data Engineering
- Transformation, lakehouse, Spark, and notebooks
- Data Warehouse
- Warehouse, managed SQL warehouse
- Database
- Storage, native operational database
- Power BI
- Dashboard, integrated visualisation and BI
- Real-Time Intelligence
- Streaming and intraday analyticsNeeds Fabric capacity
- Data Science
- Machine learning and advanced analyticsNeeds Fabric capacity
Cloud: Power BI
Cloud- Power BI Service
- Dashboard, the cloud BI platform for sharing and collaboration
Delivery models
Five ways the work is run. The choice follows the shape of the requirement rather than the other way round, and models can be combined within one engagement.
Projects Based
Scope, timeline, and deliverables are defined up front.
Suited to
An end to end implementation, from ingestion through the warehouse to the dashboard, with a fixed scope.
Man Month
Resourcing provided as a monthly allocation.
Suited to
Continuous analytics development with a dedicated team.
Quota Mandays
A quota of effort drawn down as it is needed.
Suited to
Incremental enhancement of a data platform already running.
Training
Enablement on Power BI, Microsoft Fabric, and semantic modelling.
Suited to
Building the customer's own internal capability and adoption.
Support and Maintenance
Post-implementation support, both preventive (monitoring, health checks, problem prevention) and corrective (fixing issues after they occur).
Suited to
Keeping the end to end platform running and optimal.
How the work is scoped
Engagements delivered by this capability, each with defined modules, deliverables and a timeline.
- Cloud Infra, Data & AI Platform
Data Platform Foundation
A foundation engagement that carries scattered data through ingestion and ETL into one governed data warehouse for reporting and analytics.
- Work modules
- 3
- How scope is agreed
- Scope depends on the number of source systems and reporting requirements, agreed with the practice team before work begins
- Cloud Infra, Data & AI Platform
Data Protection Baseline
Discovery and classification first, then a labelling and loss prevention baseline that reflects what the organisation actually holds.
- Work modules
- 3
- How scope is agreed
- Scope depends on the size of the data estate and the number of regulatory obligations in scope, agreed with the practice team before work begins