Data Warehouse Design & Implementation
Build structured data warehouses that support consistent reporting, business intelligence and analytical workloads.
Data Warehouses & Lakehouses for Better Business Decisions
Bring your data together, simplify analytics and build a trusted foundation for reporting and business intelligence with NCS London.
Overview
Turn Fragmented Data into Trusted Insights
Data spread across disconnected systems can slow reporting, create inconsistencies and limit visibility across your business.
NCS London helps you design and modernise data warehouses and lakehouses that bring information together, support reliable analytics and adapt to changing business needs.
Challenges
Is Your Existing Data Architecture Limiting Business Intelligence?
Fragmented Data SourcesBusiness-critical information remains distributed across systems, preventing a unified view of performance.
Inconsistent ReportingDifferent data definitions and transformation rules produce conflicting metrics across departments.
Slow Analytical WorkloadsLegacy architectures struggle with growing data volumes, complex queries and changing reporting demands.
Limited Access to Diverse DataTraditional structures can make it harder to combine structured and semi-structured data for advanced analysis.
Rising Platform ComplexityMultiple tools, duplicated pipelines and disconnected environments increase management overheads.
Limited AI ReadinessData silos, inconsistent quality and weak governance make it harder to prepare trusted data for AI initiatives.
Your data platform should make information easier to consolidate, govern and analyse, not introduce more complexity into your technology environment.
What We Offer
Data Warehouses and Data Lakehouses Services
We help you select and build an architecture that addresses your existing data constraints and supports your future analytical requirements.
Data Lakehouse Architecture
Design lakehouse environments that bring data engineering, analytics and supported data workloads together.
Data Integration & Consolidation
Connect data from relevant systems to create a more consistent view of business information.
Data Quality & Governance
Establish data validation, quality controls and governance practices that improve trust in analytical data.
Data Platform Performance & Optimisation
Assess workloads, resource usage and platform performance to identify opportunities for improvement.
Analytics & Reporting Enablement
Prepare trusted data for reporting, dashboards and business intelligence across relevant teams.
Platform Selection
Databricks vs Snowflake vs BigQuery: Choosing the Right Platform
The right choice depends on your existing technology environment, workload characteristics, governance needs and intended business outcomes.
Databricks
An integrated lakehouse approach spanning data engineering, large-scale processing, analytics and suitable AI or machine learning workloads.
Suitable for
Architectures that need data engineering, analytics and machine learning workloads on a single lakehouse platform.
Snowflake
A governed analytical data environment supporting SQL-driven workloads, data sharing and scalable warehouse capabilities.
Suitable for
Teams prioritising governed analytics, controlled data sharing and scalable warehouse performance.
Google BigQuery
Serverless analytical workloads within Google Cloud, with scalable SQL analytics and integration across the wider Google Cloud ecosystem.
Suitable for
Google Cloud environments needing serverless SQL analytics and close integration with Google Cloud services.
Not sure which platform fits? We assess workload suitability, integration requirements, governance, performance, skills and operating costs before recommending an architecture.
Discuss Your Data Platform RequirementsOur Approach
From Data Strategy to a Working Platform
Our structured approach connects business requirements with architecture, implementation and ongoing improvement.
Each engagement is scoped around your current environment, business priorities and target-platform requirements.
- 01
Assess & Discover
Review data sources, business requirements, current architecture and reporting needs.
- 02
Design the Architecture
Define the target platform, data flows, governance requirements and implementation priorities.
- 03
Build & Validate
Develop the platform and data workflows, then validate data quality and workload requirements.
- 04
Deploy & Optimise
Transition to the agreed environment and review performance against business and technical goals.
Business Outcomes
What a Modern Warehouse or Lakehouse Can Deliver
Better Business Insight
Make trusted information more accessible for reporting, analysis and business decisions.
Simpler Data Operations
Reduce complexity across data workflows and improve consistency in how information is managed.
Greater Data Confidence
Strengthen data quality and governance to support more reliable reporting.
A Platform Ready to Grow
Build an architecture that can adapt to changing data volumes and analytical needs.
Actual outcomes depend on architecture design, data quality, implementation and ongoing governance.
Why NCS London
Why Choose NCS London for Data Warehouses and Data Lakehouses?
Architecture Aligned with Business Goals
Connect platform decisions to reporting requirements, operational needs and long-term growth.
Multi-Platform Perspective
Evaluate Databricks, Snowflake and BigQuery against your specific workloads rather than defaulting to one technology.
Integration-First Implementation
Address data silos, transformation dependencies and source-system connectivity as part of the overall design.
Governance Built into the Architecture
Consider data quality, access controls, lineage and ownership from the outset.
Performance and Cost Awareness
Account for workload behaviour, processing requirements and operating costs throughout platform design and optimisation.
Support as Your Data Grows
We help optimise, extend and govern your platform after implementation, so it continues to support new data sources, users and analytics requirements.
Frequently Asked Questions
A data warehouse is designed primarily for structured, governed analytical data and business reporting. A data lakehouse combines data lake flexibility with capabilities that support data management and analytical workloads. The right architecture depends on your data types, processing needs, governance requirements and intended use cases.
We assess your workloads, existing cloud environment, integration requirements, analytical use cases, governance needs, skills and total operating costs. Databricks can suit broader lakehouse and data engineering requirements, Snowflake can suit governed analytical and data-sharing workloads, and BigQuery can suit serverless SQL analytics in Google Cloud. The final choice depends on your specific requirements.
Yes. We assess the current architecture, data models, dependencies and performance limitations to identify which components can be retained, optimised, migrated or redesigned. A phased modernisation approach may reduce disruption and help prioritise investment.
They can consolidate relevant sources, standardise transformations and establish consistent data models and business definitions. Validation, reconciliation and governance are essential to ensure the resulting reports remain dependable.
They can provide a foundation for suitable analytics, data science and AI workloads by bringing relevant data and processing capabilities into a more integrated architecture. Successful implementation still depends on data quality, access controls, governance, workload design and the specific AI use case.
Yes. We start from what you already have, review how it is used and recommend platform improvements that build on existing investments where that makes sense.
Most work can be planned to limit disruption. Where changes need a maintenance window or cutover, we agree timing, rollback options and communication with your team in advance.
Start with a conversation about your current environment, priorities and constraints. We then recommend a sensible first step, such as an assessment or scoping session, before any wider work is agreed.
Contact us
Build a Data Platform That Supports Better Decisions
If fragmented data, inconsistent reporting and legacy warehouse constraints are limiting your analytics capabilities, NCS London can help you define a practical path forward.
Assess your current architecture, compare suitable platform options and build a data foundation aligned with your business objectives.
Prefer to talk? Call us on 020 3326 7268