Organisations consolidating reporting data
Create one governed analytical foundation across applications, finance, sales, operations, customers, and external sources.
Warehouse architecture, dimensional and domain modelling, migration, performance, and reporting foundations
Data warehousing
Rokad designs, builds, migrates, and optimises data warehouses that provide governed historical information, consistent metrics, and reliable analytical performance.
Designed for / 01
A focused delivery model for the organisations that need it.
A useful warehouse organises data around decisions, history, business meaning, and repeatable consumption. Rokad builds ingestion, staging, transformation, dimensional or domain models, semantic layers, quality, performance, security, migration, and reporting foundations.
Teams modernising a legacy warehouse
Improve reliability, deployment, testing, performance, cost, documentation, lineage, and cloud integration.
Companies replacing spreadsheet reporting
Establish controlled historical models and metric definitions that support dashboards and self-service analysis.
Challenges / 02
The problems this service is built to solve.
The warehouse mirrors source systems rather than business questions
Consumers must reconstruct relationships, history, status, and metric logic independently in every report.
Historical changes are lost or misrepresented
Customer, product, organisation, pricing, and operational attributes change without explicit history and effective-date modelling.
Performance improvements create uncontrolled complexity
Copies, extracts, aggregates, materialisations, and caches proliferate without ownership, lineage, or lifecycle.
Capabilities / 03
What Rokad can deliver.
01
Warehouse requirements, source, consumer, history, and metric assessment
02
Cloud, on-premises, columnar, lakehouse, and hybrid warehouse architecture
03
Staging, integration, dimensional, Data Vault, domain, and semantic modelling
04
Incremental loads, CDC, history, late data, reconciliation, and backfills
05
Transformation tests, documentation, lineage, quality, and governance
06
Partitioning, clustering, materialisation, workload, query, and cost optimisation
07
Warehouse migration, reporting continuity, security, and managed operation
Platform expertise
Platform-specific implementation services.
01
Google BigQuery engineering services
Rokad designs, builds, migrates, governs, optimises, and operates BigQuery analytical platforms and data warehouses on Google Cloud.
02
Amazon Redshift engineering services
Rokad designs, builds, migrates, governs, optimises, and operates Amazon Redshift analytical warehouses on AWS.
Solution components / 04
The system behind the visible product.
Source and staging layer
Faithful ingestion, timestamps, history, raw evidence, schema changes, quality, and replay capability.
Integrated business model
Conformed entities, facts, dimensions, domains, relationships, effective dates, and shared business definitions.
Serving and semantic layer
Metrics, aggregates, marts, permissions, performance, discoverability, documentation, and BI consumption.
Warehouse operation
Loads, freshness, quality, incidents, performance, capacity, cost, access, schema changes, and lifecycle.
Use cases / 05
Where this capability creates practical leverage.
Enterprise reporting warehouse
Consolidate finance, customer, sales, product, operations, and service data into consistent historical models.
Cloud warehouse migration
Move schema, history, transformations, schedules, security, and reports with reconciliation and continuity controls.
Customer and product analytics foundation
Model journeys, cohorts, usage, revenue, retention, subscriptions, support, and product behaviour consistently.
Regulatory or audit reporting
Create traceable historical transformations, evidence, access, retention, reconciliation, and controlled report outputs.
Architecture and integration / 06
Designed to fit the wider technology environment.
History by requirement
Choose snapshot, event, slowly changing, transaction, or effective-dated patterns based on the questions and evidence required.
Business grain first
Define what one row represents, keys, events, measures, dimensions, and timing before designing tables or dashboards.
Reconciliation across layers
Compare source, staging, integrated, semantic, and report outputs so transformation errors are visible and explainable.
Quality and control / 07
Production requirements are part of the build.
Trust through contracts
Ownership, schemas, semantics, freshness, completeness, access, and failure expectations are explicit between producers and consumers.
Tested transformation
Pipelines and models include validation, reconciliation, lineage, observability, and controlled change before decision use.
Governed access
Identity, classification, least privilege, retention, masking, audit, and usage boundaries follow the sensitivity of the data.
Delivery / 08
A controlled path from requirement to operation.
Discover
Clarify the objective, users, systems, constraints, dependencies, risks, and measurable acceptance criteria.
Architect
Define the target design, interfaces, controls, migration or delivery sequence, and operating model.
Deliver and validate
Implement in controlled increments with testing, review, documentation, observability, and stakeholder validation.
Operate and improve
Establish ownership, service controls, measurement, support, and a prioritised improvement backlog.
Typical deliverables
Warehouse source, history, metric, consumer, and quality assessment
Target warehouse, model, security, performance, and migration architecture
Production ingestion, staging, transformation, and warehouse models
Semantic layers, marts, metrics, documentation, and BI interfaces
Tests, reconciliation, lineage, observability, and access controls
Migration, performance, cost, runbook, and operating documentation
Engagement models / 09
Use the delivery structure that matches the work.
Assessment and roadmap
A bounded evidence review, target direction, prioritised risks, and executable next-stage plan.
Fixed-scope delivery
A defined implementation, migration, prototype, procurement, or transformation outcome with acceptance criteria.
Embedded specialists
Specialists working alongside internal product, engineering, data, operations, security, or procurement teams.
Managed lifecycle
Ongoing ownership, maintenance, monitoring, supplier coordination, reliability, security, and improvement.
Related capabilities / 10
Continue through the wider product and technology system.
01
Analytics engineering
Build tested transformations and governed metric models inside the warehouse.
02
Business intelligence
Deliver dashboards and decision workflows on trusted warehouse models.
03
Data pipelines
Ingest and reconcile operational sources into the warehouse.
01
Cloud and DevOps
Cloud architecture, platforms, CI/CD, Kubernetes, security, reliability, and migration.
02
Technology consulting and research
Strategy, architecture, discovery, due diligence, feasibility, and market intelligence.
03
Managed technology services
Application, cloud, security, reliability, maintenance, and continuous engineering operations.
FAQ
Data warehousing
Scope, ownership, assumptions, delivery, security, and long-term operation are clarified before work begins.
01Should we use dimensional modelling or another approach?
Should we use dimensional modelling or another approach?
The choice depends on source volatility, history, governance, consumer needs, team skills, auditability, and platform. We may combine raw, integrated, domain, dimensional, and semantic layers.
02Can you migrate an existing warehouse without breaking reports?
Can you migrate an existing warehouse without breaking reports?
Yes. We inventory consumers, map schemas and logic, run systems in parallel, reconcile outputs, validate performance, and phase report migration with rollback options.
03How do you manage metric consistency?
How do you manage metric consistency?
We define grain, filters, timing, status, dimensions, ownership, tests, documentation, and semantic models so important metrics have controlled definitions.
04Can Rokad improve warehouse performance and cost?
Can Rokad improve warehouse performance and cost?
Yes. We analyse workload, scans, joins, partitions, clustering, materialisations, concurrency, caching, schedules, storage, retention, and ownership before optimisation.
Data engineering
Build a warehouse around historical truth and consistent decisions.
Rokad can define the model, migrate the data, establish quality and performance, and preserve reporting continuity.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.