Organisations building a shared data foundation
Consolidate fragmented pipelines, storage, warehouses, tooling, permissions, and operational practices.
Cloud data foundations, lakehouse, governance, access, observability, developer experience, and operations
Data platform engineering
Rokad designs and builds governed data platforms that provide reliable ingestion, storage, compute, transformation, discovery, access, quality, and operations.
Designed for / 01
A focused delivery model for the organisations that need it.
A data platform is the shared operating system for data producers, engineers, analysts, applications, and AI teams. Rokad designs cloud and hybrid platforms around workload requirements, ownership, access, data products, interoperability, reliability, cost, and developer experience.
Data teams scaling beyond project-specific infrastructure
Provide reusable ingestion, transformation, quality, catalogue, access, and observability capabilities.
Companies enabling analytics and AI together
Support reporting, exploration, machine learning, retrieval, applications, and governed data exchange on one coherent foundation.
Challenges / 02
The problems this service is built to solve.
Every team assembles its own data stack
Tools, storage, schemas, security, quality, deployment, and monitoring diverge, increasing cost and reducing trust.
The platform stores data but does not make it usable
Ownership, semantics, documentation, quality, lineage, access, and consumer interfaces remain unclear.
Cloud data cost grows without accountability
Compute, storage, movement, retention, inefficient queries, duplicate copies, and idle resources lack measurement and ownership.
Capabilities / 03
What Rokad can deliver.
01
Data-platform strategy, workload assessment, and target architecture
02
Lake, lakehouse, warehouse, object, stream, catalogue, and metadata foundations
03
Ingestion, orchestration, transformation, notebook, feature, and serving services
04
Identity, access, classification, encryption, masking, retention, and audit
05
Data quality, contracts, lineage, observability, incident, and ownership systems
06
Developer environments, templates, CI/CD, infrastructure code, and self-service
07
Performance, capacity, cost, backup, recovery, governance, and managed operation
Platform expertise
Platform-specific implementation services.
01
Snowflake data platform services
Rokad designs, builds, migrates, governs, optimises, and operates Snowflake data platforms for analytics, data products, applications, and AI workloads.
02
Databricks lakehouse engineering services
Rokad designs, builds, migrates, governs, and operates Databricks lakehouse platforms across data engineering, analytics, machine learning, and AI.
03
Microsoft Fabric implementation services
Rokad designs, implements, migrates, governs, and operates Microsoft Fabric data platforms across engineering, warehousing, real-time analytics, and Power BI.
Solution components / 04
The system behind the visible product.
Storage and compute plane
Object, warehouse, lakehouse, stream, query, processing, isolation, scale, performance, and lifecycle.
Data control plane
Catalogue, metadata, lineage, ownership, classification, access, policy, quality, contracts, and audit.
Engineering platform
Repositories, environments, orchestration, tests, deployment, templates, observability, documentation, and self-service.
Consumer interfaces
Tables, semantic models, APIs, files, features, streams, dashboards, notebooks, search, and data products.
Use cases / 05
Where this capability creates practical leverage.
Cloud data-platform implementation
Create secure accounts, storage, compute, orchestration, governance, access, observability, and delivery foundations.
Lakehouse or warehouse modernisation
Move from fragmented or legacy systems to a more reliable, interoperable, governed, and cost-controlled platform.
Enterprise data-product platform
Enable domains to publish owned, documented, tested, discoverable, and supported datasets and interfaces.
AI-ready data foundation
Support training, inference, features, retrieval, evaluation, lineage, and governed access to operational evidence.
Architecture and integration / 06
Designed to fit the wider technology environment.
Workload-driven platform choice
Select warehouse, lakehouse, streaming, query, catalogue, and orchestration capabilities from actual latency, scale, skill, and governance needs.
Separation with interoperability
Separate storage, compute, metadata, orchestration, and serving where useful while preserving open contracts and portable data.
Platform as a product
Define users, supported paths, service levels, documentation, telemetry, cost, support, roadmap, and deprecation for platform capabilities.
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
Data-platform workload, tooling, governance, and cost assessment
Target platform, storage, compute, metadata, security, and operating architecture
Infrastructure code, environments, orchestration, access, and shared services
Catalogue, lineage, quality, observability, policy, and ownership implementation
Developer templates, CI/CD, self-service, cost, and support controls
Runbooks, platform documentation, governance, and handover plan
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
Data pipelines
Connect sources and consumers through reliable batch and streaming delivery.
02
Data warehousing
Create governed analytical storage and models on the platform.
03
Analytics engineering
Build tested transformations, semantic models, and metric definitions.
01
Cloud and DevOps
Cloud architecture, platforms, CI/CD, Kubernetes, security, reliability, and migration.
02
AI development
Governed AI applications, agents, retrieval, models, evaluation, and intelligent automation.
03
Technology consulting and research
Strategy, architecture, discovery, due diligence, feasibility, and market intelligence.
FAQ
Data platform engineering
Scope, ownership, assumptions, delivery, security, and long-term operation are clarified before work begins.
01Do we need a data lakehouse?
Do we need a data lakehouse?
Not automatically. We compare warehouse, lakehouse, lake, operational store, streaming, and managed-service options against workloads, skills, interoperability, governance, performance, and cost.
02Can Rokad build on our existing cloud?
Can Rokad build on our existing cloud?
Yes. We can use current cloud, identity, network, security, CI/CD, and data services where they meet the target requirements.
03How do you prevent a data platform becoming a dumping ground?
How do you prevent a data platform becoming a dumping ground?
We define ownership, contracts, lifecycle, quality, catalogue, access, retention, cost, and consumer interfaces rather than measuring success by data volume alone.
04Can the platform support multiple business units?
Can the platform support multiple business units?
Yes. We can design domain, account, project, workspace, catalogue, access, compute, cost, and service boundaries for shared governance with delegated ownership.
Data engineering
Create a data platform people can trust, understand, and operate.
Rokad can assess workloads and tooling, define the target platform, implement shared capabilities, and establish long-term operation.
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.