Capability 01
Data Platform Architecture
Assess your data landscape and design a target platform architecture aligned with reporting and AI needs.
- Data landscape assessment
- Target architecture design
- Platform selection
- Platform roadmap
data ai
Trusted data, ready for analytics and AI.
We design and build the platforms and pipelines that bring your data together reliably. Your teams get consistent, governed data they can use for reporting, analysis and AI.
Overview
Data Platforms & Engineering
Data Platforms & Engineering helps organizations bring data together from across the business and turn it into a trusted, governed foundation for analytics and AI.
We assess your data landscape, design the right platform and build automated pipelines that deliver consistent, quality-checked data. Legacy warehouses are modernized in phases, and governance defines ownership, quality and access from the start.
The result is reliable data your teams can trust for reporting, analysis and AI.
Who this is for
- Data and IT leaders with data spread across many systems and spreadsheets
- Organizations whose reports disagree because data is defined differently
- Teams running ageing data warehouses that are slow or costly to change
- Businesses preparing their data foundations for AI initiatives
What gets in the way
Challenges this service addresses
Data spread across systems
Business data sits in separate applications, databases and spreadsheets, so building a complete view takes manual effort.
Low trust in data
Different definitions, duplicates and quality issues mean reports disagree and users question the numbers.
Fragile data pipelines
Pipelines fail without warning, data arrives late and fixing issues depends on a few individuals.
Legacy platforms limiting growth
Ageing warehouses struggle with new data volumes and types, and changes take too long or cost too much.
Unclear ownership and access
Nobody is clearly responsible for key data, and access to sensitive information is not consistently controlled.
What we do
Capabilities
Each capability can be scoped on its own or combined into a larger programme of work.
Capability 02
Modern Data Platform Implementation
Build cloud data warehouses and lakehouse platforms that scale with data volumes and business demand.
- Lakehouse and warehouse setup
- Data modeling
- Storage and compute design
- Platform automation
Capability 03
Data Pipelines & Integration
Build automated, monitored pipelines that ingest and transform data from applications, files and streams.
- Batch and real-time ingestion
- Data transformation
- Pipeline orchestration
- Pipeline monitoring and alerts
Capability 04
Data Warehouse Modernization
Migrate legacy warehouses and reporting databases to modern platforms with validated data and minimal disruption.
- Legacy platform assessment
- Data and logic migration
- Reconciliation testing
- Phased cutover
Capability 05
Data Governance & Quality
Implement quality rules, catalogs, lineage and access controls so data is trusted, documented and protected.
- Stewardship workflows
- Quality rules and monitoring
- Data catalog and lineage
- Access and privacy controls
What changes
Business outcomes
- A single, trusted source for business data
- Reliable data delivered on time for reporting
- Foundations ready for analytics and AI
- Lower effort to add new data sources
- Controlled access to sensitive data
How we work
Our approach
- 1 Assess Review data sources, platforms, quality and priority use cases, and agree the target scope.
- 2 Design Define the architecture, data models, pipelines and governance, starting with high-value data domains.
- 3 Build Implement the platform and pipelines as code, delivering data domains in iterations.
- 4 Test Reconcile data against source systems and validate quality, performance and access controls.
- 5 Launch Move to production with monitoring and documentation, then hand over to your team or our support service.
Why Hinkor
How we back that up
Use cases drive the design
We build data domains in the order your reporting and AI priorities need them, not all at once.
Quality checked at every stage
Data is reconciled with source systems and monitored continuously, so issues are found before users see them.
Platform delivered as code
Pipelines, models and configuration are version-controlled, making the platform repeatable and easy to extend.
Connected to analytics and AI
Our analytics and AI teams use the same platform, so data is designed for how it will actually be used.
Engagement options
Starting points
Start with a defined piece of work. If the scope is not clear yet, begin with an advisory engagement and decide the delivery shape afterwards.
- Advisory Engagements Time-boxed expert advice, assessments and roadmaps that help you make confident decisions before you invest.
- Fixed-Scope Projects Defined scope, deliverables and timeline, delivered end to end by a Hinkor team accountable for the result.
- Dedicated Teams & Team Extension Hinkor engineers and specialists who join your team, follow your processes and work toward your roadmap.
Data Platform Assessment
Review your data sources, platforms and quality, and receive a target architecture and prioritized roadmap.
Data Domain Pilot
Build one priority data domain end to end to prove the platform, pipelines and governance approach.
Related services
Often delivered together
- Analytics & BI Turn trusted data into dashboards, reports and insights that help people make better decisions every day.
- AI, ML & Generative AI Design, build and run machine learning models, AI assistants and agents that solve defined business problems safely.
- Integration & API Engineering Connect applications, partners and data through secure APIs and integrations that keep business processes running.
- Cloud Migration & Modernization Plan and migrate workloads to the cloud on a secure, governed foundation, modernizing platforms where it adds value.
FAQ
Questions about Data Platforms & Engineering
If your question is not here, ask it directly.
Ask a direct questionWhich data platform is right for us?
It depends on your data volumes, use cases, existing cloud and skills. We compare platforms such as Databricks, Snowflake and Microsoft Fabric against your requirements and recommend the best fit.
How is this different from IoT & Embedded Engineering?
IoT & Embedded Engineering builds the devices, firmware and connectivity that collect and send data. Data Platforms & Engineering stores, combines and prepares that data for analytics, reporting and AI.
How is this different from Analytics & BI?
Data Platforms & Engineering builds the platforms and pipelines that deliver trusted data. Analytics & BI turns that data into dashboards, reports and insights that people use to make decisions.
How is this different from Integration & API Engineering?
Integration & API Engineering connects applications so they exchange data in real time to run business processes. Data Platforms & Engineering collects data from those applications for analytics, reporting and AI.
Who maintains the platform after launch?
Each platform is handed over with monitoring, documentation and runbooks. Your team can operate it, or Hinkor can provide ongoing pipeline support and improvements as data sources change.
Next step
Ready to build trusted data foundations?
Talk to our data team about your data sources, platforms and priorities.