Data & AI Practice
Data Platform Engineering
A data platform is the shared foundation that storage, processing, governance and access controls sit on, so that teams across the enterprise work from one trusted source rather than many conflicting copies. Erpvora designs and engineers these platforms to balance openness for innovation with the control that regulated enterprises require.
Design and build a unified, governed data platform that serves analytics, data science and operational use cases from one trusted foundation.
The business challenge
When every team builds its own stack, the enterprise ends up with duplicated data, inconsistent definitions and no single place to apply security or governance. The same metric means different things in different reports, and nobody can answer who can see what.
A platform without strong foundations fails the other way too. Heavy central control slows teams down, shadow solutions appear, and adoption stalls. The challenge is to provide a platform that is governed and self service at the same time, with guardrails rather than gates.
Our approach
We design around clear separation of storage, compute and access, with a governance layer that applies security, cataloguing and lineage consistently. Teams get self service access within policy, so they can move fast without working around central controls.
We build the platform as a product with a roadmap, documented standards and an onboarding path for new teams and datasets. Shared services such as ingestion, quality and access provisioning are reusable, so each new use case starts further ahead than the last.
Capabilities
- Platform architecture spanning storage, compute, governance and access
- Shared ingestion, transformation and quality services
- Data cataloguing, lineage and metadata management
- Role based and attribute based access control within policy
- Self service provisioning for teams and workloads
- Cost allocation, monitoring and platform operations
How we deliver
- 01
Operating model
We agree how the platform will be governed, who owns what, and how teams request access and onboard data.
- 02
Foundation design
We architect storage, compute, security and the governance layer so controls apply consistently across workloads.
- 03
Shared services
We build reusable ingestion, quality and access services that every use case can draw on.
- 04
Onboard teams
We bring the first teams and datasets onto the platform, proving the self service path and refining guardrails.
- 05
Operate and scale
We monitor usage and cost, publish standards and extend the platform as new domains come aboard.
Typical use cases
- Replacing fragmented team stacks with one governed foundation
- Providing self service analytics within enforced security policy
- Supporting analytics, data science and operational workloads on shared infrastructure
- Implementing a data mesh or domain oriented ownership model
- Centralizing cataloguing and lineage across the enterprise
- Allocating and controlling data platform cost by team
Business impact
- One trusted foundation instead of conflicting copies
- Consistent security, governance and lineage across workloads
- Faster delivery through reusable shared services
- Self service speed without loss of control
- Transparent cost allocation across consuming teams
- A platform that scales as new domains join
Frequently asked questions
Is this a data mesh or a central platform?
It can be either or a blend. We design the operating model around your organization, often a central platform providing shared services with domain teams owning their own data products.
How do you avoid the platform becoming a bottleneck?
By providing self service within guardrails. Teams provision access and onboard data through automated, policy enforced paths rather than queuing for a central team.
Do we need a lake, a warehouse or both?
Most platforms combine open storage for raw and engineered data with warehouse style serving for curated analytics. We design the mix around your workloads.
How is cost kept under control?
We separate storage and compute, allocate cost to consuming teams, and monitor usage so spend is visible and owned rather than pooled and opaque.