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Modern Data Platforms

One governed platform for analytics and AI, designed for your scale and your region.

Platform choices made today decide what analytics and AI can do for years: cost, performance, lock-in, residency and how easily data can be shared. The right answer depends on your workloads, skills and regulatory context, not on the vendor with the best demo.

We design vendor-neutral, architecture-led platforms, including options that keep data and processing within the UAE or Saudi Arabia where required.

The problem

What usually gets in the way

  • Data is spread across legacy warehouses, lakes and departmental databases.
  • Cloud costs grow faster than usage.
  • Residency rules make it unclear which services and regions can be used.
  • The platform serves reporting but cannot support AI workloads.

What we deliver

Capabilities

01

Platform strategy

Warehouse, lakehouse or mesh, cloud or hybrid, chosen against your workloads and constraints.

02

Vendor-neutral selection

Structured evaluation and proofs of concept across leading platforms.

03

Architecture and landing zone

Secure, governed foundations with identity, networking and cost controls.

04

Migration

Phased migration from legacy platforms with reconciliation and parallel running.

05

Data products

Domain-owned, documented datasets designed for reuse.

06

FinOps and operations

Cost visibility, workload tuning and platform operations.

Delivered by

Services involved

The service lines combined to deliver this solution.

How it fits together

Reference architecture

A vendor-neutral outline. Components are selected for your workloads, skills and regulatory context.

  1. ConsumptionBI and reportingData science and AIData sharing and APIs
  2. Data productsDomain data productsSemantic layerFeature store
  3. PlatformLakehouse or warehouseOpen table formatsStreaming and batch
  4. FoundationCloud or hybrid landing zoneIdentity and securityResidency controls

How we engage

Start small, prove value, scale

  1. 01Assess

    3–6 weeks

    Current-state review, target architecture and platform options.

    • Architecture options
    • Vendor shortlist
    • Cost and migration estimate

  2. 02Pilot

    8–12 weeks

    Landing zone plus first data products on the target platform.

    • Working platform
    • First use cases live
    • Validated costs

  3. 03Scale

    Ongoing

    Migrate workloads and onboard domains in waves.

    • Migration waves
    • Decommissioned legacy
    • FinOps reporting

Outcomes

What we measure

  • Platform cost per workload
  • Time to deliver a new data product
  • Share of data on governed platform
  • Legacy systems decommissioned
  • Query performance and availability

Where it applies

Example use cases

  • Telecommunications

    Network and customer data on a single lakehouse.

  • Government

    Sovereign data platform with in-country processing.

  • Banking

    Warehouse modernisation with regulatory reporting migration.

  • Retail

    Real-time inventory and sales data platform.

Accelerators

Reusable assets we are building

Delivered today as services. These accelerators are being developed from that work to shorten future engagements.

In development

Platform selection framework

Weighted criteria and test scripts for vendor-neutral evaluation.

In development

Landing zone blueprint

Reusable secure foundation patterns, including residency options for the UAE and KSA.

Technology

Platforms we design around

Vendor-neutral: examples of platforms commonly used for this solution. Names identify technologies only and do not imply a partnership.

  • Databricks
  • Snowflake
  • Microsoft Fabric
  • Google BigQuery
  • Amazon Web Services
  • Microsoft Azure
  • Oracle Cloud Infrastructure
  • Apache Iceberg
See the full technology ecosystem →

Insights

Further reading

Warehouse, Lake, Lakehouse or Mesh?

The question is usually framed as a choice between four architectures. It is really two separate decisions: where data is stored and processed, and who owns and governs it. Most enterprises need an answer to both.

Read →

Snowflake, Databricks and Data Marts

The table-format war between Snowflake and Databricks is effectively over. The contest has moved up the stack, to where your business definitions, security policies and AI features live. That is where the architecture decision now sits.

Read →

AI Data Residency in KSA and the UAE

Hosted in the region is not the same as processed in the region. For most frontier models, a Gulf deployment today stores your data locally and sends your prompts abroad, so residency has to be decided per workload and per feature.

Read →

Vendor-Neutral Technology Selection

Vendor neutrality does not mean avoiding vendors or running everything on two clouds. It means pricing the exit before you sign, and scoring it alongside features, cost and contract terms.

Read →

Talk to us about Modern Data Platforms

Start with a short assessment: we will rank the opportunities, check readiness and agree how success will be measured.

Start a conversation

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