Data Foundation
We architect and deploy data lakes, warehouses, and lakehouses on Azure, AWS, and GCP — engineered for the performance, sovereignty, and compliance requirements of GCC enterprises.
Our approach
DAI Consultancy designs and deploys cloud data platforms tailored to each organisation's regulatory landscape and business objectives. Whether the engagement calls for a centralised data warehouse on Snowflake, a multi-cloud lakehouse on Databricks, or a hybrid architecture spanning on-premises and public cloud, we begin every project with a governance-first assessment that maps data classification, access policies, and compliance requirements before a single resource is provisioned.
Our approach covers the full deployment lifecycle: reference architecture design, environment provisioning, network and identity configuration, data ingestion layer setup, and performance benchmarking. We work with Azure Synapse, Amazon Redshift, Google BigQuery, Snowflake, and Databricks to match each workload to the platform that delivers the best cost-performance ratio. Post-deployment, we establish monitoring dashboards and cost-optimisation guardrails so the platform remains performant and financially sustainable.
GCC enterprises operating across multiple jurisdictions benefit from our multi-region deployment patterns that satisfy data residency requirements while maintaining a unified analytical layer. The result is a cloud data platform that serves as a trusted, scalable foundation for every downstream initiative — from executive dashboards to real-time AI inference.
What's included
The blueprint behind the build — compute, storage, networking, and security layers, plus a phased migration plan with dependency mapping and rollback procedures.
Terraform, Bicep, or CloudFormation templates deployed for repeatable, auditable environment setup across development, staging, and production.
A provisioned, governed data platform on Azure, AWS, or GCP — landing zones, network, identity, and security configured and ready for workloads.
Source systems connected and landing data in the governed platform, with monitoring dashboards and cost guardrails live from day one.
Benchmarked query latency, throughput, and concurrency under realistic workloads, with reserved-capacity, auto-scaling, and tiering strategies that often reduce cloud spend by 20-40%, depending on workload mix.
Want to scope this for your organisation?
Discuss a Platform AssessmentRegional framework alignment
We map this service to the official data governance, privacy, security, sharing, and operating-model expectations that apply in each jurisdiction.
Background
Cloud data platforms form the backbone of any modern data strategy. A well-architected platform consolidates disparate data sources into a single, governed environment where analytics, machine learning, and generative AI workloads can operate at scale. For enterprises across the GCC, the choice of cloud infrastructure carries additional weight: data protection and data management regulations in Saudi Arabia (PDPL, NDMO frameworks), Qatar (PDPPL, QDKC), Oman (PDPL, MTCIT National Data Governance Framework), the UAE (Federal PDPL, plus the DIFC and ADGM free-zone laws), and Bahrain (PDPL) can affect data residency, access control, encryption, cross-border transfer, and governance design.
Use cases
Centralising transaction, risk, and customer data from legacy core banking systems into a governed cloud warehouse for real-time regulatory reporting.
Building petabyte-scale data lakes for seismic, sensor, and operational data with zone-based access controls that satisfy national data sovereignty rules.
Deploying sovereign-cloud or hybrid-cloud platforms that keep citizen data within national borders while enabling cross-ministry analytics.
Creating real-time streaming architectures for network telemetry, CDR processing, and customer experience analytics at scale.
Related services
FAQ
A cloud data platform is a managed environment — typically a data lake, warehouse, or lakehouse — hosted on a public or hybrid cloud. GCC enterprises benefit because cloud platforms offer elastic scale, built-in disaster recovery, and the ability to enforce data residency policies required by regional regulations such as Saudi Arabia's PDPL, Qatar's PDPPL, and Oman's PDPL.
DAI Consultancy is cloud-agnostic and works across Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). We also deploy on specialised platforms including Snowflake and Databricks, selecting the best fit based on workload requirements and compliance constraints.
Timelines depend on scope and complexity. A typical deployment often ranges from 8 to 16 weeks — a single-cloud warehouse with standard ingestion pipelines is typically operational in around 8 weeks, while a multi-region lakehouse with advanced governance and real-time streaming may take 12-16 weeks.
We map every data asset to its classification and residency requirements before selecting cloud regions. Infrastructure-as-code templates enforce that resources are provisioned only in approved regions, and network policies prevent data from leaving designated boundaries.
Let’s discuss how our governance-first approach to cloud data platforms can accelerate your data and AI initiatives.
