Data Foundation
Cloud platforms built for enterprise scale
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
How we deliver cloud data platforms
DAI Consultancy designs and deploys cloud data platforms tailored to each organization's regulatory landscape and business objectives. Whether the engagement calls for a centralized 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-optimization guardrails so the platform remains performant and financially sustainable.
What's included
Deliverables
Reference Architecture & Migration Plan
The blueprint behind the build — compute, storage, networking, and security layers, plus a phased migration plan with dependency mapping and rollback procedures.
Infrastructure-as-Code Environments
Terraform, Bicep, or CloudFormation templates deployed for repeatable, auditable environment setup across development, staging, and production.
Production Platform Deployment
A provisioned, governed data platform on Azure, AWS, or GCP — landing zones, network, identity, and security configured and ready for workloads.
Data Ingestion Layer
Source systems connected and landing data in the governed platform, with monitoring dashboards and cost guardrails live from day one.
Performance & Cost Baseline
Benchmarked query latency, throughput, and concurrency under realistic workloads, with reserved-capacity, auto-scaling, and tiering strategies designed to reduce cloud spend for your workload mix.
Want to scope this for your organization?
Discuss a Platform AssessmentRegional framework alignment
Localized to GCC frameworks
We map this service to the official data governance, privacy, security, sharing, and operating-model expectations that apply in each jurisdiction.
NDMO data operations, architecture & classification; PDPL security/transfer
- Map data classes to approved regions and storage tiers
- Encryption, identity, and continuity evidence
Qatar National Data Standards — storage/operations, architecture & security
- Residency and exchange patterns mapped to QDKC domains
- Classification-to-storage matrix
MTCIT data operations, classification & architecture; PDPL transfers
- Storage, backup/restore, and disaster-recovery design
- Classification impact assessment and markers
Federal PDPL, Smart Data, Abu Dhabi storage/security, free zones
- Region selection across federal, emirate, and free-zone rules
- Smart Data classification and exchange
PDPL security/transfer; open-data hosting constraints
- Security and confidentiality controls for hosted data
- Cross-border transfer review
Background
Why it matters
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.
Use cases
Industries we serve
Financial Services
Centralizing transaction, risk, and customer data from legacy core banking systems into a governed cloud warehouse for real-time regulatory reporting.
Energy & Oil and Gas
Building petabyte-scale data lakes for seismic, sensor, and operational data with zone-based access controls that satisfy national data sovereignty rules.
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FAQ
Frequently asked questions
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 specialized 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.
Ready to get started?
Let’s discuss how our governance-first approach to cloud data platforms can accelerate your data and AI initiatives.