Data Platform Architecture
Warehouse and lakehouse design that stays affordable as you grow.
Cloud data platforms on AWS, GCP, or Azure — architected for governance, predictable cost, and the volumes you will reach in two years rather than the ones you have today.
Architecture Is Mostly About Cost You Cannot See Yet
Cloud data platforms rarely fail on capability. They fail on economics — a design that is comfortable at ten gigabytes becomes eye-watering at ten terabytes, and by then the architecture is load-bearing and expensive to change.
We model running cost as part of the design, not after it. Partitioning strategy, storage tiers, compute isolation, and materialisation choices all get decided with a projected bill attached, based on your realistic growth rather than an optimistic one.
We are equally direct about when you do not need a platform rebuild. Plenty of teams arrive convinced they need a lakehouse when a well-modelled Postgres would serve them for another two years. Migrating early is an expensive way to solve a problem you do not yet have.
Service Inclusions
Warehouse & Lakehouse Design
Snowflake, BigQuery, Databricks, or Redshift — selected on your workload, team, and existing commitments.
Cost Modelling Up Front
Projected storage and compute spend at current and future volumes, so budget conversations happen before the build.
Phased Migration
Parallel running with reconciliation, so you can verify the new platform before decommissioning the old one.
Governance by Design
Access control, data classification, and audit logging built into the architecture rather than retrofitted under pressure.
Compute Isolation
Workload separation so a heavy analytics query cannot degrade production pipelines or ETL windows.
Decision Records
Every significant architectural choice documented with its rationale and the alternatives rejected.
A Process Built for Clarity
No black boxes. No surprise invoices. Every project at Mornis Global follows a disciplined four-phase process designed to reduce risk and maximise value at every stage.
Current State & Volumes
Existing systems mapped, data volumes and growth rates measured, actual bottlenecks identified.
Requirements & Constraints
Latency needs, compliance obligations, team capability, and budget ceiling documented as design constraints.
Architecture & Costing
Target design with modelled running costs at projected volumes, plus rejected alternatives and why.
Foundation Build
Core platform provisioned as infrastructure-as-code with security and access controls from the start.
Phased Migration
Workloads moved incrementally with parallel running and reconciliation at each stage.
Optimisation & Handover
Cost tuning against real usage, runbooks, and enablement for your team.
The Tech Stack
We select technologies based on performance, scalability, and long-term maintainability, not trends.
Snowflake
Specialized implementation of Snowflake in the Warehouse space.
Databricks
Specialized implementation of Databricks in the Lakehouse space.
BigQuery
Specialized implementation of BigQuery in the Warehouse space.
Terraform
Specialized implementation of Terraform in the Infrastructure as Code space.
Apache Iceberg
Specialized implementation of Apache Iceberg in the Table Format space.
AWS / GCP / Azure
Specialized implementation of AWS / GCP / Azure in the Cloud space.
Real-World Impact
Regional Logistics Operator
The Challenge
“A legacy on-premise warehouse could no longer complete overnight ETL within its window, and analytics queries were routinely blocking operational loads.”
The Solution
We designed a cloud lakehouse with isolated compute for analytics and pipelines, modelled costs at three-year projected volume, and migrated in phases with parallel reconciliation.
Key Performance Indicators
Common Inquiries
Everything you need to know about our specialized services.
Will Your Platform Survive Your Growth?
Tell us your current volumes and where they are heading. We will model what your architecture costs at that scale.