---
title: "The warehouse behind all your BI."
url: "https://www.omnicliq.com/en/business-intelligence/data-infrastructure/"
description: "We design your cloud data warehouse so BI scales and doesn't break under its own complexity."
category: "business-intelligence"
locale: "en"
---

# The warehouse behind all your BI.

> We design your cloud data warehouse so BI scales and doesn't break under its own complexity.

## A warehouse built for decisions.

Most data infrastructure fails quietly. Queries slow down, costs climb unchecked, dashboards break when something changes in the sources. Almost always the root is the original architecture, built when there was less data and a smaller team.

We build the warehouse around the questions it has to answer, not as a copy of the sources. Organisation choices rest on real queries, not defaults, and cost and access are controlled from the start, so the infrastructure grows without becoming a problem.

The scope usually covers setting up or migrating the warehouse, the data model, cost control, security and data loading. Data engineering, dashboards and data activation all rest on top of it.

## What changes for you.

- **Cloud warehouse** — A design matched to the workloads that will run: schema, partitioning and clustering chosen deliberately, not left on defaults.
- **Data model** — Dimensional modelling and marts built around real business questions, with shared reporting logic across tools and teams.
- **Cost control** — Query cost monitoring, limits and quotas that keep the warehouse from becoming an uncontrolled expense.
- **Access and security** — Role-based access, row-level security where needed and audit logs. Sensitive data is treated as sensitive from the design stage.
- **Data loading** — Pipelines from ads, analytics, CRM, e-shop and finance, with incremental syncs, backfill and checks that stop silent data corruption.
- **Migration experience** — Warehouse migrations are rarely simple. We have done them from legacy analytics platforms, other clouds and custom systems, with a tested method.
## How we work.

1. **Discovery** — We map data sources, reporting needs and the current architecture, and define what the warehouse must support.
2. **Architecture** — We document the warehouse, schema and data model, with a stated reason for each cost and access decision, not defaults.
3. **Delivery** — We set up or migrate the warehouse, the loading flows and access controls. The base is ready for data engineering and reporting.
4. **Operations** — We monitor cost and performance. We hand over to your team with documentation, or keep running it as part of the engagement.
## FAQ

### Which cloud warehouse do you recommend?
It depends on your current stack, data volume and the cloud services you already use. We work on the main cloud warehouses and recommend on fit. If you are already on a specific cloud, the native option is usually the right one.

### Does it make sense if we already have a warehouse?
Often yes, because most were built when the company was smaller. We usually find schemas that push cost up for no reason, models that force analysts to rebuild the same logic and cost control gaps that show up as budget surprises.

### Do you migrate from legacy platforms?
Yes, from legacy analytics platforms, other cloud warehouses and home-grown databases. The method is tested, and the execution depends on the source system and how much downtime you can tolerate at cutover.

### How do you handle security and GDPR?
Role-based access, row-level security where needed, audit logs and policies for personal data are built into the architecture from the start, not added later.

### Real-time or batch loading?
Most reporting is well served by hourly or daily loading. We set up real time only where it drives real-time decisions, such as personalisation or fulfilment operations. We don't claim everything must be real time when it doesn't.