Data Platforms & Cloud

A warehouse wins when the questions are known; a lake wins when they are not. In most organisations Neuros does not separate them: raw data sits in cheap object storage while business rules are modelled in the warehouse layer with dbt. The distinction is about cost and schema flexibility, not fashion — as long as the raw layer stays queryable, both live on one platform.

Neuros makes cost visible first: every charge is tagged to a team, an environment and a workload. Then three items are examined — idle resources, unnecessary cross-region data transfer and over-provisioned storage. On the query side, partitioning, result caching and retention rules are applied. Reservations and savings plans come last, because locking in a wrongly sized system at a discount does not help.

You are ready if you can answer four questions: who owns this data, how often is it refreshed, can a wrong record be traced back to its source, and which fields hold personal data. Neuros runs those four as a maturity scan and closes the gaps before any model work, because a model trained on dirty data repeats the dirt at scale.

Neuros works on AWS, Azure and Google Cloud, and picks by three criteria: your existing enterprise agreements, the stack your team already knows, and the region the data must sit in. Because infrastructure is defined in Terraform, provider lock-in stays manageable — but managed services differ per provider, so portability is a cost item rather than a goal.

This is an architectural decision, taken up front. Neuros keeps data only in the region you specify; where it must stay in Türkiye, a local region or an on-premises installation is chosen. If personal data will cross a border, the transfer conditions of law 6698 and the required undertakings are written into the project documentation — it is not something resolved afterwards.