Agile & incremental
Add new sources without breaking existing models — pipelines keep running during expansion.
Service · Architecture & Lakehouse
A future-proof data architecture is the basis for every data initiative. From lakehouse design to cloud migration: cimt designs and implements platforms that are scalable, manageable and ready for AI.
A modern data architecture separates storage from compute, combines the flexibility of a data lake with the reliability of a data warehouse, and provides a scalable foundation for operational and analytical workloads alike. cimt designs this architecture as a lakehouse on Snowflake, modelled with Data Vault 2.0 for reusability and auditability, with erwin Data Modeler as the modelling tool. Deployment is tuned to your reality: cloud-native, hybrid or private cloud, based on data classification, compliance requirements and existing infrastructure.
Lakehouse architecture
The lakehouse paradigm combines the best of a data lake (flexibility, cost) with a data warehouse (ACID, governance, performance). As Snowflake partner we design architectures that are production-ready from day one.
| Architecture layer | Function | Snowflake capability |
|---|---|---|
| Ingest & Streaming | Loading data from sources (batch & real-time) | Snowpipe, Kafka connector, Dynamic Tables |
| Storage & Raw Zone | Storing unprocessed data, schema-on-read | Internal/External Stages, Iceberg Tables |
| Transformation | Modelling, cleansing, enriching data | Snowpark, dbt, Qlik Talend Cloud |
| Serving & Analytics | Making data available for BI, AI, apps | Virtual Warehouses, Data Sharing, Cortex AI |
| Governance & Security | Access control, lineage, compliance | Horizon, RBAC, Dynamic Data Masking, Lineage |
Modelling
For enterprise environments where sources change, history must be retained and auditability is required. We design Data Vault models with erwin Data Modeler for version control and automatic DDL generation.
Add new sources without breaking existing models — pipelines keep running during expansion.
Every change is traceable back to source, time and load process — essential for EU AI Act and GDPR.
Teams work independently on hubs, links and satellites without blocking each other.
Optimally tuned to Snowflake's elastic compute — scales with workload.
Deployment
Not every organisation can or wants to move fully to public cloud. We design architectures that fit your data classification, compliance requirements and existing infrastructure.
Full Snowflake SaaS on AWS / Azure / GCP. Maximum scalability and minimal operational overhead.
Combines cloud compute with on-premises data sources. For organisations migrating step by step or where latency matters.
Snowflake in your own VPC or regional instance for data residency and compliance requirements.
Start with insight
In 2–3 weeks we map the architecture, dependencies and quick wins of your current platform — concrete and fact-based.
Frequently asked
A traditional data warehouse requires structured, schema-on-write data and is expensive to scale for large raw datasets. A data lake stores anything in cheap storage but lacks transaction guarantees and governance. A lakehouse combines both: cheap object storage below, with ACID transactions, governance and SQL performance above. Snowflake is one of the architectures that delivers this combination in a single platform.
Star schemas and 3NF work well for stable source systems and known reporting needs. Data Vault 2.0 is designed for enterprise reality: source systems change, new sources are added, history must be retained, audit trails are mandatory. The extra modelling complexity pays back in maintainability and extensibility over years.
We are a Snowflake partner with deep expertise, but the lakehouse architecture and Data Vault modelling are portable. For organisations with existing Databricks or BigQuery investments we apply the same principles to those platforms. The choice depends on your existing cloud strategy, integration patterns and team expertise.
A working MVP (1–2 source domains, basic governance, first BI layer) typically stands in 3–4 months. Full enterprise implementation (10+ sources, complete Data Vault, governance, monitoring) is 9–12 months. We always work incrementally — first value within the quarter, not at the end.