Consumption-based pricing
Pay only for storage and compute you use — no upfront investment, no tuning per warehouse.
Technology Partner · Snowflake
The leading cloud data platform that combines data warehouse and data lake into one lakehouse architecture. cimt delivers lakehouse design, Data Vault 2.0 modelling, migration and managed services.
Snowflake is a cloud-native data platform that combines the benefits of a data warehouse (ACID, governance, SQL performance) with those of a data lake (cost, flexibility, unstructured data) in a single lakehouse architecture. cimt positions Snowflake as the storage- and-compute layer in our reference architecture, combined with Data Vault 2.0 for modelling flexibility and Qlik Talend Cloud for data integration. Multi-cloud (AWS / Azure / GCP), consumption-based pricing, and near-zero maintenance — we implement, model, migrate and manage end-to-end.
Core benefits
Four properties that set Snowflake apart from legacy data warehouses and generic cloud database instances.
Pay only for storage and compute you use — no upfront investment, no tuning per warehouse.
Run on AWS, Azure or GCP — replicate data across clouds without vendor lock-in.
No indexes, no vacuum, no tuning — Snowflake manages the infrastructure.
Share data safely with partners and customers via Marketplace and Private Data Exchange — no copies.
Data Vault 2.0
We implement Data Vault 2.0 as the modelling methodology on Snowflake — Hubs, Links and Satellites that admit new sources without breaking existing models, with full audit trail.
Standardised modelling structure that admits new sources without breaking risk.
Every change traceable — essential for compliance and governance.
Inherently parallelisable, optimal for Snowflake multi-cluster compute.
Integrate ERP, CRM, IoT, APIs and flat files into one uniform model.
Our Data Vault implementations are supported by erwin Data Modeler for visual model design and Qlik Talend Cloud for automated pipeline generation.
cimt Snowflake services
| Service | Description | More |
|---|---|---|
| Architecture & design | Lakehouse reference architecture, Data Vault 2.0 modelling, zone layout (Raw / Curated / Consumption) | View → |
| Data integration | ETL/ELT pipelines via Qlik Talend Cloud, CDC, API integration, Snowpipe streaming | View → |
| Migration | Migration of on-premises data warehouses (Oracle, SQL Server, Teradata) to Snowflake — parallel run, validation, rollback | View → |
| Analytics & BI | Qlik Sense dashboards on Snowflake data, direct query and import mode | View → |
| Managed Services | Ongoing management, monitoring, cost optimisation and warehouse tuning | View → |
Snowflake for your organisation
In a first conversation we look at your current data warehouse situation and which Snowflake deployment model fits — including a first cost indication.
Frequently asked
Snowflake uses consumption-based pricing: pay separately for storage (per TB/month) and compute (per credit). No upfront cost or minimum contract period. Cost depends on your data volume, user count and query complexity. cimt helps optimise through proper warehouse sizing, auto-suspend and resource monitoring — we often save 20-40% on the initial Snowflake bill.
Yes. cimt has extensive experience with migrations from Oracle, SQL Server, Teradata and other platforms. Our approach covers schema conversion, data movement, query rewrite and parallel validation. We use Data Vault 2.0 to lay a future-proof modelling layer during the migration — not just "as-is" relocation but modernising as we move.
Both are powerful cloud data platforms with different focus. Snowflake excels at SQL-based analytics, data warehousing and data sharing. Databricks is stronger in data engineering with Spark and ML workloads. cimt picks Snowflake when the primary need is analytics and BI, and when a SQL-first approach fits your organisation and team skills.
Not strictly — star schemas and dimensional models work fine on Snowflake too. Data Vault 2.0 pays back especially with multiple source systems, long historical retention, compliance requirements (audit trail) and teams that want to develop in parallel. For simple use cases we often recommend star schema; for enterprise scope almost always Data Vault.