We design data warehouses and lakehouse environments built for reporting, analytics and the AI layer. We work with Snowflake, Microsoft Fabric and Databricks, among others. We build a consistent data layer with lineage, governance and data classification for GDPR, AI Act and the needs of regulated organisations. The goal is a single source of truth for data across the organisation.
We reduce inconsistencies between systems, so reports use the same data definitions regardless of source. This lets the board, finance, sales and operations work from a consistent picture of the data.
Self-service BI allows reports and analyses to be prepared faster without involving the IT team each time. The goal is on-demand access to data, rather than only after a manual close and merge of reports from multiple systems.
ML models and AI solutions require data of known quality, provenance and structure. A modern data stack reduces the risk of building AI on inconsistent, undocumented or hard-to-access data.
Cloud data warehouse architecture separates the cost of storing data from compute power and scales compute on demand. In many scenarios this reduces the cost of maintaining on-premise infrastructure and hardware.
We help choose the right data platform: Snowflake, Microsoft Fabric or Databricks. The decision is based on the client's ecosystem, team skills, security requirements, integrations and cost over a five-year horizon.
We prepare migrations from platforms such as Teradata, Oracle Exadata or SAP BW to a modern data stack. Scope includes TCO analysis, a migration plan, prioritisation of data domains and a dual-run phase.
We design data flows from SAP, Salesforce, Workday, SaaS applications, ERPs and custom sources. We use tools including Snowpipe, Fabric Data Pipelines, Fivetran, Stitch and integrations tailored to the client's needs.
We design data models and a semantic layer for self-service BI. Scope may include dbt, Snowflake dynamic tables, dimensional modelling and slowly changing dimensions.
We implement governance mechanisms, end-to-end lineage, a data dictionary and data classification for GDPR and audit requirements. We work with Snowflake Horizon, Microsoft Purview, Atlan and Collibra, among others.
We prepare the data layer for BI, analytics and AI/ML models. Scope may include Power BI, Tableau, Looker, Snowflake Cortex, Fabric Copilot and Databricks Mosaic AI.
A Modern Data Stack project starts with a data audit: what data exists, where it is stored, its quality, who is accountable for it, and which business processes rely on it. On this basis we prepare a migration map, MVP scope and priority data domains. The first warehouse or lakehouse for a single domain - for example sales, finance or operations - is typically launched in 10-14 weeks. We then extend the solution to further data domains, developing governance, data-quality monitoring, lineage and a data-ownership model in parallel. A full migration from an on-premise warehouse to a modern data stack usually takes 12-24 months and is carried out in stages, with a dual-run period for source systems and the new data platform.
Technology stack
The team's certifications in Snowflake, Microsoft Fabric, data engineering, data governance and enterprise systems confirm SNOK's readiness to deliver Modern Data Stack projects end to end.
Chemicals group after five acquisitions
Consolidation of data from five companies into Snowflake, combined with MDM master data and a data warehouse. The project delivered a single source of data for management reporting.
FMCG manufacturer
Migration of SAP BW to Snowflake, governance using Microsoft Purview, and BI reporting in Power BI.
Retail sector company
Microsoft Fabric as the foundation for analytics and AI: a lakehouse, a well-organised data layer and preparation of the environment for Copilot.
Snowflake is usually a good choice for multi-cloud organisations and high-volume analytics environments. Microsoft Fabric works well in organisations tightly integrated with Microsoft 365, Azure and Power BI. SNOK helps make the decision based on TCO, the technology ecosystem, team skills and security requirements.
SAP BW and BW/4HANA remain supported solutions, but many organisations are migrating part of their analytics layer to Snowflake, Microsoft Fabric or Databricks to make it easier to integrate data from sources outside SAP. SNOK carries out BW migrations to a modern data stack while preserving data semantics.
Snowflake operates on a consumption-based model - cost derives from compute and storage usage. For mid-sized organisations, monthly cost can vary widely depending on data volume, query count, number of environments and how compute is managed. SNOK helps design the cost model and optimisation mechanisms.
Yes, data engineering expertise is needed in a production environment. A modern data stack can, however, reduce the maintenance burden compared with a classic on-premise warehouse, thanks to automation, dbt, Snowflake dynamic tables and managed services.
