DATA WAREHOUSE CONSULTING
Data warehouse consulting for modern, open architectures
From data strategy to a production-ready platform—cloud, on-premises, or hybrid. Technology-agnostic, with robust governance and scalability. We work with you to develop the architecture that best suits your data, your budget, and your compliance requirements.
Modern data warehouse consulting doesn’t start with the tool—it starts with your goals. We combine proven data warehouse principles with open, cloud-native architectures—using Snowflake, Databricks, Qlik, Talend, and dbt. This creates a data platform that scales, remains auditable, and delivers real value to business units. Learn more about the big picture in our Data & Analytics Consulting services.
The Reality of Modern Data Architectures
Three obstacles slow down data-driven projects—regardless of industry or tools used.
Data Silos & Fragmented Sources
Distributed systems, inconsistent definitions, no single point of truth—reports contradict each other.
Scaling & Costs
Growing data volumes are driving up costs, while rigid architectures are slowing down time-to-insight.
Governance & Auditability
A lack of lineage and incomplete documentation pose a risk to compliance and audits.
Architectural Approaches for Your Data Warehouse
Depending on your data strategy, toolset, and business requirements, we’ll work with you to select the right model—one that’s vendor-neutral.
Classic Core DWH
Reliable ETL processes, high data quality, and proven BI structures for long-term reporting needs.
Data Vault 2.0
Audit-traceable, auditable, and easily expandable—ideal for compliance and growing source systems.
Cloud Lakehouse
Open formats (Apache Iceberg), SQL, and ML on a single platform—with Snowflake or Databricks.
Hybrid Architecture
A combination of on-premises and cloud—for phased migration and compliance-compliant workloads.
Logical DWH
Real-time data virtualization without physical replication—for example, using Denodo.
Streaming / Kappa
Real-time pipelines instead of batch processing—ideal with Kafka and Confluent for IoT, logistics, and fraud detection.
Typical Use Cases
From corporate reporting to the foundation for AI—this is what our customers are implementing with modern data warehouse architectures.
Centralized Group Reporting
Standardized KPI reports and self-service analytics using Qlik, Power BI, or Tableau based on consolidated data.
Self-Service BI & Data Products
Business units access reusable data products in a governed manner—without IT bottlenecks.
Master Data & MDM
Centrally managed master data for e-commerce, production, and CRM—automatically distributed.
Real-Time & IoT Analytics
Streaming Pipelines for Logistics, IoT, and Customer Service – Analyzing Events in Real Time.
SAP BW Replacement
Replace BW on HANA with Snowflake or Databricks—without losing reporting capabilities and at a lower cost.
Fundamentals of ML & AI
A clean data platform as the foundation for machine learning, forecasting, and agentic AI.
Technology & Stack
We are open to all technologies and select the platform that best suits your needs. For AI-powered analytics, we rely on Qlik as the leading platform—combined with Qlik Talend for robust data integration. This is complemented by modern cloud and governance components.
Key Topics in the Data Warehouse Environment
Want to dive deeper? We explore these topics in more detail on separate pages.
Data Lakehouse Strategy
Evaluate lakehouse architecture and develop a future-proof data strategy—with our workbook.
Data Vault Modeling
Historical, audit-proof data warehouse models using Data Vault 2.0.
Data Integration with Qlik Talend
Robust ETL/ELT integration with Qlik Talend—over 110 certified consultants.
Data Governance & Data Catalog
Transparent data flows and standardized terminology with Collibra, Alation, or One Data.
Snowflake
Cloud DWH implementation and architecture consulting for Snowflake.
Databricks
Lakehouse, data engineering, and AI on the Databricks platform.
Data warehouse projects in customer environments
From reporting to AI forecasting—this is what our customers are achieving with modern data warehouse architectures.
Phoenix Contact
Cloud-based data warehouse with Azure & Snowflake for uniform management of key figures worldwide.
Myneva Group
Centralized reporting for social institutions with Talend & Data Vault for audit compliance.
Porta
Real-time data from the e-commerce channel flows into a central Snowflake solution with Data Vault.
Veolia Environmental Services
Cloud Data Warehouse for Logistics & Waste Management with Qlik, Talend, and Azure – including automation and governance.
Cosnova
Data integration for reporting & trend analyses in the cosmetics trade with Snowflake and BI connection.
Generali
Data Vault 2.0 for rule-based harmonization of complex data structures for actuarial evaluations.
25+
Years of project experience
300+
Consultants
Qlik Elite
Partner since 2009
ISO 27001
Certified for Information Security
What questions do IT managers ask about data warehouse consulting?
Cloud, on-premises, or hybrid—which data warehouse architecture is right for us?
That depends on your data strategy, compliance requirements, and existing toolset. We provide an unbiased assessment and recommend the architecture that best aligns with your goals—from traditional core systems to Data Vault to cloud lakehouses—whether in the cloud, on-premises, or in a hybrid environment.
Is a Lakehouse (Snowflake/Databricks) a better choice than a traditional data warehouse?
Often, yes—especially when dealing with large, diverse data sets, ML workloads, and cost optimization using open formats such as Apache Iceberg. We’ll compare both approaches for your specific situation and calculate the potential savings in concrete terms.
We already use Power BI, Tableau, or SAP BW. Can we work with cimt?
Yes. Our services—architecture, integration, governance, and data quality—are technology-neutral. cimt is a Qlik Talend Elite Partner as well as a partner of Snowflake and Databricks, but we can easily work with the toolset you’re already using.
What technologies do you use in Data & Analytics?
These include Databricks, Snowflake, and Qlik Talend for platform and integration; dbt for transformation; and Collibra, Alation, and Open Data Hub for governance and cataloging. For analytics and AI-powered analysis, we rely on Qlik—which we consider the leader in AI support for data analytics.
How does a DWH project with cimt work?
We usually start with a no-obligation strategy session, followed by a concise architecture assessment. This results in a prioritized roadmap—and we guide you every step of the way, from the initial decision all the way to productive, auditable operations.
How do you ensure governance, auditability, and compliance?
Through audit-ready models such as Data Vault, end-to-end lineage, and a data catalog (e.g., Collibra or Alation), as well as clear ownership structures—ensuring that your data remains traceable, verified, and compliant.
Arrange a DWH strategy meeting now
Let’s outline the right data warehouse architecture for your company during a no-obligation initial consultation—one that’s technology-agnostic and tailored to your specific situation.