The growing volume of data, the need for near real-time reporting, and the expectation of full analytical availability make traditional on-premises solutions increasingly insufficient. The cloud enables:
- scalable processing of large data volumes,
- flexible management of compute and storage resources,
- integration of data from multiple sources into one cohesive model,
- cost optimization through billing models aligned with actual usage.
Choosing the right platform—such as Microsoft Fabric, Snowflake, Google BigQuery, or Amazon Redshift—should not be a purely technical decision made solely by the IT department. It is a strategic choice that impacts:
- the quality of management reporting,
- decision-making speed,
- data security and compliance,
- the long-term architecture of the Business Intelligence environment.
In the following sections of this article, we analyze the key differences between leading solutions and highlight the criteria worth considering when selecting a cloud platform for building a data warehouse.
What Is a Modern Cloud Data Warehouse?
A modern cloud data warehouse is a scalable analytical environment that enables the integration, storage, and processing of data from many systems within a single, cohesive architecture. Unlike traditional on-premises solutions, it does not require maintaining your own server infrastructure and allows flexible resource management.
Comparison Criteria
Selecting a platform for building a cloud data warehouse should be based on clearly defined criteria.
Cost Model
It is worth analyzing:
- pay-as-you-go models,
- the option to reserve resources,
- autoscaling,
- billing transparency.
The cost model directly affects the total cost of ownership (TCO).
Scalability and Performance
Key considerations include:
- dynamic scaling of compute power,
- support for many simultaneous users,
- stability when working with large data volumes.
Platform performance directly impacts the productivity of analytics teams.
Integration with the Data Ecosystem
A modern environment should support:
- ETL and ELT processes,
- integration with BI tools,
- support for data science and AI solutions,
- easy connectivity to ERP, CRM, and financial systems.
The broader the integration, the lower the risk of data silos.
Security and Compliance
The platform must ensure:
- access control,
- encryption of data at rest and in transit,
- compliance with legal regulations,
- audit and monitoring mechanisms.
Data security is one of the key elements of BI architecture.
Support for Lakehouse Architecture
More organizations are adopting the lakehouse approach, which combines features of a data lake and a data warehouse. Support for this architecture includes:
- storing raw and processed data in one environment,
- supporting multiple data formats,
- integration with analytical and reporting tools.
This is a major direction in modern cloud platforms.
Overview of Leading Solutions
Choosing a cloud data warehouse platform should be preceded by an analysis of architecture, cost model, and the degree of integration with the existing IT environment. Below are the most commonly chosen solutions on the market.
4.1 Microsoft Fabric (including Warehouse and Lakehouse)
Microsoft Fabric is a comprehensive analytics platform that integrates data ingestion, processing, storage, and reporting in one environment. A key architectural component is OneLake—a unified data storage layer.
The platform provides:
- full integration with Microsoft Power BI,
- alignment with Azure services and the Microsoft 365 ecosystem,
- support for warehouse and lakehouse architectures within a single platform,
- a unified data model across the organization.
For companies using Power BI, this means simplified architecture, shorter implementation time, and elimination of data silos. Microsoft Fabric is often the natural choice for organizations operating in the Microsoft ecosystem.
4.2 Snowflake
Snowflake is a data cloud platform operating in a multi-cloud model (AWS, Azure, Google Cloud). Its architecture allows independent scaling of storage and compute layers.
Key features include:
- flexible compute scaling independent of storage,
- a billing model based on compute time consumed,
- secure data sharing between organizations,
- high performance in environments with heavy query loads.
Snowflake is often selected by organizations implementing a multi-cloud strategy and collaborating with multiple business partners.
4.3 Google BigQuery
Google BigQuery is a serverless data warehouse operating within the Google Cloud ecosystem. Users do not manage infrastructure—scaling happens automatically.
The platform offers:
- billing based on the amount of data processed (per query) or in a flat-rate model,
- native integration with Google Cloud analytics and ML tools,
- high performance in analyzing very large datasets,
- support for near real-time data processing.
BigQuery is especially effective in projects involving advanced analytics and machine learning models.
4.4 Amazon Redshift
Amazon Redshift is one of the longest-standing cloud data warehouse platforms and is part of the AWS ecosystem.
Key characteristics:
- operation in provisioned or serverless mode,
- integration with Amazon S3 and AWS analytics services,
- broad support for BI and ETL tools,
- technological maturity and enterprise-grade stability.
Redshift is commonly adopted by organizations already using AWS infrastructure.
Practical Comparison – Key Differences
When comparing leading cloud data warehouse platforms, several aspects influence Business Intelligence strategy.
Architectural Flexibility
Platforms vary in their approach to separating storage and compute, supporting lakehouse architectures, and enabling multi-cloud deployment. The choice should align with long-term IT strategy.
Cost Transparency
Pay-as-you-go models, per-query billing, and resource reservations can significantly impact total cost of ownership. Understanding cost structure in the context of actual workloads is essential.
Integration with the Existing IT Environment
Organizations using Microsoft 365 and Power BI often choose Microsoft Fabric. Companies operating within AWS or Google Cloud ecosystems naturally lean toward native services.
Advanced Analytics and AI Capabilities
Integration with data science tools, ML support, and near real-time data processing may provide a significant competitive advantage.
Impact on the Organization’s BI Strategy
The platform determines data architecture, access management models, and how management reports are built. It influences the BI environment for years to come.
How to Match the Solution to Organizational Needs?
Choosing a cloud data warehouse should be based on a thorough analysis of the technological landscape and business objectives. It is essential to understand which systems operate in the organization—ERP, CRM, financial systems, marketing platforms—and how data is currently integrated and reported.
The skills of the IT and BI teams must also be assessed. A different approach suits an organization with an experienced data engineering team than one expecting high automation and out-of-the-box governance mechanisms.
Long-term strategy must also be considered—whether the priority is reducing vendor lock-in or fully utilizing a single technology ecosystem.
Before selecting a platform, pre‑implementation analytical workshops are recommended to define requirements, project scope, and target data architecture.
The Role of an Implementation Partner in the Selection Process
Selecting a cloud data warehouse platform often requires the support of an experienced technology partner. The first step should be a data architecture audit, including an analysis of data sources, flows, and bottlenecks.
The next step is conducting a Proof of Concept (PoC) to verify performance, cost model, and integration with Business Intelligence tools in near‑production conditions.
The partner is also responsible for designing and implementing the data warehouse, and later for its maintenance and cloud cost optimization. Proper resource management directly affects the efficiency of the investment and system stability.
Summary
There is no single universal solution for building a cloud data warehouse. Each organization has a different IT architecture, data structure, and analytical requirements.
The selected platform should align with business strategy, analytical maturity, and the development roadmap of the Business Intelligence environment.
A consistent approach to data management is crucial, as it forms the foundation of modern, scalable, and secure analytics.