Google Cloud BigQuery vs Microsoft Fabric: Data Platform Comparison
Modern data platforms such as Google Cloud BigQuery and Microsoft Fabric are revolutionising the way information is analysed.
Thanks to them, it becomes possible to extract valuable insights, automate complex processes, and make more precise business decisions. A conscious choice of the optimal analytical tool can determine your company’s strategic advantage in the market.
What are modern data platforms?
A modern data platform is a comprehensive solution that enables managing the entire data lifecycle from acquisition, through storage and processing, to analysis and visualisation.
Unlike traditional systems, modern platforms offer scalability, flexibility, and advanced analytical features, often using the cloud as the operating environment, which eliminates the need to manage physical infrastructure.
What is Google BigQuery
Google BigQuery is a serverless, fully managed cloud data warehouse that enables lightning-fast analysis of massive datasets using SQL.
It eliminates the need to manage infrastructure and allows you to focus on data analysis instead of server maintenance. It is part of the Google Cloud Platform ecosystem.
BigQuery uses columnar architecture and a distributed processing model that allows queries to be executed in parallel across thousands of servers. It separates storage from processing, enabling flexible scaling and cost optimisation. Thanks to this, it provides instant results even for petabytes of data without the need for indexing.
Strengths
Google BigQuery stands out with unmatched query performance on massive datasets. The serverless architecture eliminates infrastructure management, providing instant scalability. Advanced integration with Google Analytics tools, ease of use with SQL, and powerful ML capabilities directly in the database make it ideal for companies focusing on data analysis.
Weaknesses
BigQuery costs can quickly rise with unoptimised query processing of large amounts of data. There are limited transformation capabilities compared to dedicated ETL tools. Strong ties to the Google Cloud ecosystem limit flexibility in multi-cloud environments. There are limits for certain operations and delays when reading newly written data.
Integrations
Google BigQuery natively integrates with the Google Cloud ecosystem (Storage, Dataflow, Dataproc, Looker) and popular analytical tools like Tableau or Power BI. It offers connectors for marketing systems (Google Analytics, Google Ads) and business systems. APIs and libraries for programming languages are also available, enabling custom integrations.
AI and Machine Learning
Google BigQuery ML allows building machine learning models directly in SQL, without the need to export data. It supports regression, classification, recommendations, and anomaly detection. It integrates with TensorFlow and AutoML, allowing advanced users to implement their own algorithms. Models can be trained directly on data stored in the warehouse.
Pricing model
Google Cloud BigQuery offers a pay-as-you-go model where you pay for executed queries and storage. For predictable workloads, subscriptions (BigQuery Reservations) with dedicated resources are available. It is worth optimising queries, as costs can rise quickly with inefficient analyses.
When to choose Google BigQuery?
Choose Google BigQuery if you need lightning-fast analysis of massive datasets without infrastructure management. It is ideal if you use Google Cloud, prefer a pay-as-you-go model, need advanced SQL and ML analyses in the database, and your priorities are performance and scalability without complications.
What is Microsoft Fabric
Microsoft Fabric is a comprehensive analytics and data platform that combines diverse tools in one environment. Based on a SaaS architecture, it integrates components such as Data Factory, Data Engineering, Data Warehouse, and Power BI.
Its central element is OneLake, a unified data repository. Fabric offers built-in AI features, including Microsoft Copilot, enabling task automation and generating intelligent insights.
Microsoft Fabric is based on a SaaS architecture with OneLake as the central element, which eliminates data silos. The platform connects all data workloads, from data engineering, through warehouses, to real-time analytics.
Benefits include centralised data management, seamless integration with the Microsoft ecosystem, built-in AI features, and the medallion architecture (bronze-silver-gold) supporting data processing from raw to advanced analytics.
See the business guide on Microsoft Fabric
Strengths
Microsoft Fabric stands out with comprehensive integration of all aspects of data analytics in one platform. A key advantage is OneLake, a unified repository that eliminates data silos. Fabric offers a wide range of analytical tools tailored to different roles within an organisation. Native integration with the Microsoft ecosystem (Power BI, Azure, Microsoft 365) ensures smooth workflows. Built-in AI features, including Microsoft Copilot, automate tasks and deliver intelligent insights. Fabric also enables comprehensive data management with access control and regulatory compliance.
Weaknesses and limitations
Microsoft Fabric, despite its comprehensiveness, has its limitations. The platform is strongly tied to the Microsoft ecosystem, which may hinder integration with other vendors’ solutions. Fabric is a relatively new product, so it has less mature functionalities compared to specialised tools. Multi-cloud flexibility is limited mainly to Azure. The capacity model may be less flexible for organisations with variable computing needs. Additionally, the platform’s complexity may extend the learning curve for new users.
Integrations
Microsoft Fabric offers native integration with the entire Microsoft ecosystem, including Microsoft 365, Microsoft Azure, Microsoft Copilot Studio, and Microsoft Power Platform. It also has numerous connectors to external systems, including Snowflake, Google BigQuery, MongoDB, and AWS S3. Thanks to Data Factory, Fabric can ingest data from diverse structured and unstructured sources. Integration with Power BI provides advanced visualisation capabilities, and connection with Microsoft Azure AI Foundry enables the use of advanced artificial intelligence features.
AI and Machine Learning
Microsoft Fabric offers advanced AI capabilities through integration with Azure Machine Learning in Microsoft Azure AI Foundry and Microsoft 365 Copilot. The platform enables building, deploying, and managing ML models within a unified environment, without the need to switch between tools. AI features are embedded throughout the entire data lifecycle, from engineering to business analysis. Fabric automates routine tasks, generates quick reports, and builds auto-models, making it a good choice for companies seeking integrated AI experiences.
Pricing model
Microsoft Fabric offers two main pricing models: Pay-as-you-go (flexible, no commitments) and Reserved (with savings up to 40% with annual reservation). Costs depend on two main factor:s computing power (Compute) and storage (Storage). A single compute capacity can handle all features simultaneously and be shared across multiple projects. Fabric also offers three types of licenses for users: Free, Pro, and Premium per-user.
See the Microsoft Fabric licensing and pricing guide
When to choose the Microsoft Fabric data platform?
Microsoft Fabric will be the optimal choice for organisations already using the Microsoft ecosystem. It works well for companies seeking a comprehensive solution covering the entire data lifecycle from acquisition to visualisation. It is ideal for enterprises needing integration of various teams (data engineers, analysts, data scientists) on one platform.
Fabric will also suit organisations that want to leverage advanced AI features without building complex infrastructure, using built-in tools supported by Microsoft Copilot.
What is the difference between Google BigQuery and Microsoft Fabric
Google BigQuery focuses on high-performance analysis of massive datasets in a data warehouse model, while Microsoft Fabric offers a comprehensive platform combining many analytical tools. BigQuery is a specialised solution for fast SQL query processing, while Fabric is an ecosystem integrating different aspects of working with data.
BigQuery is tightly tied to Google Cloud Platform, while Fabric is integrated with the Microsoft ecosystem. They also differ in their approach to data storage. BigQuery is based on its own columnar format, while Fabric uses OneLake as a shared repository.
Fabric provides a more comprehensive approach with dedicated environments for different specialists, while BigQuery stands out with simplicity and SQL query performance.
The choice between them depends on the existing infrastructure, specific analytical needs, and the organisation’s integration preferences.
Which system to choose for the company?
Small company
For a small company, the key selection factor is ease of implementation and minimising administrative costs.
For a small company, BigQuery will be a better choice if it needs a simple, efficient data warehouse with a pay-as-you-go model that does not require large upfront investments. Microsoft Fabric will work better in small companies already using the Microsoft ecosystem, which need an integrated analytical environment with Power BI visualisations.
Medium company
A medium-sized company should base its choice on the existing IT infrastructure and specific analytical needs.
A medium-sized company should choose BigQuery if the priority is analysis speed and simplicity of implementation without a large IT team. Microsoft Fabric will be a better choice for organisations that need a comprehensive analytical environment integrating data from many systems and want to avoid information silos between departments.
Large company
Large enterprises should make their choice based on IT strategy, existing investments, and a long-term vision of data management.
Large enterprises should consider BigQuery if processing performance of massive datasets is key and they are already using Google Cloud. Microsoft Fabric will be a better choice for corporations integrated with the Microsoft ecosystem, needing a comprehensive analytical platform supporting different roles in the organisation and offering advanced AI capabilities.
Summary
The choice between Google BigQuery and Microsoft Fabric depends primarily on the company’s priorities and existing technology ecosystem. For small and medium-sized companies without large IT teams, Microsoft Fabric will often be easier to implement, while large organisations may consider a hybrid approach, leveraging the strengths of both platforms in different areas of activity.
BigQuery offers unmatched data analysis performance in a simple, serverless model with pay-per-use fees. Companies using Google Cloud, focusing on efficient SQL analyses, should choose BigQuery.
Implementing Microsoft Fabric will be a better choice for organisations integrated with the Microsoft ecosystem, valuing ease of implementation, a unified environment, and ease of use without the need for infrastructure management.
