Google Cloud vs. Microsoft Azure for Building Medallion Architecture Data Platforms – Pros, Cons, Evaluation, and Examples

In the rapidly evolving landscape of data analytics and AI, choosing the right cloud platform for implementing a Medallion Architecture can make or break your data strategy.

As a Google Cloud and Google Workspace expert at Saulius-Systems, I've worked extensively with GCP's tools, but I've also evaluated Microsoft Azure's offerings, including Fabric, to provide balanced insights for our readers.

This review compares the two ecosystems head-to-head for building modern data lakehouses using Medallion Architecture—a layered approach (Bronze, Silver, Gold) that refines data progressively for scalability, quality, and usability.

We'll dive into key aspects like:

• architecture,

• performance,

• pricing, and

• AI integration, highlighting pros and cons based on recent 2025 analyses.

  I'll include real-world examples and an overall evaluation to help you decide what's best for your organisation. To enhance understanding, I've added visual diagrams using Mermaid for flowcharts and charts, along with a comparison table.

  Let's get started.

What is Medallion Architecture?

Medallion Architecture is a data design pattern that organises your data lake or lakehouse into progressive stages of refinement: Bronze (raw), Silver (standardised), and Gold (curated). Originally popularised in lakehouse environments, it's highly adaptable to cloud platforms, where you can use open formats like Apache Iceberg or Delta Lake for advanced features such as time travel and schema evolution.

Both GCP and Azure support it, but their implementations differ in terms of unification versus modularity.

Architecture and Integration

• Google Cloud (GCP) Approach

GCP adopts a modular, best-of-breed strategy, combining services like BigQuery (for lakehouse querying), Cloud Storage (data lake), Dataflow (ETL pipelines), and Pub/Sub (streaming).

For Medallion, you can use BigLake with Apache Iceberg for open-format tables across layers.

- Pros:

High flexibility—mix and match tools without vendor lock-in. Supports multi-cloud and open standards like Parquet/Delta. Easy integration with external tools (e.g., Databricks on GCP for unified governance).

- Cons:

Requires more setup and orchestration (e.g., using Cloud Composer for workflows), which can increase complexity for smaller teams.

• Microsoft Azure (Fabric) Approach

Microsoft Fabric is a unified SaaS platform, bundling lakehouse (OneLake), data engineering (Pipelines/Dataflows), and BI (Power BI) in one workspace. Medallion is natively supported with shortcuts for Bronze ingestion and Delta tables for Silver/Gold transformations.

- Pros:

Seamless integration within the Microsoft ecosystem (e.g., direct ties to Azure Synapse and Power BI). Reduces silos with a single pane for governance and metadata.

- Cons:

More opinionated and less modular, which may lead to vendor lock-in. Limited multi-cloud support compared to GCP.

Evaluation: If your stack is Microsoft-heavy (e.g., Office 365, Azure AD), Fabric's all-in-one design wins for speed to value.

GCP shines for custom, hybrid environments where flexibility is key.

Performance and Scalability

• GCP

BigQuery's serverless model auto-scales queries across petabytes, with ML integration via BigQuery ML. For Medallion, Dataflow handles streaming/batch transforms efficiently, and Iceberg enables time travel for rollbacks.

- **Pros:

Exceptional query speed (sub-second for TBs) and cost-optimised storage/compute separation. Handles real-time data well with Pub/Sub.

- **Cons:

Performance can dip with unoptimized schemas; it requires manual partitioning in Silver/Gold layers.

• Azure/Fabric

Fabric leverages Delta Lake for ACID transactions and Spark-based processing, with real-time intelligence for streaming. It's optimised for large-scale ELT in lakehouses.

- **Pros:

Strong in concurrent workloads and governance (e.g., automatic lineage in Purview). Scales seamlessly for enterprise data volumes.

- **Cons:

Can be slower for ad-hoc queries vs. BigQuery; higher latency in multi-workspace setups.

Evaluation: GCP edges out for raw analytical performance in 2025 benchmarks, especially ML-heavy workloads.

Fabric is better for integrated, governed scalability in regulated industries.

Pricing and Cost Management

• GCP

Pay-per-query model in BigQuery (e.g., $6/TB scanned), with flat-rate slots for predictable costs. Storage is cheap via Cloud Storage.

- **Pros:

Transparent and cost-effective for intermittent workloads; free tiers and autoscaling prevent overprovisioning.

- **Cons: Query costs can spike with inefficient code; no bundled pricing for full pipelines.

• Azure/Fabric

Pay-as-you-go, but unified billing across components.

Capacities start at ~$0.36/hour for compute.

- **Pros: Predictable for heavy users; integrations reduce hidden costs (e.g., no extra ETL fees).

- **Cons: Can be pricier for storage-heavy Medallion layers; vendor-specific pricing lacks multi-cloud discounts.

Evaluation: GCP often undercuts Azure by 20-30% for query-based analytics, but Fabric's bundling saves on management overhead for Microsoft shops.

Ease of Use and Developer Experience

• GCP

Intuitive console and APIs, with strong support for Python/Spark via Colab integration.

- **Pros: Developer-friendly with open-source alignment; quick prototyping.

- **Cons: Steeper learning curve for assembling Medallion pipelines.

• Azure/Fabric

No-code/low-code options in Dataflows; tight Power BI synergy.

- **Pros: User-friendly for business analysts; metadata-driven automation simplifies governance.

- **Cons: Less flexible for custom scripts; occasional UI glitches in previews.

Evaluation: Fabric lowers the barrier for non-engineers, while GCP appeals to devs building bespoke solutions.

AI/ML Integration

• GCP

Vertex AI embeds ML directly into BigQuery for predictions in Gold layers.

- **Pros: Leading in generative AI (e.g., Gemini models); seamless with TensorFlow/PyTorch.

- **Cons: Requires extra setup for end-to-end MLOps.

• Azure/Fabric

Copilot and Azure ML integrate with lakehouses for AI-driven insights.

- **Pros: Strong in enterprise AI with Microsoft ecosystem (e.g., OpenAI ties).

- **Cons: Less mature in open ML frameworks vs. GCP.

Evaluation: GCP leads for cutting-edge AI in 2025, but Fabric excels in productivity tools like Copilot for data pros.

Real-World Examples

- **Retail Analytics on GCP**: A fresh produce retailer like Ida ingests supply chain data into Bronze via Pub/Sub, cleanses in Silver with Dataflow (deduplicating via Spark), and aggregates inventory forecasts in Gold using BigQuery ML.

Result: Processes millions of rows daily for AI predictions, reducing chaos in perishable goods management.

- **Healthcare Pipeline on Azure/Fabric**: A U.S. healthcare company uses shortcuts for Bronze EHR ingestion, Notebooks for Silver validation (ensuring HIPAA compliance), and Power BI for Gold dashboards.

This secured sensitive data across layers, improving integrity and access controls.

Overall Evaluation and Recommendations

In 2025, both platforms excel at Medallion Architecture, but choices boil down to your ecosystem.

Microsoft Fabric (8/10) is ideal for unified, Microsoft-centric environments—great for quick wins in BI and governance, but with potential lock-in.

Google Cloud (9/10) wins for flexibility, performance, and AI innovation, especially in multi-cloud or dev-heavy setups, though it demands more orchestration.

For Saulius-Systems clients, I recommend starting with GCP for AI architectures, but hybrid approaches (e.g., Databricks on both) can bridge gaps.

What's your experience? Share in the comments!

s Team | October 05, 2025*