Microsoft Fabric and Snowflake take center stage in the modern data platform landscape. In late 2025, both companies announced significant interoperability and ecosystem enhancements that reshape how enterprises manage data at scale. Microsoft and Snowflake unveiled a shared vision to simplify cross-platform data access by allowing Snowflake to operate on data stored in Microsoft OneLake through open standards like Apache Iceberg and Parquet, so customers no longer need separate copies of data for analytics and governance.
This move toward seamless interoperability reflects a broader push for flexible analytics architectures that support AI and real-time insights without data duplication. The market context explains why this comparison matters. Industry reports show that over 70% of enterprises now operate hybrid or multi-cloud data environments, and analytics spending continues to rise as AI adoption accelerates.
Organizations evaluating modern data platforms increasingly focus on factors like total cost of ownership, ease of governance, integration with BI tools, and readiness for AI workloads. This is where the trade-offs between Microsoft Fabric’s tightly integrated model and Snowflake’s platform-agnostic architecture become clear.
In this blog, we break down Microsoft Fabric vs Snowflake, comparing architecture, capabilities, pricing considerations, and use cases to help you decide which platform aligns best with your data strategy and business goals.
Key Takeaways Microsoft Fabric and Snowflake are both powerful data platforms, but they serve different types of organizations and use cases. Fabric is a natural fit for teams already working within the Microsoft ecosystem, especially those using Azure, Power BI , and Microsoft 365. Snowflake is better suited for businesses that want multi-cloud flexibility and the freedom to work with a wide range of tools. Fabric follows a predictable, fixed pricing model, while Snowflake charges based on how much you actually use. Analytics and governance feel more seamless with Fabric through Power BI, whereas Snowflake stands out for secure and scalable data sharing. Choosing between the two ultimately comes down to your cloud approach, how you prefer to manage costs, and the skills your team already has.
Understanding the Core Difference Microsoft Fabric puts data engineering, warehousing, data science, real-time analytics, and business intelligence all in one place. Launched in May 2023, it runs everything on OneLake, which acts as a central data lake for your entire organization.
You don’t need to connect multiple separate tools anymore. Data pipelines , SQL analytics, Apache Spark processing, and Power BI reporting all work from the same data source. No more duplicating data or spending weeks on integration work between platforms.
What sets Fabric apart:
Single interface for everything: Run all your analytics workloads on one platform instead of jumping between five different tools and managing separate loginsOneLake storage: One central data lake using Delta Lake format that every service can access, which cuts down on data movement and copiesPower BI built right in: DirectLake mode lets dashboards update in real-time without importing data first, making things noticeably faster than older BI setupsAzure only: Runs exclusively on Microsoft Azure, which makes life easier if you’re all-in on Azure but rules out multi-cloud setups completelyFixed monthly pricing: Pay a set amount for Fabric Capacity Units each month instead of usage-based billing, so budgets stay predictable even if you end up paying for capacity you’re not using
If you’re already running on Azure and Microsoft 365, Fabric fits right in. Your existing security policies, user accounts, and billing all connect automatically. Teams already comfortable with Power BI usually pick it up pretty quickly.
Snowflake runs on Amazon Web Services, Microsoft Azure, and Google Cloud Platform . This means you can use it wherever your data happens to live without getting stuck with one cloud vendor. The company started in 2012 and now powers analytics for more than 9,000 businesses.
Storage and compute work separately in Snowflake. You can scale up processing power without touching storage, and you only pay for what you actually use. This setup has basically become the standard for cloud data warehouses because it handles workloads that go up and down without wasting money.
What sets Snowflake apart:
Works everywhere: Run the same setup on AWS, Azure, or Google Cloud, and replicate data across clouds if you need to, which keeps you from getting locked into one vendorScale things separately: Create multiple virtual warehouses for different teams that scale on their own, so the marketing team running big queries won’t slow down financePay for what you use: Only get charged when warehouses are actively running, with per-second billing and warehouses that shut down automatically when idle, though you need to watch costs carefullyData Marketplace: Get instant access to thousands of external datasets from weather services, financial feeds, and demographic providers without building custom connectionsUse whatever tools you want: Connect any ETL tool, BI platform, or data science setup through standard connectors, which gives you freedom but means more setup work
The downside is complexity. You’ll manage licenses for multiple tools, set up connections correctly, and possibly recreate security rules across different systems.
Real-Time Analytics and AI Capabilities Snowflake’s AI and Streaming Approach Snowflake handles real-time data through Snowpipe Streaming and recently put serious money into AI through Cortex AI. Machine learning and large language models now run directly in the data warehouse.
What Snowflake offers for real-time and AI:
Snowpipe Streaming: Gets data in with under one second of delay through API calls, handling millions of events per second when you need information fastDynamic Tables: Refresh materialized views automatically when source data changes, so you don’t need to manage pipelines manually for real-time aggregationsCortex AI functions: Use capabilities like SENTIMENT, TRANSLATE, SUMMARIZE, and EXTRACT_ANSWER right in your SQL queries without calling external APIsSnowpark ML: Build and run machine learning models in Python inside the warehouse, keeping data secure without moving it to separate ML platformsVector database: Built-in support for similarity search and storing embeddings, which matters a lot for RAG applications
Banks use Snowpipe Streaming for fraud detection that needs to analyze transactions as they happen. Retailers run sentiment analysis on customer reviews using Cortex AI without setting up separate AI infrastructure.
Snowflake works well for continuous data loading, but it’s not built for true real-time analytics where you need answers in milliseconds. It’s better for near-real-time situations where a few seconds of delay won’t hurt.
Microsoft Fabric’s Real-Time Analytics Powerhouse Fabric has dedicated Real-Time Analytics powered by KQL (Kusto Query Language) databases. This technology came from Azure Data Explorer and gives Fabric a real edge when you need extremely fast analytics.
What Fabric offers for real-time and AI:
KQL Database: Built specifically for time-series data and logs, ingesting data in microseconds and returning query results in milliseconds even with billions of eventsEvent streams: Works natively with Azure Event Hubs and IoT Hub to pull in sensor data, application logs, and telemetry at huge scaleCopilot AI assistant: Talk to it in plain English to write KQL queries, generate Python code, and build data transformations without needing deep technical skillsAzure OpenAI integration: Access GPT-4 and other language models directly to build custom AI applications inside FabricReal-time Power BI: Dashboards update automatically as streaming data comes in, with zero refresh delays for monitoring operations
Manufacturing companies use Fabric’s Real-Time Analytics to watch production equipment and catch problems within milliseconds. IT teams build dashboards that show current system health across thousands of servers.
Fabric really shines for genuine real-time analytics where sub-second speed matters. For IoT stuff, application monitoring, and security operations, Fabric’s KQL database beats Snowflake’s streaming pretty decisively.
The catch is you need to learn KQL, which is different from SQL. Teams end up learning KQL for real-time work and SQL for data warehousing, which takes more time than Snowflake’s SQL-everywhere approach.
Data Sharing and Collaboration Snowflake’s External Data Sharing Strength Snowflake’s Data Marketplace changes how companies get external data. With over 2,000 datasets from weather services, financial providers, and marketing data companies, you can add external data to your analysis instantly without building integrations.
How Snowflake handles sharing:
Zero-copy sharing: Share live data with partners without copying data or creating exports, and they see changes right awaySecure data clean rooms: Work with other companies on joint analytics while keeping the actual data private, which advertising and healthcare research use heavilyData monetization: Sell your own datasets through the marketplace to create new revenue from data you already haveCross-cloud sharing: Share data between accounts running on different clouds (like AWS to Azure) without manually moving filesPrecise permissions: Set exactly which tables, views, or even specific rows people can see, with security that enforces itself
Banks use this to add economic indicators to customer data. Retailers mix sales numbers with weather patterns and foot traffic. Healthcare groups join research projects where everyone can query combined data without exposing individual patient information.
Microsoft Fabric’s Internal Collaboration Focus Fabric focuses on collaboration inside your company rather than external sharing. The platform emphasizes shared workspaces where different teams work together on the same data.
How Fabric handles collaboration:
Shared workspaces: Data engineers, analysts, and business users work in one place with interfaces designed for their specific roles and permissionsOneLake shortcuts: Point to data stored anywhere (Azure, AWS S3, Google Cloud Storage ) without actually copying it into FabricGit integration: Version control for notebooks, pipelines, and data models with branching and merging for team development workPower BI integration: Business users view data through Power BI reports they already know while technical teams handle the underlying dataLimited external sharing: You can export data or build APIs for outside access, but there’s nothing like Snowflake’s marketplace built in
For teams inside your company, Fabric’s approach cuts down on friction. Data engineers build pipelines, data scientists train models, and analysts create reports all using the same OneLake data without making copies.
But if you need to share data with partners, suppliers, or join industry data exchanges, Snowflake’s built-in features become pretty much necessary. Fabric wasn’t really designed with external data collaboration in mind.
Developer Experience and Tools Snowflake’s Language Flexibility Snowflake supports lots of programming languages and workflows. Snowpark lets developers write Python, Java, and Scala code that runs directly inside Snowflake without moving data out to external compute.
What developers get in Snowflake:
Multiple languages: Python, Java, Scala, JavaScript, and standard SQL all work with the same featuresIDE integration: Connects to VS Code, PyCharm, IntelliJ, and other popular development tools through official extensionsSnowpark DataFrames: Write transformations using DataFrame APIs that feel like pandas or Spark, so Python developers can jump in quicklyCustom functions: Build your own functions in Python or Java that run natively with access to external librariesContainer services: Deploy custom applications and ML models in containers that sit next to your data for complex processing
Data engineers who know Python find Snowpark familiar because it uses pandas-style syntax. Teams with different technical backgrounds like having language choices.
Microsoft Fabric’s Microsoft-Centric Tooling Fabric gives you a guided experience built around Microsoft tools and languages. It’s optimized for people who know SQL Server , Azure, and Power BI rather than offering tons of language options.
What developers get in Fabric:
T-SQL focus: Main query language is Transact-SQL, which matches SQL Server syntax to make migration from on-premises easierPySpark notebooks: Apache Spark integration for distributed processing, though you need to learn Spark-specific concepts and APIsFewer language choices: Mainly SQL and Python, with less flexibility than Snowflake’s multi-language setupVisual tools: Drag-and-drop pipeline builders and dataflow designers for people who prefer clicking over codingCopilot help: AI writes SQL and Python code for you, suggests ways to make things faster, and explains what query results mean for less experienced folks
If your team already knows Microsoft technologies well, Fabric’s focused approach speeds things up. Teams using different languages or who prefer working from the command line might find it limiting compared to Snowflake.
Data Engineering and ETL Capabilities Snowflake’s Pipeline Automation Snowflake provides solid automation for data pipelines through Streams, Tasks, and Snowpipe. You can build event-driven pipelines that react to changes automatically without needing external orchestration tools.
Snowflake’s data engineering features:
Streams: Track what changed in tables (inserts, updates, deletes) for incremental processing without complicated change data captureTasks: Schedule SQL statements or stored procedures with dependencies for managing complex workflowsSnowpipe: Loads data files automatically as they show up in cloud storage within minutes, no manual work neededExternal functions: Call AWS Lambda, Azure Functions, or other APIs during processing for custom business logicDynamic tables: Refresh materialized views automatically based on source data changes, making pipeline upkeep simpler
Companies with continuous pipelines like Snowflake’s automation. New data arriving every few minutes gets processed automatically without tools like Apache Airflow.
Microsoft Fabric’s Integrated Pipelines Fabric includes Data Factory pipelines and Dataflows for ETL, borrowing features from Azure Data Factory . Everything connects with other Fabric services for a smooth experience building complete data workflows.
Fabric’s data engineering features:
Visual pipeline designer: Drag-and-drop interface for building ETL without code, so analysts and citizen developers can use it100+ connectors: Ready-made connections to common sources including SaaS apps, databases, file systems, and APIsDataflow Gen2: Low-code transformations using Power Query , the same thing Excel and Power BI users already knowSpark integration: Full Apache Spark for complex transformations, machine learning , and handling massive datasetsUnified monitoring: See pipeline runs, notebook executions, and warehouse queries all in one place
For companies where business analysts build ETL, Fabric’s visual tools lower the bar. Data engineers who want detailed control and code-first development might prefer Snowflake’s way of doing things.
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Storage Architecture and Data Format Snowflake’s Proprietary Storage Optimization Snowflake stores data in its own compressed columnar format built for analytical queries. The platform handles storage optimization automatically without you needing to tune anything.
How Snowflake manages storage:
Micro-partitioning: Splits tables into small chunks (50-500MB) automatically and keeps metadata for fast query pruningAutomatic clustering: Keeps reorganizing data for better query performance without you creating indexes or managing partitionsTime Travel: Keeps old versions of data for 1-90 days so you can query data as it was at specific times or recover from mistakesZero-copy cloning: Make instant copies of databases or tables that share storage until you change them, handy for testingAutomatic compression: Gets 4-5x smaller storage on average without you picking compression settings
The downside is you’re stuck with Snowflake. Data in Snowflake’s format needs Snowflake to query it well. Exporting big datasets to use elsewhere takes time and costs money in data transfer fees.
Microsoft Fabric’s Open Delta Lake Format Fabric stores data in Delta Lake format, an open-source table format that works with different engines. Being open gives you flexibility and maybe easier migration, but needs more hands-on management.
How Fabric manages storage:
Delta Lake format: Open standard that works with Apache Spark, Databricks, and other Delta-compatible engines, so you’re not locked inOneLake architecture: One logical data lake that all Fabric services can access, cutting down on duplicate dataShortcuts: Point to data in external storage (AWS S3, Azure Blob, Google Cloud Storage) without copying it into FabricACID transactions: Keeps data consistent when multiple people write and read at the same time using Delta Lake’s transaction logVersion control: Track changes over time with Delta’s versioning, though you need to set up retention policies yourself
The open format means switching platforms later might be easier. You can read Delta tables with other tools without being stuck in Fabric. But you give up some of Snowflake’s automatic optimization.
Multi-Cloud and Deployment Flexibility Snowflake’s True Multi-Cloud Architecture Snowflake runs natively on AWS, Azure, and Google Cloud with the same features and performance on all three. This setup handles complex enterprise needs for backup systems and data residency rules.
Multi-cloud capabilities:
Cross-cloud replication: Copy databases automatically across different cloud providers for disaster recovery and complianceFailover groups: Switch workloads between clouds during outages with barely any downtime, something enterprises test regularlyData residency compliance: Keep data in specific regions to follow rules like GDPR data localizationCloud-agnostic queries: Same SQL works identically no matter which cloud provider infrastructure you’re onIndependent cloud accounts: Run separate Snowflake accounts on different clouds while sharing data securely between them
Global companies use this to run main operations on AWS in North America while keeping Azure systems in Europe for GDPR, with automatic copying between them.
Microsoft Fabric’s Azure-Only Reality Fabric only runs on Microsoft Azure . Azure has data centers in 60+ regions, but you can’t run Fabric on AWS or Google Cloud no matter what.
What Azure-only means:
Simpler architecture: No choosing which cloud provider, which cuts complexity for companies committed to AzureAzure service integration: Connects seamlessly to Azure IoT Hub, Event Hubs, Cosmos DB, and other Azure services without cross-cloud networking hasslesRegional limits: Only works in Azure regions where Fabric is available, which might not cover everywhere you needNo multi-cloud option: Can’t use Fabric as part of architecture spanning AWS, Azure, and GCPVendor dependency: Completely rely on Microsoft for all cloud infrastructure , pricing, and keeping services running
For companies already on Azure, this doesn’t matter. For those with multi-cloud plans or big investments in AWS or Google Cloud, it’s a dealbreaker that needs workarounds like copying data or running separate analytics platforms on different clouds.
Pricing: What You’ll Actually Pay Microsoft Fabric Costs Fabric uses capacity-based pricing with Fabric Capacity Units. You buy a certain amount of compute and storage capacity, then everything shares that capacity. Pricing starts around $262 monthly for F2 capacity good for development, while production usually needs F64 capacity at roughly $8,400 monthly.
What capacity pricing includes:
All platform services: Data warehouse, pipelines, Spark processing, real-time analytics, and Power BI rendering in one priceOneLake storage: 1TB storage per capacity unit included, additional storage runs $0.02-0.03 per GB monthlyPredictable billing: Fixed monthly costs no matter how much you use, which simplifies budgets but means possibly paying for capacity sitting idleNo per-query charges: Run unlimited queries without tracking individual query costs, though queries still eat into shared capacity
Small teams with 5-10 users and under 5TB expect $15,000-30,000 yearly. Medium companies with 50-100 users and 20-50TB typically spend $100,000-200,000 yearly. Big deployments with 500+ users often hit $400,000-1,000,000+ yearly.
Snowflake Costs Snowflake charges separately for storage and compute using credits. Credits represent computational work, costing $2-4 each depending on your edition and region. Storage runs $23-40 per terabyte monthly. A medium virtual warehouse costs 4 credits per hour when running.
How Snowflake pricing works:
Compute credits: Only charged when virtual warehouses actively run queries, with per-second billing and automatic shutdown when idleStorage costs: Separate from compute, covering active data and Time Travel retention for historical queries and recoveryVariable monthly bills: Costs go up and down based on actual use, giving flexibility but needing governance to stop runaway spending from bad queriesExtra charges: Data transfer between regions ($0.01-0.02 per GB), Snowpipe streaming for real-time ingestion, and Cortex AI function calls
Small businesses typically spend $20,000-40,000 yearly including BI tool licenses. Medium companies run $90,000-250,000 yearly. Big enterprises often spend $500,000-2,000,000+ yearly depending heavily on how efficient queries are and data volume.
Microsoft Fabric vs Snowflake: Side-by-Side Comparison Feature Microsoft Fabric Snowflake Platform Type All-in-one platform (warehousing + analytics + AI) Pure data warehouse Best For Microsoft ecosystem users Cloud-agnostic organizations Pricing Capacity-based (pay upfront) Pay-per-use (pay as you go) AI Integration Built-in AI and Copilot Requires third-party tools Flexibility Locked to Microsoft tools Works with any cloud/tool Data Integration Built-in pipelines and Power BI integration Connects easily with third-party ETL tools Storage OneLake unified storage for all data Separate cloud storage with auto-optimization Learning Curve Steeper if new to Microsoft, easier for existing users Simpler SQL-based approach, easier to start
When Microsoft Fabric Is the Right Choice Choose Fabric if your organization runs primarily on Microsoft technology. Companies with 70%+ of users working in Power BI daily, Azure-committed cloud strategies , and limited data engineering resources benefit most from Fabric’s unified approach.
Ideal Fabric scenarios:
Heavy Power BI dependency for dashboards and reporting across the organization Azure-only infrastructure with no multi-cloud plans in the next 3-5 years Teams that prefer integrated platform simplicity over tool flexibility Finance teams requiring predictable fixed monthly costs rather than usage-based billing
Fabric’s native Power BI integration eliminates performance bottlenecks and simplifies data modeling significantly. Organizations with limited technical resources appreciate the guided experience and unified interface.
Snowflake: Best for Multi-Cloud and Flexible Architectures Choose Snowflake if you need multi-cloud flexibility or want to avoid vendor lock-in. Companies with infrastructure spanning AWS, Azure, and GCP, or those requiring external data sharing capabilities, find Snowflake’s approach more suitable.
Ideal Snowflake scenarios:
Multi-cloud architecture or regulatory requirements demanding data residency flexibility Preference for choosing best-of-breed tools for BI, ETL, and data science High concurrency workloads with 100+ simultaneous analytical users Need to share data securely with partners or access external datasets through Data Marketplace
Financial services , retail companies on AWS infrastructure, and life sciences organizations conducting clinical trials typically choose Snowflake for its mature platform and data sharing capabilities.
Quick Assessment Framework: Organizations with 80%+ Microsoft technology stack should lean toward Fabric . Those with diverse technology ecosystems, genuine multi-cloud strategies, or experienced data engineering teams typically benefit more from Snowflake’s flexibility.
Fabric offers unified experience, predictable pricing, and simpler management but limits you to Azure with a smaller community. Snowflake provides multi-cloud portability, mature features, and tool flexibility but requires managing multiple tools and cost governance expertise.
Both platforms handle enterprise-scale analytics well. Your specific context around existing technology, cloud strategy, team skills, and Power BI usage determines the right choice more than raw technical capabilities.
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Case Study: Revolutionizing Data Management with Microsoft Fabric Challenge The client’s Azure Data Lake wasn’t keeping up with their operational needs. The architecture lacked scalability, data models were inefficient, and table storage wasn’t optimized. Manual ingestion and monitoring slowed processes and added errors. Costs were rising due to an unoptimized setup, and weak security controls created risks in their decision‑making systems.
Solution Kanerika rebuilt the data environment using Microsoft Fabric. The team streamlined the architecture, automated ingestion and monitoring, and improved data models and storage design to boost speed and cut costs. Security was strengthened with better governance and hierarchical Row‑Level Security. They also replaced SSIS workflows with Azure pipelines to create a more reliable, scalable analytics foundation.
Results 55% increase in operational efficiency 98% improvement in scalability 31% reduction in processing costs More accurate and trusted analytics from refined workflows and validation
Enterprise Data Modernization Made Simpler with Kanerika and Microsoft Fabric Kanerika is a Data and AI company focused on helping enterprises improve productivity through practical technology solutions. As a certified Microsoft Data and AI Solutions Partner and one of the first global implementers of Microsoft Fabric, we help organizations rethink their data strategy with solid, real-world Fabric deployments. Our work comes from hands-on experience across Fabric architecture, AI features, and unified analytics.
We go beyond the basic setup. Our team builds custom analytics and AI solutions shaped around each company’s needs. Some teams want stronger real-time decisions. Others want cleaner business intelligence or better use of large datasets. We design scalable models, pipelines, and dashboards that support these goals while using the strengths of Fabric’s unified platform. This includes Spark-based engineering, OneLake storage, shared compute, and the Microsoft ecosystem that ties it all together.
Kanerika’s work also includes automated migration paths to Fabric. Our approach reduces manual effort for SSIS and SSAS migration and cuts unnecessary delays during transition. These solutions are built on what we’ve learned from Fabric integration and data engineering projects. We help teams move to Fabric without disrupting daily work, then support them with ongoing improvements as their analytics needs grow.
FAQs 1. What is the main difference between Microsoft Fabric and Snowflake? Microsoft Fabric is an integrated analytics platform with built-in data engineering, warehousing, real-time analytics, and BI tools like Power BI, all in one ecosystem. Snowflake is primarily a high-performance cloud data warehouse that separates compute and storage and focuses on scalable SQL analytics and multi-cloud flexibility.
2. Which platform is better for businesses already using Microsoft tools? If your business already uses Microsoft Azure, Power BI, or Microsoft 365, Fabric provides seamless integration across these services and simplifies workflows by keeping data ingestion, processing and reporting in a unified environment. Snowflake can integrate, too, but may require more external connectors.
3. How do pricing models differ between Fabric and Snowflake? Snowflake typically uses a credit-based pricing model where compute and storage are billed separately based on usage. Fabric uses a capacity-based model where you provision computing capacity that is shared across workloads, which can be more predictable for steady usage patterns.
4. Which platform is better at multi-cloud support? Snowflake natively runs on AWS, Azure and Google Cloud with consistent architecture across clouds, making it a strong choice for multi-cloud strategies. Microsoft Fabric is tightly integrated with Azure and its ecosystem, so it is best suited for Azure-centric deployments.
5. Can Fabric and Snowflake handle real-time analytics? Microsoft Fabric includes real-time analytics capabilities and integrates directly with Power BI for live dashboards and insights. Snowflake can support near real-time analytics, too, but often relies on external tools or pipelines to stream data into its warehouse.
6. Which platform is easier for business analysts versus technical teams? Fabric offers low-code/no-code tools and Copilot-assisted workflows that make it accessible for analysts and BI teams. Snowflake is highly SQL-centric and may feel more natural to data engineers and teams focused on structured analytics and data warehousing.
