Microsoft Fabric Vs. Snowflake: Unveiling the Powerhouses of Data and Analytics 

9th November 2023 by in Blog, Data & AI, Microsoft Fabric

Microsoft Fabric Vs. Snowflake:
Unveiling the Powerhouses of Data and Analytics 

Microsoft Fabric Vs. Snowflake: Unveiling the Powerhouses of Data and Analytics 

In this data-driven world, businesses are constantly searching for the perfect data management and analytics solution that can cater to their unique needs. Among the top contenders are Microsoft Fabric and Snowflake – two behemoths in the SaaS realm for data management and analytics.  

But what distinguishes them? Let’s embark on a journey of exploration of the differences between these two entities and discover their potential. 

Architectures Showdown: Microsoft Fabric Vs. Snowflake 

At the heart of Microsoft Fabric lies a unified ecosystem designed to streamline data and analytics processes. Microsoft Fabric is empowered by leading-edge AI capabilities, including Co-Pilot, Chat GPT, and AI-driven insights, exemplifying a revolutionary leap in data and analytics solutions. Notably, Fabric adheres to the principle of segregating storage and computation, optimizing efficiency. It also boasts seamless integration with popular office applications and collaboration platforms such as Teams. 

With OneLake, Power BI, Azure Synapse, and Azure Data Explorer working harmoniously, users get the best of both worlds: tightly integrated analytics and a unified data lake. The integrated capabilities, powered by AI, amplify the innovation potential and informed decision-making like never before. 

The diagram below illustrates Microsoft Fabric’s architecture and services. 

Microsoft Fabric architecture 

On the other end of the spectrum, Snowflake is a SaaS service that is Cloud agnostic, has zero maintenance, limitless instant scaling, built-in query optimization, hybrid columnar storage, and decoupled compute and storage that scales independently. Those qualities have made Snowflake one of the favorite cloud data warehousing choices in the market so far.   

Snowflake follows the usage-based pricing model, and you only get charged for used compute (per second) and storage (average TB of data stored per month after compression). 

Benefits like performance and support for structured and semi-structured data, concurrency scaling and accessibility, seamless data sharing, high availability, and security first approach make Snowflake very popular in the market.  

Snowflake Architecture 

Microsoft Fabric prioritizes end-to-end integration and ease of use for users already familiar with Power BI and Azure data services. Meanwhile, Snowflake stands out for its managed cloud platform, data separation, flexibility, and robust security. 

Feature Showdown: Microsoft Fabric Vs. Snowflake 

Let’s dive in with a side-by-side comparison of the high-level features offered by both Microsoft Fabric and Snowflake: 

Sr. No  Features  Snowflake  Microsoft Fabric  Winner 
1  Use cases  Data Warehouse, Streaming, Data Engineering, Data Lake, Data Science, and Data Applications.  Data Engineering, Data Science and Machine Learning, Data Lake, Data Warehouse and BI.  Fabric: Broader set of services including leading BI product 
2  Compute  Limitless scaling with virtual warehouses.  Fully managed platform supporting multiple computes like Spark, Synapse SQL, and Kusto DB.  Snowflake:  Fabric needs to mature limitless compute. 
3  Storage  Hybrid columnar storage with managed micro-partitions with zero intervention needed for indexing and partitioning.  Fabric offers OneLake as a single, unified, logical data lake that is built on ADLS Gen2 leveraging open-source Parquet and Delta formats.  Both  
4  Monitoring   Resource monitors, control policies, and cost-monitoring options are available  Microsoft Fabric offers:  
1. Data Activator: “Monitors queries, datasets, and event streams for patterns, trigger alerts, and actions”.  
2. Monitoring hub, which serves as a central location to monitor various activities, including dataset refresh and Spark Job runs and is available for Power BI, Data Factory, Data Engineering, and Data Science. 		 Both  
5  Administration  Near-zero Administration is a SaaS platform.  Near-zero Administration is a SaaS platform.  Both  
6  Security and Compliance  Compliant with SOC 1 Type II, SOC 2 Type II, HIPAA, GDPR, ISO 27001, and FedRAMP.  Data is encrypted at rest and offers RBAC (Role-Based Access Control) for user and privilege management. Robust security includes MFA (Multi-Factor Authentication) and VPN connectivity.  Fabric offers applying Sensitivity labels to guard your sensitive content against unauthorized data access and leakage.   Additionally, data is encrypted at rest and offers RBAC (Role-Based Access Control) for user and privilege management. Robust security includes MFA (Multi-Factor Authentication) and VPN connectivity.  Snowflake: Due to support for virtual private Snowflake instances.  
7  Data Ingestion  With Snowpipes, Stream, Stages, External tables, and third-party tools like Mitillion, Fivetran, and the Native Copy command.  Data Pipelines, Data Flows, and Copy commands; cross-warehouse ingestion; and partners like DBT-Data Build Tool, Databricks, and more.  Both 
8  Support for building data apps  Streamlit is an open-source Python library that facilitates the creation and sharing of beautiful, custom web apps for machine learning and data science. It enables you to quickly build and deploy powerful data apps within minutes.  Custom data apps can be quickly created using Power BI.  Both 
9  Support for Notebook Environment  Python worksheets provide support for all DataFrame-based libraries, including but not limited to Pandas, NumPy, Boto3, Plotly, and more.  Fabric supports the Notebook environment for both data science and data engineering workloads.  Fabric: Snowflake depends on third-party tools (Jupyter) for notebook environments.   
10  Querying and Analysis  Snowflake supports Snowsight, Snowsql, Jupyter Notebook environment, etc. for querying data and analysis.  Fabric supports Notebook, SQL Endpoint (Spark), and SQL WH interfaces for connectivity.  Both 
11  Data Sharing  Snowflake supports both public and private live data sharing and data monetization capabilities using the marketplace.  Needs to leverage the “Azure Data Share” service for data sharing. Data sharing is not a default option with Fabric for now.  Snowflake: Advanced data-sharing features 
12  Cloning/Copying source datasets  Zero copy cloning is supported for most of the object types, including tables and schemas.   Only Zero copy cloning of tables is supported for now, and there are no other object types or schemas.  Snowflake: Advanced cloning options 
13  Reporting needs  Basic support for building dashboards and charts.  Fabric supports enterprise-grade reporting features supplied using Power BI.  Fabric: due to the inbuilt Power BI service 
14  Data Profiling Capability  Default Data profiling supports the first 1 million rows for every query result or output.  We need to develop custom scripts using languages like Python, R, or SQL to perform in-depth data profiling. Services like Azure Databricks, Data Pipelines, and Synapse Analytics can help with this too.  Snowflake Every select query result gets profiled by default. 
15  Pipeline and orchestration  Snow Tasks provides basic noncyclic orchestration and support for third-party tools like Airflow and Data Factory.  Data Factory, Data Flows, Copy Command, Pipeline Jobs.  Fabric as it provides native tools, flexibility, and integration ease 
16  Data support  Support structured and semi-structured data types like Avro, Parquet, JSON, etc.   Native integration with Azure Data Lake and Delta Lake makes it an ideal choice for structured and unstructured data.  Fabric:  As it supports unstructured data types. 

Wait, we’re not done yet! Let’s dig deeper into the trenches and explore the object-level comparison of these remarkable platforms. 

Sr. No  Snowflake Objects  MS Fabric- Equivalent Objects (Synapse) 
1  Tables  (Permanent, Transient, Temporary)  Tables (permanent and local temporary tables are supported). Global temporary tables and transient user tables in TEMPDB are not supported in Fabric. Primary and Foreign Keys are not supported. Tables are case-sensitive. 
2  Constraints and keys: Snowflake supports defining and maintaining constraints but does not enforce them, except for NOT NULL constraints, which are always enforced.  Constraints and keys: For warehouses, PRIMARY KEY, and UNIQUE constraints are only supported when NONCLUSTERED and NOT ENFORCED options are used. 
 
FOREIGN KEY is only supported when the NOT ENFORCED option is used. 
3  Standard SQL views are supported  Standard SQL views are supported 
4  Materialized views are natively supported.  Materialized views are only supported in Fabric real-time analytics and do not yet support fabric warehouse or Lakehouse. Instead of using materialized views, you can materialize the results of complex queries by storing them in tables. This approach allows you to pre-compute and store the results of expensive or frequently used queries, improving query performance for subsequent executions. 
5  External tables are supported natively in Snowflake.  External tables are supported only in Lakehouse, and you can create external tables directly on top of OneLake files. 
6  Secure views are used to share sensitive objects across different accounts.   The secure views option is not available or supported in Fabric yet. 
7  Capturing table CDC is made easy using table Streams in Snowflake. Below are different types: Delta (standard stream) Append Only stream Insert Only  In Fabric, Change Data Capture (CDC) is not natively supported as a built-in feature. However, you can implement CDC-like functionality using other Azure services.  
One possible approach is to use Azure Data Factory and Azure Event Hubs to capture, and process change data. 
8  Sequences are supported natively within Snowflake.  Sequence objects are not supported yet in Fabric. We need to build custom logic to create sequences in Fabric.  
9  In Snowflake, Procedures are supported with JavaScript, Python, and SQL Language.  In the Fabric warehouse -Stored Procedures are supported with only SQL language support. There is no support for other languages yet. 
10  Function, UDFs (support Java, JavaScript, and SQL).  In Fabric Scalar Functions, Table Valued functions are only available with SQL language support 
11  Stages (both internal and external Stage creation is supported).  Shortcuts for External Storage and Onelake/Lakehouse for Ad Hoc File Uploads. 
12  Schema’s creation is supported by Snowflake.  Schema creation is supported by Fabric. 
13  File Formats: All standard file formats are supported.  All standard file formats (CSV, ORC, Parquet, JSON) are supported. 
14  We can create database instances in Snowflake.  An equivalent construct in Fabric is called a “Warehouse”. 
15  Roles: Elaborate roles and role hierarchies can be created within Snowflake.  Roles are supported at the Fabric Workspace level. Available roles are: 
1. Admin  
2. Member  
3. Contributor  
4. Viewer. 
16  Resource monitors can be configured to manage virtual warehouse costs.  Microsoft Fabric, the Monitoring hub, serves as a central location to monitor various activities, including dataset refresh and Spark Job runs, and is available for Power BI, Data Factory, Data Engineering, and Data Science 
17  External function executions (AWS Lambda) are possible through Snowflake.  External function execution is not supported in the Fabric Preview Environment. 
18  Metadata access using the information schema.  The Information Schema option is supported in the Fabric Warehouse environment. 
19  Snowpipes can be created for the automatic loading of files from the external stage.  In Fabric, Structured Spark Streaming can be leveraged to load data files. 
20  In Snowflake Zero, copy cloning of databases, schemas, and other objects is fully supported.  In Fabric Zero, copy cloning of only tables is supported in Preview. 
21  Data Masking- Both row and column-level masking and tag-based masking are possible.  Object-level security can be managed using GRANT, REVOKE, and DENY syntax.  
Row-level security is currently not supported.  
Dynamic data masking is currently not supported. 
22  Search Optimization on tables.  Search optimization is not available as a service. 
23  Disaster Recovery: Up to 90 days of Time travel and 7 days of fail-safe.  Time travel is supported by Lakehouse Delta Tables only. 

Concluding Thoughts 

In conclusion, both Microsoft Fabric and Snowflake are robust data and analytics platforms, each with their own strengths and capabilities. Both are competitive platforms with their own pros and cons that would look a lot like each other in the future as both are trying to become end-to-end analytics platforms. 

The success of both platforms depends on your specific needs and requirements. There is no easy answer to which platform to choose without understanding the customer’s unique situation. It could be Snowflake, Fabric, or a combination of both, depending on the current state and where you want your future enterprise software landscape to be. 

Reach out to us today, and let’s make your data dreams a reality! 

At iLink Digital, we are passionate about empowering businesses with cutting-edge data solutions.  

Contact us and experience the power of data solutions tailored to your unique business needs. Request a personalized demo or free consultation with our team of experts. 

Digambar Jadhav

Digambar Jadhav

Senior Technical Architect DATA

Linkedin
About Author

“Bringing over 15 years of extensive experience in the field, Digambar Jadhav is a seasoned data architect specializing in modern cloud data technologies such as Snowflake, Databricks and Microsoft Fabric. With a versatile background in various roles within data warehousing projects, he excels in architecture design, solutioning, and offering comprehensive support in data engineering and data modeling. Known for leveraging cutting-edge cloud platforms, Digambar is committed to delivering innovative solutions and optimizing data ecosystems.”

SHARE
Share on facebook
Share on google
Share on twitter
Share on linkedin

Related Blog Posts

    Open Chat

    Etiam magna arcu, ullamcorper ut pulvinar et, ornare sit amet ligula. Aliquam vitae bibendum lorem. Cras id dui lectus. Pellentesque nec felis tristique urna lacinia sollicitudin ac ac ex. Maecenas mattis faucibus condimentum. Curabitur imperdiet felis at est posuere bibendum. Sed quis nulla tellus.

    ADDRESS

    63739 street lorem ipsum City, Country

    PHONE

    +12 (0) 345 678 9

    EMAIL

    info@company.com

    PURCHASE