Azure Synapse VS Snowflake – Which is Best?

October 16, 2024

Data is everywhere in our digital product engineering world. By 2025, we’ll have a massive amount of data globally – 175 Zettabytes! With so much information, businesses need good tech tools to collect and understand data quickly.

Choosing the right cloud data platform is really important for this. In this blog, we’ll look at two major players – Azure Synapse and Snowflake, and explain what makes them unique.

This choice is about more than just technology – it can really impact how well a business uses its data.

Keep reading to see the comparison of Azure Synapse vs Snowflake, so you can figure out which one fits your business needs best!

What is Azure Synapse?

Azure Synapse is Microsoft’s tool for working with data in the cloud. It uses smart technology to handle lots of data quickly.

This platform lets different data experts work together to find useful information. It helps companies gather, organize, and understand their data to make better decisions faster.

Think of it like a powerful helper that turns raw data into valuable insights, making businesses work better and succeed more.

🡆 Key Features

• Comprehensive cloud data warehousing capabilities
• Built-in tools for governance
• Highly parallelized processing
• Seamless integration with various Azure offerings

🡆 Pros of Azure Synapse

Azure Synapse is a robust analytics platform that merges data warehousing, big data processing, data integration, and machine learning, enabling you to access, analyze, and visualize your data at scale. Here are some benefits:

• Comprehensive Data Warehousing: Offers an all-in-one solution for storing and managing data in the cloud.
• Massively Parallel Processing: Handles large datasets efficiently, providing fast and responsive analytics for informed decision-making.
• Seamless Integration: Integrates with a variety of Azure products, resulting in a single platform for sophisticated analytics and data management.
• Serverless Flexibility: Provides complete control and adaptability for various use cases.
• Real-time Information: Offers dependable data and precise reports promptly.
• Adaptable Data Storage: Allows data storage in any preferred format, with the flexibility to structure and organize data as needed.
• Unified Microsoft Ecosystem: Integrates seamlessly with other Microsoft solutions, enhancing collaboration and compatibility.
• Integrated Governance Tools: Ensures secure, compliant, and effective data management with built-in governance tools.
• Exceptional Performance: Processes tasks quickly for optimal performance across diverse data scenarios.

🡆 Cons of Azure Synapse

While Azure Synapse is a dependable solution for data management, it has some limitations. Common issues users have faced include:

• File Size: There are restrictions on the size of files that can be loaded onto the platform.
• Usability: Generating reports can be less user-friendly due to the lack of intuitive, drag-and-drop features.
• Integration Complexity: The integration within the Azure ecosystem could be more seamless, especially compared to alternatives like Databricks.
• Browser Dependency: There is no dedicated tool for laptop-based work, with most users preferring an independent solution over browser-based operations.
• Analytics Capability: Expanded analytics capabilities are needed to meet evolving data management needs.
• Restricted Data Types: Certain data types and table DDL operations are limited and may not be fully supported or replicated within Synapse.
• Row Size Constraint: The size of rows in source tables is limited to 7,500 bytes, restricting the volume of data that can be accommodated.
• Table Limitation: A maximum of 5,000 tables can be added to a single link connection, limiting the scalability of table additions.
• System Tables Exclusion: System tables and security configurations are excluded from replication processes, affecting the comprehensiveness of data transfers. 
• SQL User Constraints: SQL users are restricted from performing administrative tasks, requiring the use of T-SQL for such operations.
• T-SQL Limitations: Several T-SQL functionalities are not yet supported, affecting the range of tasks that can be accomplished using this language within Synapse.

What is Snowflake?

A completely managed platform called Snowflake is intended for data science, data warehousing, lake management, and safe real-time information exchange.

Built on either Amazon Web Services or Microsoft Azure, Snowflake’s unique architecture separates storage and computing, allowing for independent scaling.

This cloud-based platform can handle large volumes of data and provides advanced analytics.

With built-in data-sharing and support for various integration tools, Snowflake offers a flexible, scalable solution, allowing users to focus more on data analysis rather than infrastructure management. 

🡆 Key Features

• Scalable computational capabilities.
• Easy data sharing.
• Streamlined data cloning.
• Compatibility with external tools, including various Azure products.

🡆 Pros of Snowflake

Snowflake is a robust cloud-based data warehousing solution with many features. Here are some key advantages:

• Data Science Capabilities: Snowflake excels in providing powerful tools for advanced analytics.
• User-Friendly Interface: The platform is easy to use and offers responsive technical support.
• Versatile ETL Provisions: Snowflake supports diverse ETL pipelines and offers adapters that evolve to meet changing data processing needs.
• Stability: Snowflake is known for its reliable performance in various data operations.
• SQL to NoSQL Translation: This feature allows seamless conversion of SQL workloads to NoSQL, enhancing data flexibility.
• Time Travel Feature: Users can access historical data, which is valuable for data exploration.
• Cloning External Tables: Snowflake allows the cloning of external tables for easy data replication and management.
• Innovative Functionality: Snowpiping, Time Travel, and Snowpipes are features that increase the platform’s overall functioning.

🡆 Cons of Snowflake

Even though Snowflake is a flexible data warehousing solution, there are a few things to think about:

• Integration Complexity: Better integration with tools like Liquibase is needed for seamless changes to the data warehouse model.
• Data Sharing Limitations: Improving data sharing across different business units can streamline collaboration.
• Machine Learning and AI Enhancement: Enhancing machine learning and AI capabilities to match industry standards.
• Operational Data Store (ODS) Space: There is a need to expand the ODS space within Snowflake.
• Cost Transparency: Clearer information about costs and pricing would help users manage resources effectively.
• Product Design Ambiguity: The design can be misunderstood, so clearer communication and user understanding are needed.
• Migration Challenges: Easier migration processes, especially for ODS features, would facilitate transitions from other platforms.
• OLTP Feature Gap: Adding OLTP features would address specific needs for instantaneous query response times.
• Python Integration Improvements: Enhancing integration with Python for a smoother experience for users who switch between SQL and Python within the platform.

Azure Synapse Analytics vs Snowflake: Key Differences

In “Azure Synapse Analytics vs Snowflake: Key Differences,” we compare features like data storage, handling, speed, protection, growth ability, connectivity, user experience, cost control, global availability, architecture, analytics, machine learning, and AI/ML compatibility.

Feature/Ability	Azure Synapse	Snowflake
Data Storage	Part of Azure’s system works with flexible and set-up resources	Cloud-based data storage system
Data Handling	All-in-one system for organizing and studying data	Concentrates on data storage, with separate tools for data organization
Speed	Made for big data analysis using set-up resources	Cloud-based structure for fast and efficient work
Protection	Uses Azure AD for user checking and role-based controls	Many layers of protection, including auto-encryption
Growth Ability	Adjusts computer power and storage as needed	Self-adjusts with independent computer and storage growth
Data Language	Works with SQL and Apache Spark	Uses SQL-type queries
Multiple Tasks	Handles many searches and separates workflows	Can link to many different platforms and systems
Connecting with Other Tools	Closely works with Azure products	Integration capabilities with various platforms and tools.
User Experience	Familiar setup for those who know Azure	Simple, easy-to-use interface
Cost Control	Use and pay as needed, with adjustable resource allocation	Pay based on usage, with separate charges for storage and processing
Worldwide Availability	Can be used in many Azure locations around the world	Supports multiple cloud providers globally
Architecture	One platform for cloud-based data analysis	Broad analytics service merging business data storage and big data study
Administration	Uses automation for ETL and ELT tasks	Provides a single system to manage all data-related tasks.
Built-In Data Analytics	Lets users query data right away	Changes data storage into a system for quick business insights and predictions
Machine Learning in Database	Supports creating predictive models within the system	Offers tools for gathering and interpreting data
Data Lake Analytics	Up-to-date data lake supporting Parquet file format	Uses Parquet files in Data Lake for better Polybase performance
Cloud Capabilities	Enables real-time data exchange on its cloud platform	Built-in, spread-out SQL processing system
AI/ML Compatibility	Works closely with Azure’s machine learning tools and Power BI	Provides automatic AI learning inputs and built-in AI business tools

Snowflake vs Azure Synapse Comparison on Various Parameters

Snowflake vs Azure Synapse comparison on various parameters depends on their architecture, PaaS vs. SaaS models, scalability, performance, integration, security and many more. We have written a detailed comparison to help businesses choose the most suitable data solution.

1. Architecture

Snowflake uses a cloud-native design that separates computing and storage, enabling independent scaling and cost efficiency. In contrast, Azure Synapse Analytics features a distributed architecture that integrates tightly with the Azure ecosystem, promoting seamless scalability and integration with Azure services.

Snowflake vs Azure Synapse is often viewed as superior in this regard due to its flexible separation of computing and storage, which enhances flexibility and cost management.

2. PaaS vs. SaaS

Snowflake runs as a SaaS platform across various cloud providers, with storage and compute credits segregated. Azure Synapse Analytics serves as a PaaS solution, providing a free Azure Synapse Workspace and data warehousing tools.

The choice between these platforms depends on user preferences, as both have distinct benefits. 

3. Scalability

Snowflake excels with automatic scalability via a multi-cluster architecture, ensuring optimal performance and virtually limitless storage.

Azure Synapse Analytics provides on-demand scalability with Data Warehouse Units (DWUs), offering flexibility based on workload needs. Snowflake vs Azure Synapse is favored for its seamless resource management and automatic scalability.

4. Performance

Snowflake makes use of its special architecture and optimization methods, such as instant cloning, to speed up query processing. Azure Synapse Analytics employs distributed query processing to deliver high-performance analytics, parallelizing operations across multiple compute resources.

The best choice depends on specific use cases and workload patterns, with neither platform emerging as a clear winner in performance.

5. Integration

Both platforms have robust integration capabilities with common data integration tools and BI platforms.

However, Azure Synapse Analytics stands out due to its close integration with the Azure ecosystem, supporting services like Azure Data Factory and Azure Analysis Services. In terms of integration, Azure Synapse Analytics is considered superior.

6. Security

Strong security features, such as access controls, encryption, and adherence to industry standards, are implemented by both platforms. There is no clear winner in security, as both Snowflake and Azure Synapse Analytics prioritize data protection.

7. Cost

Snowflake’s pricing approach is consumption-based, allowing users to pay according to how much storage and computing resources they consume.

Azure Synapse Analytics uses provisioned DWUs and storage use to determine the price. The decision between the two depends on factors like usage patterns, data volume, and specific requirements, making it difficult to declare a clear winner in terms of cost.

8. Data Cloud Management

Snowflake maintains data storage and access using SQL queries, preventing direct access to data objects. Azure Synapse Analytics, while also completely managed, provides users with greater control over data processing. The decision is based on the intended ratio of control to management.

9. Data Analytics

Advanced analytics are supported by Snowflake through platform integrations, which may come with extra charges. Azure Synapse Analytics provides a comprehensive set of data analytics tools without extra charges, including Azure Machine Learning, PowerBI, Azure Data Factory, and Synapse Studio. Azure Synapse vs Snowflake is regarded as superior because it provides a comprehensive analytics solution without additional costs.

What is AWS Redshift?

AWS Redshift is a cloud-based data storage service provided by Amazon. Through the use of SQL, users can query petabytes of structured and semi-structured data from operational databases, data lakes, and data warehouses.

Redshift competes with Snowflake and integrates smoothly with other AWS services, allowing you to save query results in open formats to S3. It offers multiple data import options and an easy setup process like other AWS services, ensuring data security through encryption.

Comparison of Cloud Data Warehouses: Azure Synapse vs AWS Redshift vs Snowflake

Parameter	Azure Synapse Analytics	AWS Redshift	Snowflake
Architecture	Distributed architecture integrated with the Azure ecosystem.	Columnar storage with parallel processing, tightly integrated with the AWS ecosystem.	Cloud-native architecture with separate computing and storage, providing independent scaling.
Service Model	PaaS (Platform as a Service)	PaaS (Platform as a Service)	SaaS (Software as a Service)
Scalability	On-demand scalability with Data Warehouse Units (DWUs).	Automatically scales with concurrency scaling clusters.	Automatic scalability through multi-cluster architecture, ensuring optimal performance.
Performance	High-performance analytics based on distributed query processing and parallel processes.	High performance with advanced query optimization and parallel execution.	Leverages instant cloning and optimization techniques for faster querying.
Integration	Seamless integration with Azure services like Data Factory, Analysis Services, and more.	Integrates well with AWS services like S3, Glue, and Athena.	Strong integration with various data integration tools and BI platforms.
Security	Strong security measures, such as encryption, access limits, and compliance.	Comprehensive security features including encryption, VPC, and compliance certifications.	Strong security measures include encryption, access controls, and adherence to industry regulations.
Cost Model	Price calculation combines provisioned DWUs and storage use.	Pay-as-you-go pricing with the opportunity to reserve instances to save money.	A consumption-based pricing approach in which you pay for the actual use of storage and computation resources.
Management	Offers more control over data processing, fully managed within the Azure ecosystem.	Managed by AWS, provides control over performance tuning and scaling.	Fully managed with limited direct access to data objects, SQL-based management.
Data Analytics	Includes tools like Azure Machine Learning, PowerBI, and Synapse Studio without extra cost.	Advanced analytics capabilities with integrations to AWS analytics tools like QuickSight.	Supports advanced analytics through various platform integrations, potentially incurring costs.

Conclusion

Choosing the right cloud application platform is crucial for leveraging vast amounts of data effectively. Azure Synapse and Snowflake both offer powerful, scalable solutions with unique strengths.

Azure Synapse excels in integration with the Azure ecosystem, providing comprehensive analytics tools and robust data management.

Snowflake stands out for its user-friendly interface, independent scaling of computing and storage, and versatile data handling capabilities.

AWS Redshift also presents a strong alternative, especially for users within the AWS environment.

Ultimately, the best choice depends on your specific business needs, existing infrastructure, and desired features.