Azure Synapse and Snowflake are both good ETL platforms, so how do you choose between them? See how their features stack up and which one is more suitable for your use cases.
Azure Synapse and Snowflake are two commonly recommended ETL tools for businesses that need to process large amounts of data. Choosing between the two will depend on the unique strengths of these services and your company’s needs. These are the key differences between Synapse and Snowflake, including their features and where they excel.
What is Azure Synapse?
Azure Synapse (formerly known as Azure SQL Data Warehouse) is a data analytics service from Microsoft. It’s part of the Azure platform, which includes products like Azure Databricks, Cosmos DB and Power BI.
Microsoft describes it as offering a “unified experience to ingest, explore, prepare, transform, manage, and serve data for immediate BI and machine learning needs.” The service is one of the most popular tools available for information warehousing and the management of big data systems.
Key features of Azure Synapse include:
- End-to-end cloud data warehousing
- Built-in governance tools
- Massively parallel processing (MPP)
- Seamless integration with other Azure products
What is Snowflake?
Snowflake is another popular big data platform, developed by a company of the same name. It’s a fully managed PaaS used for various applications — including data warehousing, lake management, data science and secure sharing of real-time information.
A Snowflake data warehouse is built on either the Amazon Web Services (AWS) or Microsoft Azure cloud infrastructure. Cloud storage and compute power can scale independently.
Like most available data platforms, Snowflake is built with key trends in business intelligence automation in mind, including automation, segmentation of intelligence workflows and growing use of XaaS tools.
Key features of Snowflake’s platform include:
- Scalable computing
- Data sharing
- Data cloning
- Integration with third-party tools, including many Azure products.
Like Synapse, Snowflake is also an MPP platform.
Azure Synapse vs Snowflake: How the platforms compare
The two ETL products have a lot in common, but they differ in specific features offered, strengths, weaknesses and popular use cases. In a head-to-head comparison of the two platforms, it becomes more obvious which service is right for a business.
Use cases and versatility
Synapse and Snowflake are both built for a range of data analysis and storage applications, but Snowflake is a better fit for conventional business intelligence and analytics. It includes near-zero maintenance with features like automatic clustering and performance optimization tools.
Businesses that use Snowflake for storage and analysis may not need a full-time administrator who has deep experience with the platform.
Native integration with Spark Pool and Delta Lake makes Synapse an excellent choice for advanced big data applications, including AI, ML and data streaming. However, the platform will require much more labor and attention from the business’s analytics team.
A Synapse administrator who is familiar with the platform and knows how to effectively manage the service will likely be necessary for a business to benefit fully. Setup of the Synapse platform will also likely be more involved than for Snowflake, meaning businesses may need to wait longer to see results.
Snowflake is not built to run on a specific architecture and will run on top of three major cloud platforms: AWS, Microsoft Azure’s cloud platform and Google Cloud.
A layer of abstraction separates the Snowflake storage and compute credits from the actual cloud resources from a business’s provider of choice.
Each virtual Snowflake warehouse has its own independent compute cluster. They do not share resources — meaning that the performance of one warehouse shouldn’t impact the performance of another.
By contrast, Azure Synapse is built specifically for Azure Cloud. It is designed from the ground up for integration with other Azure services. Snowflake will also integrate with many of these services, but it lacks some of the capabilities that make Synapse’s integration with Azure so seamless.
Snowflake has built-in auto-scaling capabilities and an auto-suspend feature that will allow administrators to dynamically manage warehouse resources as their needs change. It uses a per-second billing model, and being able to quickly scale storage and compute up or down can provide immediate cost savings.
The zero-copy cloning feature from Snowflake also allows administrators to create a copy of tables, schemas and warehouses without duplicating the actual data. This allows for even greater scalability.
Azure also offers strong scalability but lacks some of the features that make Snowflake so flexible. Serverless SQL Pools and Spark Pools in Azure have automatic scaling by default. However, Dedicated SQL Pools require manual scaling.
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Which is right for your business: Azure Synapse or Snowflake?
A company deciding between Synapse and Snowflake is in a good position. Both platforms are excellent data storage and analysis services, with features necessary for many business intelligence and analysis workflows.
However, the two do differ when it comes to specific strengths and ideal use cases. Snowflake excels for companies that want to perform more traditional business intelligence analytics and will benefit from excellent scalability.
Azure Synapse has a steeper learning curve than Snowflake and scalability may be more challenging, depending on the type of Pool a business uses. However, it’s an excellent choice for companies working with AI, ML and data streaming and will likely perform better than Snowflake for these applications.
More comparisons of data management solutions
For additional information, see Firebolt vs Snowflake: Compare data warehousing platforms, Databricks vs Snowflake: ETL tool comparison, Snowflake vs AWS Redshift: Data warehousing software comparison and Dremio vs Snowflake: Comparing two of the best ETL tools.