Azure Synapse and Snowflake are both good ETL platforms, so how do you choose between them? See how their features are stacked up and which ones are more suitable for your use

Azure Synapse and Snowflake are two of the most commonly recommended ETL tools for businesses that need to process large amounts of data. Choosing between the two will depend on the unique strength of these services and the needs of your company These are the main 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 analysis service from Microsoft. It is part of the Azure platform, which includes products such as Azure Databricks, Cosmos DB and Power BI.

Microsoft describes it as “providing an integrated experience for receiving, exploring, preparing, converting, managing and serving data for instant BI and machine learning needs”. The service is one of the most popular tools available for data warehousing and big data system management.

Key features of Azure Synapse include:

• End-to-end cloud data warehousing
• Built-in governance tools
• Massive Parallel Processing (MPP)
• Non-stop integration with other Azure products

What is a snowflake?

Snowflake is another popular big data platform, developed by a company of the same name. It is a fully managed PaaS used for a variety of applications – including data warehousing, lake management, data science and secure exchange of real-time information.

A Snowflake data warehouse is built on either Amazon Web Services (AWS) or Microsoft Azure Cloud infrastructure. Cloud storage and computer power can scale independently.

Like most available data platforms, Snowflake is designed with the core trends in business intelligence automation in mind, including automation, segmentation of the intelligence workflow, and the increasing use of XaaS tools.

Key features of Snowflake’s platform include:

• Measurable computing
• Information exchange
• Data cloning
• Integration with third party tools, including many Azure products.

Like Synapse, Snowflake and an MPP platform.

Azure Synapse vs. Snowflake: How Platforms Compare

There are many similarities between the two ETL products, but they differ in terms of specific features, strengths, weaknesses and popular usage. Comparing the head between the two platforms, it becomes more clear which service is right for the business.

Use case and versatility

Synapse and Snowflake are both designed for data analysis and storage applications, but Snowflake is more suited to conventional business intelligence and analysis. It includes maintenance near zero with features like automatic clustering and performance optimization tools.

Businesses that use Snowflake for storage and analysis may not need a full-time administrator with in-depth experience with the platform.

Native integration with Spark Pool and Delta Lake makes Synapse a great choice for advanced big data applications, including AI, ML and data streaming. However, the platform will require much more work and attention from the business analytics team.

A Synapse administrator who is familiar with the platform and knows how to effectively manage the service is probably necessary for a business to fully benefit. The setup of the Synapse platform will also probably involve more than Snowflake, which means businesses may have to wait longer to see results.

Architecture

Snowflake is not designed to run on a specific architecture and will run on three main cloud platforms: AWS, Microsoft Azure’s Cloud Platform and Google Cloud.

A layer of abstraction separates snowflake storage and calculates credit from the actual cloud resources from the business’s preferred supplier.

Each virtual snowflake warehouse has its own unique compute cluster. They do not share resources – which means that the performance of one warehouse should not affect the performance of another.

In contrast, Azure Synapse is designed specifically for the Azure Cloud. It is designed from the ground up for integration with other Azure services. Snowflake will integrate with many of these services, but it lacks the capabilities that make the integration of Synapse with Azure so smooth.

Scalability

Snowflake has built-in auto-scaling capabilities and an auto-suspend feature that allows administrators to dynamically manage warehouse resources as they change their needs. It uses a billing model per second and can provide instant cost savings to be able to scale storage quickly and calculate up or down.

Snowflake’s zero-copy cloning feature allows administrators to create a copy of tables, schemas, and warehouses without duplicating actual data. This allows for greater scalability.

Azure also offers strong scalability but lacks some features that make snowflakes so flexible. Azure has automatic scaling of serverless SQL pools and spark pools by default. However, manual scaling is required for dedicated SQL pools.

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Which is right for your business: Azure Synapse or Snowflake?

A company that decides between Synapse and Snowflake is in a good position. Both platforms have excellent data storage and analytics services, with many features required for business intelligence and analytics workflow.

However, the two differ when it comes to specific strengths and standard uses. Snowflake is great for companies that want to analyze more traditional business intelligence and will benefit from excellent scalability.

The Azure Synapse has a steeper learning curve than the Snowflake, and scalability can be more challenging depending on the type of pool a business uses. However, it is an excellent choice for companies dealing with AI, ML and data streaming and will probably perform better than Snowflake for these applications.

Further comparison of data management solutions

For additional information, see Firebolt vs. Snowflake: Compare Data Warehousing Platform, Databrix vs. Snowflake: ETL Tool Comparison, Snowflake vs. Redshift: Data Warehousing Software Comparison and Dreaml.