Azure Synapse Analytics vs. Snowflake—A Crossroads Meetup for Cloud Migration

Written by Imran Abdul Rauf

Technical Content Writer

The global big data and business analytics was valued at $198.08 billion last year, while the worth is expected to reach a staggering $684.12 billion by 2030.

The ever increasing adoption of big data analytics tools in the digital space is highly responsible for this humongous growth. And the two protagonists of this discussion, Azure Synapse and Snowflake, are a mighty part of this growth trend.

Azure Synapse & Snowflake

Both Azure Synapse and Snowflake Analytics offer similar processing tendencies in order to distribute data across the cloud. Still, there are a number of considerable differences to help you acknowledge the right choice for your business’s specific needs.

This is a brief introduction about the two platforms and then it’ll proceed on to the comparison details.

What is Azure Synapse?

Azure Synapse is a data analytics platform that offers a single workload for all workloads during data processing. The service works with the primary aim to provide instant data prediction and business intelligence prompts.

This has been achieved by merging Azure Machine Learning with Power BI alongside the assistance of Azure Synapse to integrate mathematical machine learning models through the ONNX format. A noticeable prospect of using Azure Synapse is that the platform gives its users the liberty to query large amounts of data either by providing resources at scale, or on-demand, serverless for data exploration and ad hoc purposes.

What is Snowflake?

Snowflake is a DWaaS (data warehouse as a service) platform with an architecture different from that of Amazon Redshift as it leverages scalable, elastic Azure Blobs and Azure Data Lake—both are data storage and analytics features of Microsoft Azure. The Blob and Data Lake features are used for internal storage and to store structured, unstructured, and on-premise data, respectively, through the Azure Data Factory.

Snowflake, just like its counterpart Azure Synapse, offers a plethora of benefits from scaling virtual storehouses to compute extra resources for a large number of queries. This is done to seamlessly share data with any data consumer, and everything in between.

Azure Synapse vs. Snowflake

When we pit both the platforms against each other to decide which service provider to choose for cloud migration, each comes with its own pros and cons. The discussion makes comparisons in terms of PaaS vs. SaaS, compute resources, costing, scalability, administration, and interoperability with Azure Stack.

PaaS vs. SaaS

One of the common differences in observance is that both Azure Synapse and Snowflake are presented and marketed in different ways. Azure Synapse is a PaaS (platform as a service) that is acquired with a free Azure Synapse Workspace development environment, besides other paid resources. The best thing about using this workaround is that the other Azure resources, i.e., Azure Active Directory and Power BI, are closely integrated alongside using Azure Synapse.

While Snowflake is a SaaS (software as a service) that performs on top of Google Clouds, Azure, or AWS. The user is required to pay for the Snowflake storage and credits, and an abstraction layer is added to separate both from the already underlined compute cloud and storage. The experience of using Snowflake comes in equal measures on top of the other services providers mentioned. Moreover, locating a Snowflake user through huge amounts of cloud data limits bandwidth costs.

Compute Resources

Apparently, each platform has its own way to compute resources. Both allow their users to create SQL databases for Data Warehousing, however, act differently on those compute resources. Azure Synapse needs a dedicated SQL pool to create a lasting database that is an appropriate fit for Data Warehousing.

While SQL databases in Snowflake are entirely decoupled from the compute resources that query or load those databases. In doing so, you can use any compute resource, or multiple compute resources can be used on any SQL database in Snowflake. Secondly, you can auto-pause a resource after a certain interval of inactivity, and the resources will resume once the queries load again.


Azure Synapse charges on hourly basis, for example, if a Data Warehouse is active for only 10 hours in a month, the user will pay for only 10 hours on which the Data Warehouse was active. But if the system is active for only 30 minutes, the user will be required to pay for 1 hour.

Snowflake comes with a pay-as-you-go approach for computing which is calculated per second. The minimum threshold is 60 seconds with an option for auto-suspend and auto-resume operations. For example, if a query runs for 3 minutes, the user will be asked to pay for only 3 minutes if the virtual Data Warehouse is suspended after the query is run.


Synapse provides both serverless SQL options and a dedicated SQL pool. The dedicated SQL pool comes with a predefined unit of scale, a.k.a. Data Warehousing Unit, and the other option automatically scales to meet the user’s scaling needs.

Snowflake has an edge over Synapse in terms of scalability, mainly due to its multi-cluster, shared data architecture. Users can isolate various workloads in a shared data layer at the same time, moreover, they can also create Virtual Warehouses for limitless scale and concurrency and complete their computing efforts without any downtime.


Azure Synapse requires a considerable administration work revolving performance monitoring, concurrency management, and tuning for indexes, distribution keys, and caching and partition.

On the other hand, Snowflake is a SaaS tool striving for near-zero maintenance. If your company has a Snowflake account, you won’t need to hire full-time administrators as Snowflake Cloud Services offer quality built-in performance optimization, automatic clustering, and materialized view maintenance.

Interoperability with Azure Stack

Both Synapse and Snowflake integrate comfortably with different Azure services including Data Factory, Databricks, and Power BI. Still, in this aspect it is Synapse which wins as it is incorporated with features which are better suited for enhancing interoperability with the Azure platform.

Final Thoughts

The comparisons involving PaaS vs. SaaS, compute resources, costing, scalability, administration, and interoperability with Azure Stack are the 6 key aspects in the cloud business. Both Synapse and Snowflake will continue serving their respective market share, clients, and industries in the cloud sector.

How Can Royal Cyber Help You?

Royal Cyber is a digital transformation company offering data analytics and governance expertise as one of its flagship services and is a certified partner with Collibra, Informatica, Databricks, and Microsoft Gold Certified Partner.

We help clients make smart, informed decisions based on powerful data analytics. Connect with us at [email protected], and learn how our data-driven optimization and approach can boost your business, help you stay complaint, and make value-added decisions.

Leave a Reply