Snowflake Vs Azure

Realizing the benefits of storing their data on the cloud, various organizations transfer their data into the cloud. At the same time, some of the organizations are in a dilemma in making a decision about a cloud data platform. Popular Cloud Data Solutions accessible for people and organizations today are Snowflake and Azure Synapse. Both of them provide parallel processing capabilities for distributing data analysis across numerous cloud nodes. However, they have many differences that may assist you in deciding which one is right for your business needs. Through this blog, let us compare Snowflake and Azure.

Snowflake Vs Azure - Table of Content

What is Snowflake?

Snowflake is a cloud-based data warehouse tool which provides businesses with scalable and flexible Storage while hosting BI solutions simultaneously. It secures and protects data using SSO tokens, Amazon S3 policy controls, Google Cloud storage access permissions and Azure SAS. Alternatively, you can customize your Storage according to your storage needs.

With the cloud's elastic nature, we can customize our virtual warehouse to leverage additional computing resources to perform a large number of queries or load data more quickly. It has a multi-cluster architecture which handles concurrency issues such as delays and failures. The Snowflake architecture allows businesses to leverage it to share data seamlessly with any data consumer. Using Snowflake, we can combine structured and semi-structured data for analysis and load it into a database without being transformed or converted into a fixed relational schema in advance.

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What is Azure Synapse Analytics?

It is a cloud data warehousing solution offered by Microsoft. This is another version of ‘Azure SQL Data Warehouse’. Besides providing all the SQL Data Warehouse's features and technology, Azure Synapse integrates business intelligence, machine learning as well as data analysis tools for both non-relational and relational data.

Azure Synapse Analytics delivers complete standard CSV support for user-controlled file selection. It lets you increase Data Lakes with Event Hubs and IoT to stream across a centralized platform. Compared to other cloud suppliers, Azure Synapse Analytics would be 14 times quicker and would cost 94% less.

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Some of the key differences between Snowflake and Azure Synapse are: 


PaaS vs SaaS:

One of the main differences between Snowflake and Azure Synapse is that they are sold in different ways. Snowflake comes like SaaS that works on AWS, Azure or Google clouds. Snowflake's Compute credit and Storage are separated by the abstract layer. We can pay for the actual underlying Storage and calculate the resources and costs provided by the cloud. This approach allows the same snowflake experience to operate over one of the top 3 cloud providers.

Azure Synapse is mainly a Platform as a Service solution with a complimentary Azure Synapse Workspace development environment in addition to these resources. Eventually, you pay for Azure resources. Other Azure resources like Power BI and Azure Active Directory are tightly paired when using Azure Synapse for data warehousing.

Compute Resources: 

The most important difference between both platforms lies in their approach to computer resources. Although both platforms allow SQL databases creation for data storage, the way they operate on this database using computer resources is unique. Snowflake has fully decoupled SQL databases that were created in Snowflake from the computational resources which load or query these SQL databases. That is, any computational resource can operate on any SQL Database. This approach makes it possible for several compute resources simultaneously to use the same database.

A different approach is adopted by Azure Synapse to computing power. A dedicated SQL pool is needed to build a long-term SQL database adapted to data warehousing. This SQL database is closely connected to the computational resource dedicated to the SQL pool. The SQL pool that is dedicated must be under execution to access the associated SQL database. More than one SQL pool may not access the same SQL database simultaneously. Instead, Azure Synapse introduces a massively parallel processing engine model which distributes the SQL commands over a variety of compute nodes according to the performance level of the selected SQL pool. Dedicated SQL pools may be restarted or paused, but it is actually an API-based or manual operation.

Cost: 

Snowflake has a mechanism called "Pay-As-You-go" for the calculation that is calculated per second. The minimum duration is 60 seconds, with the possibility to suspend and resume operations automatically. Thus, if your query runs for 5 minutes, you will have to pay for 5 minutes only if the virtual data warehouse is suspended after the request is executed.

In contrast, Synapse costs are calculated on an hourly basis. For example, if your data warehouse is active for only 12 hours a month, then you will pay only for the 12 hours for which the data warehouse was available. And if the data warehouse is only active for just 30 minutes, then you will be charged for 1 hour.

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Scalability:

When concerned about Scalability, Snowflake Shines. That's because it has a multi-cluster shared data architecture. Snowflake enables you to isolate various workloads simultaneously into a shared data layer. We can also build virtual warehouses for unlimited range and concurrency and meet your computing needs without downtime.

Synapse delivers both a dedicated SQL pool as well as server-free SQL options. The first has a defined scale unit, while the second automatically adapts to your scale requirements.

Administration: 

Snowflake is a SaaS platform with the objective of achieving virtually zero maintenance. Organizations do not need full-time Snowflake administrators, as Snowflake Cloud Services offers features like automatic clustering, materialized view maintenance and integrated performance optimization. With Azure Synapse, there is a need for more management around Concurrency management and monitoring the performance and tuning.

Interoperability with Azure Stack:

Both the platforms fit nicely with Azure services like Azure Databricks, Azure Data Factory and Power BI. But, Azure Synapse Analytics will shine in this field because it has improvements that aim to improve interoperability across the Azure platform.

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Azure Synapse & Snowflake limitations:


The drawbacks of Azure Synapse include:

  • A long installation process is being carried out.
  • Azure Synapse will not support queries to be executed between databases.
  • A longer learning curve if you don't have the habit of using it.

The drawbacks of Snowflake include:

  • Snowflake may be tough to use for beginners. It also lacks a user-friendly interface.
  • The majority of users and small businesses think Snowflake is overpriced for them.
  • It is inappropriate in some cases to use databases like online transactional processing scenarios.

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 Conclusion:

In this blog, we have compared the differences between Snowflake and Azure Synapse. These differences are illustrated by service mode, computing resources, administration, Cost, scalability, and interoperability with the Azure stack. We have ended up by discussing the Limitations of Snowflake and Azure Synapse. I hope you found the blog valuable. If you have questions related to Snowflake and Azure Synapse comparison, you can comment on it in the comment section.

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Manikanth
Manikanth
Research Analyst
As a Senior Writer for HKR Trainings, Sai Manikanth has a great understanding of today’s data-driven environment, which includes key aspects such as Business Intelligence and data management. He manages the task of creating great content in the areas of Digital Marketing, Content Management, Project Management & Methodologies, Product Lifecycle Management Tools. Connect with him on LinkedIn and Twitter.