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Data analytics and data management have become more important than ever in the modern business world. But with the volume of data to be analyzed steadily rising, organizations need a way to corral all of that data in one place, where it is ripe for analysis.
Enter modern cloud-based data warehouses and data management platforms such as Microsoft Azure Synapse and Snowflake. Both are well-respected data warehousing platforms and are well-rated by users on Gartner Peer Reviews. They each provide the volume, speed, and quality demanded by business intelligence and analytics applications.
But there are as many similarities as there are differences. In many cases, the choice between using Microsoft Azure Synapse and Snowflake boils down to the specific needs of the data environment. Let’s examine them both and see who comes out ahead.
Azure Synapse vs. Snowflake: Key Features
Azure Synapse
Azure Synapse, formerly the Microsoft Azure SQL Data Warehouse, is built on a strong SQL foundation and seeks to be a unified data analytics platform for big data systems and data warehouses.
Azure Synapse’s massive parallel processing architecture is designed so that its rapid processing is not wholly reliant on expensive memory. It achieves this by using clustered and non-clustered column store indexes and segments that make it easier to determine where data is stored and how it is distributed.
Snowflake
Snowflake is a relational database management system and analytics data warehouse for structured and semi-structured data. Offered via a software-as-a-service (SaaS) model, it also uses an SQL database engine to manage how information is stored in the database and process queries against virtual warehouses within the overall warehouse, each one in its own cluster node independent of others and not sharing compute resources.
Sitting on top of that are cloud services for authentication, infrastructure management, queries, and access controls. The Snowflake Elastic Data Warehouse enables users to analyze and store data utilizing Amazon S3 or Azure resources.
Which Is Best for Its Features?
This one is close, as both are top performers. For those wanting a top-class data warehouse for analytics, Snowflake narrowly wins overall. But Azure users happy to work with Power BI will find Azure Synapse a smart choice.
To learn more, also see: Top Business Intelligence Software
Azure Synapse vs. Snowflake: Support and Ease of Use
Azure Synapse
Synapse’s reliance on SQL and Azure offers familiarity to the many people and developers who use those platforms around the world. For them, it is easy to use. In many cases, little or no training will be required.
However, those familiar with the SQL databases sometimes complain that some SQL syntax features are not available and that there are no deduplication features on table storage as well as there being no conversion tools for code.
Snowflake
The Snowflake platform is said to be user-friendly with an intuitive SQL interface that makes it easy to get it set up and running. And with 24/7 live support, Snowflake users can get assistance with any issues they may face.Snowflake automates data vacuuming, compression, diagnosis, and other features. There is also no need to copy data during scale up operations with Snowflake. In addition, Snowflake supports structured and semi-structured data.
Some users, though, state that a lack of flexibility in areas such as resizing can lead to extra expense and long hours of maintenance. And documentation is not always as thorough as it could be. And perhaps the biggest negative is lack of out-of-the-box analytics capabilities.
Which Is Best for Support and Ease of Use?
For ease of use, Azure Synapse wins, although Snowflake isn’t far behind.
Azure Synapse vs. Snowflake: Security
Azure Synapse
Azure Synapse offers data protection, access control, authentication, network security, and threat protection to help security teams identify unusual access locations, SQL injection attacks, authentication attacks, and more. Further security features include component isolation limits.
Snowflake
Snowflake boasts always-on encryption, along with network isolation and other robust security features.
Its security features come in tiers, and each higher tier costs more. But on the plus side, you don’t end up paying for security features you don’t need or want.
Which Is Best for Security?
Due to security being fully packaged within Synapse at no extra cost, Azure Synapse wins. While Snowflake offers a variety of security features useful for businesses looking to protect their data and data warehouses, many of its features are locked behind more expensive pricing tiers, which can be cost prohibitive compared to Synapse’s built-in security features.
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Azure Synapse vs. Snowflake: Integration
Azure Synapse
Microsoft has taken its traditional Azure SQL Data Warehouse and baked in integration components such as Data Factory for efforts to outcomes (ETO) and extract, load, transform (ELT) data movement as well as Power BI for analytics. Synapse even features Spark components such as Azure Spark Pools in order to run notebooks.
Moreover, Synapse works seamlessly with all of the other Azure tools. Its Purview data cataloging system, for example, is used for data governance. This makes it easy to transform, curate, and cleanse data before it is distributed to other users for analytics. Purview also makes it relatively simple to track data lineage, refer to schema of tables, and track data movement through the system.
Snowflake
Snowflake is on the AWS Marketplace but is not tightly integrated. In some cases, users comment that it can be challenging to integrate Snowflake with other tools. But in other cases, Snowflake integrates well with applications such as Tableau, Apache Spark, IBM Cognos, and Qlik. Those using these tools will find analysis easy to accomplish.
Which Is Best for Integration?
If you live within the Azure universe, it is hard to argue with the level of integration. Power BI, for example, is right there for use in analytics with almost no work at all. Azure Synapse wins unless you are on those specific applications that Snowflake especially caters too.
Azure Synapse vs. Snowflake: Pricing
Azure Synapse
When it comes to Azure Synapse, things get a little complex. It is charged according to:
- The number of data warehouse blocks and the number of hours running.
- The amount of terabytes of data stored and processed.
- The number of instances of Apache Spark Pool running and the number of hours.
- The volume of orchestration activity runs, data movement, runtime, and cores used in data flow execution and debugging.
These are all gathered together into something called synapse commit units (SCUs), or “the amount of data that is replicated from the source database to Azure Synapse Analytics,” which can be pre-purchased according to a tiered structure. But it remains complex.
According to Azure, “The number of SCUs used is based on the amount of data that is replicated and the frequency of replication. In general, the more data you ingest and store, the more SCUs you will need and the higher the cost of using Azure Synapse Analytics will be.”
Snowflake
Snowflake keeps compute and storage separate within its pricing structure. It provides concurrency scaling automatically with all editions at no extra cost. Snowflake pricing, however, is a little complex with several editions from Basic on up, and prices rise as you move up the tiers. Roughly speaking, Snowflake is about $40 a month.
Which Is Best Based on Pricing?
The differences between Azure Synapse and Snowflake makes it difficult to do a full apples-to-apples comparison. But due to the fact that its pricing scheme is a little less complex, Snowflake wins. That said, if an analytics platform has to be purchased as part of the deployment, Azure Synapse wins, as Power BI is thrown in for free.
Since pricing varies from use case to use case, users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and their analysis requirements. For some users, Snowflake will be cheaper; for others, Azure Synapse will come out ahead.
For more information, also see: Data Mining Techniques
Azure Synapse and Snowflake Alternatives
Choosing Between Azure Synapse and Snowflake for Data Management
Azure Synapse and Snowflake are excellent data warehouses and data management platforms that facilitate the retention and analysis of data.
In some cases, Snowflake’s ability to split compute and storage pricing makes it more malleable to different use cases that lie outside of the open-source and developer field. Developers, too, may prefer it, depending on the platforms they utilize. Organizations working closely with Tableau, Apache Spark, IBM Cognos, and Qlik, for example, may prefer Snowflake due to its focus on those tools and platforms.
Azure Synapse, though, is highly suited to data analysis for those users familiar with SQL and operating within the Azure ecosystem. With Power BI thrown in for free to Azure and Microsoft 365 users, though, it can be tough to beat the terms offered by Microsoft in overall pricing and deep packaging discounts.
In summary, Azure Synapse probably wins for a less technical user base. Azure Synapse is better set up for users that just want to deploy a good data warehouse and analytics tool rapidly without being bogged down by configurations, data science minutia, or manual setup. Yet, it can’t be classified as a light tool or for beginners only. Far from it.
Snowflake wins for more sophisticated and higher-end users—and when the application mix within the organization favors its integration offerings. Snowflake garnered a slightly higher rating on Gartner Peer Reviews, too. Scalability and customization were further areas where users rated Snowflake higher than Azure Synapse. For those operating within the Azure ecosystem, that shouldn’t be an issue. However, for anyone building a large-scale data warehouse and data management platform, scalability and customization limitations have to be considered.
As usual, comparison between such tools comes down to user preference for platform, programming language, and existing investment in vendor platforms.
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