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Data Warehousing

Azure Synapse vs. Snowflake: Which MPP platform is right for you?

By , September 29, 2021October 15th, 2021No Comments

When organizations need a new data warehouse or a platform dedicated to analytics, most start their search with Azure Synapse and Snowflake. These two services offer Massively Parallel Processing (MPP) that allows for easy distribution of data computation across the cloud nodes.

It’s no surprise that these two options are often compared. They are both market leaders, continue to grow at an accelerated pace, and are always evolving. Optimizing a tech stack has never been easier, but which of the two MPPs is best for you?

Although there are many similarities between the two, there are some clear differences when it comes to their architecture.

Comparing the Cost

Azure Synapse and Snowflake have very different payment structures and prices. These days, cloud data warehouses are extremely accessible. Start-up costs are low, and operational spending is minimal.

Azure Synapse calls itself a limitless analytics service. For the price point, it gives businesses plenty of freedom to query data. Sorting that data comes with a charge of around $122 per TB of processed data. However, the cost of data storage also includes several days worth of snapshot storage.

Synapse also uses its own pricing method for compute resources. This is on a sliding scale that starts at $1.20 for 100 DWUs and goes up to $360 per hour for 30,000 DWUs.

Snowflake is priced slightly differently. It comes with three layers of data warehousing: storage, cloud services, and virtual warehouses. Snowflake charges customers according to how much they use each service and for how long. So if a company uses Snowflake then they will pay a monthly fee for their data storage, but can also earn credits to spend on virtual warehousing.

If managing costs is a priority, Azure Synapse offers a more transparent pricing structure. Users can choose between on-demand pricing, but can also pre-purchase data storage to earn a discount. Snowflake also delivers on-demand pricing and storage capacity but separates the charges for compute time.

Scaling and Brand Growth

While Azure Synapse and Snowflake both advertise themselves as elastic data warehousing solutions, their scalability isn’t quite the same.

Snowflake certainly stands out when it comes to scalability. The use of a multi-cluster and shared data architecture means that different workloads can be run alongside each other, yet remain isolated. Virtual warehousing means what amounts to unlimited scale.

The result is that Snowflake is ideally suited for smaller brands that are expecting rapid scaling. It’s been designed for that purpose and does it extremely well.

Synapse uses automatic scaling, but there are limits to the available capacity. Designed more for larger data loads (think TBs and higher), SMEs may find that Azure Synapse is simply too much power for their needs.

Admin and Management

The goal of Snowflake has always been zero maintenance. That’s great news for data managers because there’s simply no need to hire or train a dedicated Snowflake account manager. It’s extremely easy to use and thrives on automated solutions.

Azure Synapse will tend to require a lot more from the admin side of things. Performance monitoring is not automated, and everything from tuning and concurrency management will need to be managed by someone.

Snowflake is the stand-out winner when it comes to administration, as it is as close to hands-free as it can be.

Capability in the Azure Stack

As you might expect, Azure Synapse stands out from the crowd when it comes to Azure service integration. Power BI, Azure Databricks, and Azure Data Factory services all allow Synapse to shine. However, Snowflake is no slouch.

Not only does Snowflake work extremely well with the Azure stack, but it’s also cloud-agnostic. That means you’re not limited to Azure services alone. Snowflake integrates seamlessly with almost all of the big-name cloud providers and third-party resources.

Although Synapse edges over Snowflake for Azure compatibility, the dividing line between them is extremely narrow. On the surface, the two are close competitors. Scratch beneath the surface and you find that there is an established partnership between Azure and Snowflake.

If you’re choosing between the two based on how well they integrate with Microsoft data services, then Synapse just about takes the lead, but it’s all very close.

Performance and Future-Proofing

This is another area where the similarities between Azure Synapse and Snowflake make choosing between the two extremely difficult. They are both extremely fast, bleeding-edge MPPs that deliver real-time data access.

Both Synapse and Snowflake continue to evolve, but neither has forgotten the basic demands of the cloud. Both have worked hard to set the standard for data warehousing.

Azure Synapse – Overview

Synapse is outstanding when it comes to handling unstructured data. Using the Azure Data Lake, Synapse offers an easy-to-use master repository for all variations of data types. All you have to do is upload your data to the lake and start building your analytics over the top of that data.

Synaps offers a dedicated SQL pool and a serverless SQL pool. This allows you to scale compute capabilities independently of your storage. For a dedicated SQL pool, the unit of scale is an abstraction of compute power that is known as a data warehouse unit. For a serverless SQL pool, being serverless, scaling is done automatically to accommodate query resource requirements. https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/overview-architecture

Thanks to the fact that it’s built-in to the Azure cloud means it comes with excellent architecture, VMs, multiple integrations, the security you’d expect, and a network that can handle massive data loads. The addition of the Azure AI toolset makes it the top choice for larger organizations that are aiming to be more advanced with their technologies.

Snowflake – Overview

Snowflake is considerably easier to set up and use, and the addition of zero-copy cloning is always useful. Users can easily use virtual warehouse copies. That’s good news if multiple teams are using the same data sets.

It’s the automation that really makes Snowflake stand out. Database optimization, partitioning, indexing, etc, can all be automated. That means a lot less time wasted on admin for data warehousing. Snowflake also benefits from a usage-based price structure. If you don’t use Snowflake, then you aren’t going to be charged.

Summary

There’s very little difference between the two biggest MPPs on the market. Both are fully optimized, straightforward to use, and capable of massive workloads.

Smaller businesses that are expecting high levels of growth should consider Snowflake before Azure Synapse. It’s designed for easier scaling, which can save a lot of time and work. For larger organizations, Azure Synapse is what you should be looking at more closely.

Authors

  • 10 Years of US-based consulting success in Data Warehousing, BI, and Advanced Analytics | Ian has extensive experience in finance, operations, and risk management with an emphasis on the use of analytics, information design, and information technology to achieve results and minimize business risk. |

  • Founder of Waterloo Data | 20+ Years of US-based consulting success in Data Warehousing, BI, and Data Science |