Snowflake and Azure Synapse are two of the leading ETL (extract, transform, load) platforms that provide similar end-goals but have some distinctions worth noting. Working with a tech-agnostic partner like proSkale before selecting the most suitable platform for your specific scenario can make or break your cloud journey.
The main commonality between Snowflake and Azure Synapse is that they are both capable of supporting businesses that process large amounts of data. While on the surface they may seem interchangeable, each platform has different benefits that support different client needs. Additionally, the platforms’ service is fundamentally different: Snowflake is SaaS (Software as a Service) and runs on top of a major cloud platform, while Synapse is a Platform as a Service (PaaS) that comes with a development environment on top of the computing resources.
Azure Synapse (previously Azure SQL Data Warehouse) is a data analytics platform described as an “unified experience” that transforms ETL processes for immediate BI and Machine Learning needs. The Microsoft-developed product is amalgamated with the Azure platform, and comes with the development environment, meaning clients pay for Azure resources without the need for an abstraction layer. While having a steeper learning curve, Synapse has solidified itself as a leading tool in information warehousing and big data management.
Azure Synapse is known for:
- Integration with Azure Products
- End-to-end Cloud Data Warehousing
- Massively Parallel Processing (MPP)
- Built-in Data Governance
Snowflake is a Software as a Service (SaaS) that manages various applications like secure sharing, data warehousing, and lake management based on users’ needs. Their data warehouses scale computing power and storage independently, while being built on Amazon Web Services (AWS) or Microsoft Azure cloud infrastructure. With this, there is a layer of abstraction to diverge the aspects clients pay for, like storage and computing credits, from the host.
Snowflake is known for:
- Building their platforms with key trends (ex. automation)
- Increased use of Anything as a Service (XaaS)
- Integrating third party tools, including Azure products
- Data Sharing
- Data Cloning
- Massively Parallel Processing (MPP)
While the platforms at first glance share many of the same features, companies today have different requirements that may not align with one of the platforms. Snowflake and Azure Synapse offer individualized features that will benefit different types of businesses. There are some key differences in their function and cost structures:
The way that the platform interacts with your data, the cloud and analytical requirements is an important part of everyday business functions. For Snowflake and Synapse, platform functionality differs in the areas of scalability, versatility in business use, and architecture.
Due to its multi-cluster and shared data architecture, Snowflake is known to have premium scalability. In a shared data layer, users can simultaneously separate workloads, giving warehouses unlimited scalability and synchrony to quickly achieve business needs. Snowflake’s scalability has built-in features that can assist with lowering costs. The ability to auto-scale, auto-suspend, zero-copy, and quick-scale storage and computing power without duplicating data aids administrators in managing the evolving requirements of warehouse resources.
While the scalability in Synapse is comparable with auto-suspend and zero-copy features, it does not have the workability of Snowflake. Synapse offers both an SQL pool and Serverless SQLs. Pools have pre-defined Data Warehouse Units (DWU) of scale, while Serverless options have automatic scalability with larger capacity.
Versatility in Business Use
Snowflake is the clear winner when it comes to everyday business intelligence and analytical needs, while Synapse is better for advanced big data applications.
Snowflake is beneficial because with its near-zero maintenance, performance optimization, and automatic clustering, it can meet the needs for an assortment of storage applications and data analysis tools. This means that a full-time administrator with expertise on the platform may not be required.
Synapse is integrated with Spark Pool and Delta Lake that assists in AI, data streaming, and ML that are beneficial for advanced big data applications. This means there is a higher labor cost with increased immersion from business analytics, and a full-time administrator who has a deep understanding of the system.
Snowflake is versatile in its architecture and can run on AWS, Microsoft Azure and Google Cloud, depending on business needs. The customization goes deeper as each warehouse does not share resources as it runs on its own cluster, eliminating the ability for one warehouse to impact another.
Azure Synapse is part of the Azure ecosystem and only compatible with Azure Cloud, allowing for a seamless integration that cannot be fully duplicated by a third party like Snowflake.
The company resources that need to be allocated to implementing and maintaining the cloud are different between Snowflake and Synapse. This is seen in the four key areas of compute resources, payment structure, administrative overhead, and integration with Azure.
Both platforms are flexible in their compute resources requirements as users have the ability to create their own SQL databases but interact differently with the virtual equipment.
A result of Snowflake’s layer of abstraction is that the platform is disintegrated from the computing resources responsible for loading and querying. This allows for computing resources, known as warehouses in Snowflake, to be used on any of Snowflake’s SQL databases.
Synapse, on the other hand, requires a dedicated data warehouse to be created for each SQL database.
Both Snowflake and Synapse have different payment structures that fit within their platforms’ service structure.
Snowflake is popular for its pay-for-what-you-use structure that charges you for the computing you use on a per-second basis. The service also includes auto-suspend and auto-resume operations, limiting the amount of time you pay for computing that is not bringing value to your company.
Azure works on an hourly basis. If you compute for forty minutes, you will be charged for the hour; however, if you only use 5 hours in a month, that is all you will pay for.
Snowflake aims for its clients to have near-zero maintenance, meaning that companies who use this platform do not need full-time administrators with intense knowledge of the service. With its features like built-in performance optimization, automatic clustering, and materialized view maintenance, administrative overhead is significantly lowered.
There is more maintenance required for Synapse with managing operations, monitoring performance, and tuning. It is likely that companies will need more time dedicated to maintaining the cloud by someone with in-depth knowledge of the service.
Integration with Azure
While both platforms integrate with Azure, Synapse is part of the same product family, increasing the usability and enhancing specifications within the platform.
Limitations within the platforms
While both platforms are highly sought-after, they both have limitations that can be deal-breakers for a company and should be considered.
Cons of Azure Synapse
- High learning curve for new users
- Extensive set-up process
- No support for queries that cross multiple databases
Cons of Snowflake
- Unoptimized for use cases such as transactional processing
- Inefficient user interface
- Cost may be high for small companies
Both platforms have proven their excellence in the cloud space, and are good options to consider but have distinct strengths when it comes to supporting business needs. Defining current and future business needs, coupled with a deep understanding of how both technologies can be leveraged to your specific scenario is crucial. Snowflake is ideal for traditional business structures looking for flexibility in their analytics. Azure Synapse benefits companies who work with ML and Data Streaming, with a possible edge in these areas. Working with an experienced partner can mean more than the difference between success and failure; it’s a way to identify barriers to success and adoption as well as how they can be solved regardless of delivery model. Empowering your current team with the right frameworks and automation tools means that you can build your own solutions center with current resources instead of costly hiring and overruns.
Empowering your current team with the right frameworks and automation tools means that you can build your own solutions center with current resources instead of costly hiring and overruns.