With the world gearing to reach 175 ZB of data by 2025, many organizations have realized that moving their data to the cloud is the need of the hour. Not only does the cloud offer benefits in terms of scalability and reduced costs, but it also provides companies access to a host of data analytics and ETL (extract, transform, and load) tools, enabling them to process large amounts of information and extract meaningful insights from it.
Azure Synapse Analytics and Snowflake are two modern Cloud Data Platforms in the market that are helping companies collect and consolidate tonnes of data on a single platform to derive quick yet valuable conclusions from it.
Since these are two of the most popular cloud data warehouse platforms, organizations are always at a crossroads when choosing between the two. While Azure Synapse Analytics and Snowflake are the most recommended tools for businesses that need to process large amounts of data, key differences will help you differentiate between the two and choose the best for your company’s needs.
Before delving deeper into the features and areas of comparison between Azure Synapse Analytics and Snowflake, let’s first introduce each of these tools in detail.
Azure Synapse Analytics
Formerly known as Azure SQL Data Warehouse, and now as Azure Synapse, this data warehousing platform is Microsoft’s limitless data analytics service that encompasses enterprise data warehousing, data integration, and big data analytics, all under a single roof. An integral part of the Azure platform, Azure Synapse Analytics creates a unified environment to ingest, explore, transform, manage, prepare, and serve data for immediate business intelligence and machine learning requirements.
Key features of Azure Synapse include:
- End-to-end cloud data warehousing with limitless scale and blazing speed
- Built-in security and privacy features and governance tools
- Massively parallel processing (MPP)
- Seamless integration with other Azure products
Another popular big data platform, Snowflake, developed by a company of the same name, is a fully managed PaaS used for numerous applications like data warehousing, lake data science, and secure sharing of real-time information.
Built on either the Microsoft Azure cloud infrastructure or Amazon Web Services (AWS), the Snowflake data warehousing platform is created taking into cognizance the segmentation of intelligent workflows, automation, and increasing usage of XaaS tools in mind.
Key features of this platform are as follows:
- Scalable computing
- Data sharing
- Data cloning
- Massively parallel
- processing (MPP)
- Integration with third-party tools
Below, we conduct a head-to-head comparison of the two tools, which will offer deeper insight into specific features, pros, and weaknesses.
Azure Synapse Analytics vs. Snowflake: A Comparison
Snowflake better suits conventional business intelligence and analytics needs with near-zero maintenance, automatic clustering, and performance optimization.
Businesses that use Snowflake for storage and analysis may not require a full-time administrator with deep experience with the platform.
Although Azure Synapse requires an administrator who is familiar with the platform, native integration with Spark Pool and Delta Lake makes it the go-to option for big data applications, including AI, ML, and data streaming. In today’s highly digitized business environment, Azure Synapse is an excellent option for businesses looking to extract, transform, load, and process enormous amounts of big data.
2. PaaS vs. SaaS
While Snowflake is a SaaS (Software as a Service) platform that runs on top of Azure, Google Cloud, or Amazon Web Services (AWS) and leverages an abstraction layer to separate the Snowflake storage and compute credits that need to be paid for from the actual underlying compute cloud and storage, Azure Synapse is a PaaS (Platform as a Service) solution that offers a free Azure Synapse Workspace development environment over and above to the data warehousing resources. In short, other Azure functionalities like Azure Active Directory and Power BI are coupled alongside the cloud resources.
Compute usage on Synapse is calculated hourly, so if your data warehouse (DW) is active only for 12 hours in a month, you pay only for those 12 hours it existed. However, you pay for the hour if your DW was active for only 30 minutes.
On the flipside, Snowflake follows a pay-as-you-go billing mechanism, with a minimum time of 60 seconds. So, if your query takes 4 minutes to execute, you only end up paying for those 4 minutes, provided the data warehouse is suspended post-execution.
Designed to scale with the needs of the organization, Snowflake is perfect for small to midsize enterprises that have the exponential potential for further growth. Its multi-cluster, shared data environment allows different workloads to be isolated concurrently on a separate layer.
Azure Synapse, on the other hand, is designed for large organizations with Big Data loads (running in TBs). This DW offers a dedicated SQL pool with a pre-defined unit of scale (Data Warehouse Unit or DWU) and a serverless SQL pool that scales automatically to meet different scaling needs.
Confused? Here our are final thoughts
Choosing between the two data warehouse solutions can certainly be daunting for organizations.
At this time, it is best to allocate resources to develop proof-of-concept and compare both platforms in terms of which is better aligned to meet the organization’s vision and use case.
For larger organizations with a .NET or Azure-only environment, Azure Synapse takes the win as Synapse can make enhancements tailored towards better interoperability with the vast spectrum of Azure services, Power BI, and advanced analytics use cases leveraging Apache Spark.
For smaller organizations, or enterprises in need of a more diverse stack, or on a different cloud, Snowflake is an excellent option.
What’s more, Snowflake is now available on Microsoft Azure in over 20 regions across the globe, implying customers are now empowered with more integrated and seamless experiences, irrespective of cloud or region.