Companies that are just getting started with their data journey on Azure are debating which cloud data warehouse platform to adopt. Azure Synapse Analytics is default from the Azure stack, but there are always better options like whether a multi-cloud platform like Snowflake is a good match. And even if you are not using any cloud platform, Azure Synapse and Snowflake are two big names in the data warehouse industry.
Both Synapse and Snowflake provide MPP or Massively Parallel Processing to distribute data computation over several cloud nodes. Both systems also separate storage and computing, which allows for independent scalability of computing and storage capacity. Also, Synapse and Snowflake support SQL.
So for enterprises that need to handle significant volumes of data, Snowflake and Azure Synapse are two often suggested ETL tools. Choosing between these two will be determined by the different qualities of these services and the demands of your organization.
What is Azure Synapse?
Azure Synapse(previously known as Azure SQL Data Warehouse) is a PaaS (Platform-as-a-Service) data platform offered by Microsoft. Synapse Analytics is a data analytics solution by Azure that combines extensive data analysis and data storage.
For Business Intelligence and instant data prediction needs, Azure Synapse provides a single workload for all workloads throughout data processing. Integration with Power BI, Azure Data Factory, SQL DW Dedicated Pools, and Azure Machine Learning has made this feasible.
Using the Azure Data Lake, Synapse offers a master repository to store all types of data easily and quickly. All that remains is for you to upload your data to the lake and begin building your analytics on top of it.
Synapse provides both a serverless and dedicated SQL pool, allowing you to scale your compute capacity without consideration for storage capacity. A serverless SQL pool automatically grows to meet query requirements since it is serverless. In addition, it supports typical CSV files and allows user-controlled file selection. As a result, Synapse is 94% less expensive and 14 times quicker than other cloud providers.
What is Snowflake?
A snowflake is a cloud-based software SaaS(Software-as-a-Service) data warehouse solution. Snowflake stores all persistent data in a centralized data repository available to all compute nodes on the platform.
This data is subsequently processed utilizing Massively parallel processing(MPP) clusters referred to as data warehouses, with a portion of the data saved locally.
Once you load data in Snowflake, it automatically employs micro partitions to internally store, organize and optimize that data into compressed columnar storage as it is fed into the platform. This data is subsequently saved on the cloud, with Snowflake handling all aspects of files such as structure, compression, size, metadata, statistics, etc.
Snowflake’s warehouses each have their autonomous computing cluster. Virtual warehouses do not exchange resources with one another. This means that Snowflake can handle nearly infinite concurrency for queries and users. Furthermore, Snowflake is cloud-agnostic, running on all three significant clouds, AWS, GCP, and Azure.
Similarities between Azure Synapse Analytics and Snowflake
Both Azure Synapse Analytics and Snowflake are excellent cloud-based data warehousing platforms. Given its huge data requirements and high computational demands, such as nightly ETL operations, a data warehouse is a good use case for the cloud. Furthermore, both platforms have distinct computational and storage capabilities.
To reduce expenses while offering better performance when needed, compute resources may be scaled up, scaled down, started or halted, which applies to storage.
Both Azure Synapse and Snowflake build warehouses in relational SQL databases, using columnar storage behind the scenes to limit data size expansion while giving good performance. Furthermore, both platforms are accessible through several data visualization tools to provide insights to end-users.
Massively Parallel Processing
MPP distributes data computation over several cloud nodes. As a result, it can manage vast volumes of data and do considerably quicker analyses on large datasets.
Separate compute and storage
It allows for separate scalability of computing and storage compared to closely linked compute and storage in traditional platforms such as Hadoop.
Relational SQL databases
Both data warehouses employ the relational SQL database model and columnar storage to keep data size expansion to a minimum while providing good performance.
Both systems may be accessible using various data visualization tools like Tableau and Power BI to provide insights to end-users.
Both systems support the extraction and parsing of semi-structured files such as CSV and JSON.
Difference between Azure Synapse vs Snowflake
Azure Synapse is a platform as a service (PaaS) that includes a free Azure Synapse Workspace and various premium tools. The nice part about adopting this workaround is that other Azure services, like Power BI and Azure Active Directory, are tightly connected with Azure Synapse.
On the other hand, Snowflake is a SaaS (software as a service) that runs on top of major cloud providers, including GCP, AWS, and Azure. It employs an abstraction layer to isolate the storage and compute capabilities, and you pay for them.
Snowflake has auto-scaling functionality for virtual nodes. In addition, workloads may be segregated independently to offer infinite concurrency because each Snowflake warehouse is on its unique computing cluster.
Snowflake also provides a zero-copy cloning feature that allows users to clone databases immediately without physically copying or storing data. Furthermore, different workloads may be segregated concurrently on a standard data layer using Snowflake. As a result, it is possible to achieve infinite scaling and concurrency based on the computational demands by constructing various Virtual Warehouses.
Because Azure Synapse is not a native SaaS service, it offers fewer scalability options than Snowflake. For example, spark and Serverless SQL Pools have automatic scaling by default. In contrast, the user must manually modify Dedicated SQL server pools as it does not have any auto suspend and resume feature like Snowflake.
In addition, there are capacity constraints for each Azure Synapse instance; consequently, when these limits are surpassed, an organization may need to spin up and maintain several Azure Synapse Analytics services. Furthermore, Azure Synapse is primarily intended for Big Data loads; hence, Synapse is overkill for smaller enterprises with minor data sizes/query demands.
The main difference between Azure Synapse and Snowflake is how they approach computational resources. While both platforms support SQL databases for data warehousing, the way they interact with those databases via computational resources is different.
Snowflake has separated the SQL databases implying any computing resource known as the warehouse in the case of Snowflake may work on any SQL database in Snowflake. This method allows several computing resources to access the same database simultaneously.
For example, one computing resource might be querying the data, and another is loading data, with no worries about one process interfering. Snowflake compute resources can also automatically halt a resource after a period of inactivity.
When inquiries reappear, resources will restart. When someone forgets to switch off a tremendous computing resource utilized for ad hoc analysis, this can help keep inadvertent expenditure in check.
Azure Synapse compute resources work differently. That SQL database is inextricably linked to the SQL pool computing resource. Therefore, the same SQL database cannot be accessed by different SQL pools simultaneously.
Snowflake being a SaaS product aims for near-to-zero maintenance. It offers automated clustering, built-in speed optimization, and materialized view maintenance, eliminating the need for full-time infrastructure management and administration. At the same time, Azure Synapse Analytics requires more administration and infrastructure management.
Snowflake’s compute charging system is based on Pay-As-You-Go and, more specifically, on pay-per-second. The minimum period is 60 seconds, with the possibility to halt and restart activities automatically. So, if your query runs for 5 minutes, you will only be charged for 5 minutes. On the other side, Azure Synapse rates are calculated hourly. So, if your Synapse Warehouse is active for 10 hours, you will only be charged for the 10. Again, the minimum period is 1 hour.
Snowflake and Synapse automatically encrypt data at rest, including role-based access control to manage user permissions. Both solutions also provide vital security surrounding multi-factor authentication and VPN connection.
Both platforms, Snowflake and Synapse, provide robust analytics and data storage services. However, they differ in terms of unique strengths and optimal use cases. You can select a data warehouse solution depending on your needs, such as security, volume, and the sort of business you are conducting.
Amit Doshi is a Cloud Engineer who has experienced more than 5 years in AWS, Azure, and Google Cloud. He is an IT professional responsible for designing, implementing, managing, and maintaining cloud computing infrastructure, applications, and services.