Minimal cost even for massive workload
many cloud data warehousing platforms offer the 'pay per use' model. In this method, you pay for space your data occupy, which includes both the storage space and computing space. But the differentiator with Snowflake's model is that you can scale up or down right before and after you complete computing. Therefore, data storage, along with the size of your computing by the second, will be included for the charges. You don't have to pay for the integrated bundle of the storage plus computing. This is a massive advantage over the other cloud data warehouse that combines the sizes of both.
Simple, Direct Architecture
Snowflake, a data cloud platform that allows businesses to consolidate their data onto one platform, was officially launched on Amazon Web Services in India in May 2020. Since then, it has on-boarded Swiggy, epiFi, Freight Tiger and Urban Company, to mention a few. Snowflake is built on AWS cloud infrastructure and is offered as a SaaS product. There is no need to intall and configure any hardware or software (even the virtual ones). Snowflake implementation is further simplified with its three-layer architecture that comprises database storage, query processing and cloud. You can experience superior durability in the storing of data.
External Data Sharing
One other advantage for the separate storage and computing in Snowflake is the ability to share the data with external vendors, even if they are not using it. Snowflake creates a pointer, and you can share this pointer for people to access the data outside the organization without risking it securely.
Multi-Cluster Systems
Query processing in Snowflake is up to the mark. You don't have to create multiple clusters to duplicate for the testing and development. You can create a test environment when you need it, point at the data and run the tests. It's a simple process that lets you focus on data analytics instead of worrying about working with the platform.