Snowflake has proven to be a robust SaaS big data storage, warehouse or lake solution for many of our clients. It has a superior architecture to scale storage, compute, and services to load, unload and use data. It's also very flexible: it supports storage of structured, semi-structured and unstructured data; provides a growing list of connectors for different access patterns such as Spark for data science and SQL for analytics; and runs on multiple cloud providers. Our advice to many of our clients is to use managed services for their utility technology such as big data storage; however, if the risk and regulations prohibit the use of managed services, then Snowflake is a good candidate for companies with large volumes of data and heavy processing workloads. Although we've been successful using Snowflake in our medium-sized engagements, we've yet to experience Snowflake in large ecosystems where data need to be owned across segments of the organization.
We often relate data warehousing to a central infrastructure that is hard to scale and manage with the growing demands around data. Snowflake, however, is a new SQL Data Warehouse as a Service solution built from the ground up for the cloud. With a bunch of neatly crafted features such as database-level atomicity, structured and semi-structured data support, in-database analytics functions and above all with a clear separation of storage, compute and services layer, Snowflake addresses most of the challenges faced in data warehousing.