Since we last mentioned Snowflake in the Radar, we've gained more experience with it as well as with data mesh as an alternative to data warehouses and lakes. Snowflake continues to impress with features like time travel, zero-copy cloning, data sharing and its marketplace. We also haven't found anything we don't like about it, all of which has led to our consultants generally preferring it over the alternatives. Redshift is moving toward storage and compute separation, which has been a strong point of Snowflake, but even with Redshift Spectrum it isn't as convenient and flexible to use, partly because it is bound by its Postgres heritage (we do still like Postgres, by the way). Federated queries can be a reason to go with Redshift. When it comes to operations, Snowflake is much simpler to run. BigQuery, which is another alternative, is very easy to operate, but in a multicloud setup Snowflake is a better choice. We can also report that we've used Snowflake successfully with GCP, AWS, and Azure.
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.