This blip is not on the current edition of the radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the radarUnderstand more
Published: Nov 20, 2019
Last Updated: May 19, 2020
May 2020

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.

Nov 2019

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.