Snowflake Cloud Data Management Performance Review
Use Cases and Deployment Scope
We use Snowflake as one of our cloud data management platforms, and the main problems it solves for us are
a) Serving as a data repository and an analytics engine for both our structured and unstructured data, from which we can extract real business intelligence, in real time.
b) It also serves as a Data Lake where raw data is can be dumped irrespective of the format it is in, without worrying about our compute and storage constraints.
c) Lastly, we also use if for clean rooms and secure data sharing which is especially important when doing M&A deals and due diligence.
Pros
- Handles both structured and unstructured data
- Compute and Storage scale automatically
- Powerful analytics and data visualization capabilities
- Secure data sharing solution
Cons
- Does not handle multi-cloud data portability very well
- It is not 100% vendor agnostic and hence not "open" in the true sense of the word
- It is not our first choice for engineering, AI and ML related work and applications
Likelihood to Recommend
Excellence for data storage across formats, especially for teams that live and breathe SQL (analysts, consultants etc.). However, the lack of a truly open architecture means that SI can be a bit of a headache in a multi/hybrid cloud environment. Also, it does not lend itself work to AI/ML application use/development.
