We performed a comparison between Snowflake and Snowflake Analytics based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The most valuable feature of Snowflake is it's an all-in-one data warehousing solution."
"The cloning functionality has been the most valuable. I have been able to completely copy databases. The data sharing concept is also useful. As compared to, for example, SAP, Snowflake is a lot more open, and it allows a lot more connectivity for other providers than an SAP ecosystem."
"Everything is automatic, and I don't have to do any maintenance."
"It was relatively easy to use, and it was easy for people to convert to it."
"I like the idea that you can assign roles and responsibilities, limiting access to data."
"The initial setup is very simple."
"The querying speed is fast."
"Working with Parquet files is support out of the box and it makes large dataset processing much easier."
"It is quite a convenient tool."
"Time Travel and Snowpipe are good features."
"The most valuable feature of Snowflake Analytics is its performance."
"Considering everything I have accessed, the product's dashboard is good since it provides multiple good options, including customization options."
"The Snowflake features I find most beneficial for data analysis are primarily related to analytics, particularly their features like materialized views and queues, which are especially useful for dashboarding purposes."
"Features like the fact that the solution is very fast and available on the cloud are some of the valuable attributes of the solution."
"Its performance speed is very good."
"The advanced features like time travel, zero copy cloning and scalability have been most useful. Snowflake requires zero maintenance for Data Warehousing on the cloud system."
"Support needs improvement, as it can take several days before you get some initial support."
"An additional feature I'd like to see is called materialized views, which can speed up some run times. I'd like it to be able to be used where you can have multiple tables inside them; materialized view. That would be nice. As well as being able to run cursors, to be able to do some bulk updates and some more advanced querying, table building on the fly."
"Availability is a problem."
"The solution could use a little bit more UI."
"It's not that flexible when compared to Oracle."
"We would like to see more security including more masking and more encryption at the database level."
"This solution could be improved by offering machine learning apps."
"Currently, Snowflake doesn't support unstructured data."
"The technical support is not very good."
"The platform's data governance space needs more capability."
"The UI must be improved."
"The solution’s interface is good but it could be improved."
"Machine learning in Snowflake isn't as advanced as in other products. I haven't heard of any successful industry-wide use cases of machine learning implemented in Snowflake. It might take a couple of years to reach the same level as Databricks."
"Moving data from legacy systems to Snowflake is not that easy. There are some cases where processors are not actually compatible with Snowflake."
"End-to-end execution of jobs isn't possible with Snowflake, which means we have to do some customization."
"The solution’s scalability could be improved."
Snowflake is ranked 1st in Cloud Data Warehouse with 92 reviews while Snowflake Analytics is ranked 7th in Cloud Data Warehouse with 30 reviews. Snowflake is rated 8.4, while Snowflake Analytics is rated 8.4. The top reviewer of Snowflake writes "Good usability, good data sharing and elastic compute features, and requires less DBA involvement". On the other hand, the top reviewer of Snowflake Analytics writes "A scalable tool useful for data lake and data mining processes". Snowflake is most compared with BigQuery, Azure Data Factory, Teradata, Vertica and AWS Lake Formation, whereas Snowflake Analytics is most compared with Azure Data Factory, Adobe Analytics, Mixpanel, Amplitude and Glassbox. See our Snowflake vs. Snowflake Analytics report.
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I wrote a white paper on this that you can download here:https://bit.ly/3bRTCqp
It comes down to five factors, in my opinion:
1. Options for Deployment - Can you deploy on any cloud, or on-premises?
2. External tables and analyze in-place features - Can you leverage data outside the DB?
3. Optimizations - What are the options for slow-running queries?
4. Depth of analytics - can you do ML, time-series, geospatial, SQL, Python, etc.
5. Other data governance features - encryption and other governance features
Of course, speed and scalability is a huge factor, but most people are on top of that.
The white paper was commissioned by Vertica, a player you didn't mention above, but I attempted to keep it vendor-neutral.
We did a PoC with Snowflake. scale up and down works as advertised. Be careful with large ETL streams. We would have to rewrite at least 20% of our ETL to make Snowflake meet or beat our current SLA's.
Snowflake is a columnar stored database. It is not a “data lake.”
The main success criteria would be "re-use" of data by SQL-driven users.
I can't speak for ALL cloud providers but on AWS the storage cost is comparable to S3. The advantage is once the data is housed in the Snowflake environment it can be re-used/re-purposed without the need of another SQL engine that has to be managed (Dremio, PrestoDB, etc.).