We performed a comparison between Snowflake and Vertica based on our users’ reviews in five categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Vertica has an edge in this comparison due to its excellent performance. Snowflake does come out on top in the Ease of Deployment category, however.
"The most valuable feature is the snapshot database. In one second, you can just take a snapshot of the database for test purposes."
"The querying speed is fast."
"I like the idea that you can assign roles and responsibilities, limiting access to data."
"From a data warehouse perspective, it's an excellent all-round solution. It's very complete."
"The solution is stable."
"The way it is built and designed is valuable. The way the shared model is built and the way it exploits the power of the cloud is very good. Certain features related to administration and management, akin to Oracle Flashback and all that, are very important for modern-day administration and management. It is also good in terms of managing and improving performance, indexing, and partitioning. It is sort of completely automated. Everything is essentially under the hood, and the engine takes care of it all. As a data warehouse on the cloud, Snowflake stands strong on its ground even though each of the cloud providers has its own data warehouse, such as Redshift for AWS or Synapse for Azure."
"For us, the virtual warehousing is likely the most valuable aspect."
"Time travel is one feature that really helps us out."
"We are also opening new areas of business and potential new revenue streams using Vertica's analytic functions, most notably geospatial, where we are able to run billions of comparisons of lat/long point locations against polygon and point/radius locations in seconds. "
"For me, It's performance, scalability, low cost, and it's integrated into enterprise and big data environments."
"The product's initial setup phase is extremely simple."
"Vertica is easy to use and provides really high performance, stability, and scalability."
"I don't need any special hardware. I can use commodity hardware, which is nice to have in a commercial solution."
"Partition and join back to node are easy and simple for DBAs."
"Bulk loads, batch loads, and micro-batch loads have made it possible for our organization to process near real-time ingestions and faster analytics."
"Eighty percent of the ETL operations have improved since implementing this solution."
"There could be better ELT tools that are appropriate for Snowflake. We decided on Matillion and it seemed to be the only one. There need to be better choices, it would be great if Snowflake provided an ELT solution that people could use. Additionally, if there was a pure cloud-based ELT tool it would be useful."
"I am still in the learning stage. It has good security, but it can always be more secure."
"We would like to have an on-premises deployment option that has the same features, including scalability."
"They do have a native connector to connect with integration tools for loading data, but it would be much better to have the functionality built-in."
"Every product has room for improvement, although in this case, it needs some broadening of the functionality."
"The solution could improve by allowing non-structured data, such as PDFs, images, or videos. We cannot see the data."
"I have heard people having difficulty with the machine learning model, so there may be room for improvement."
"The scheduling system can definitely be better because we had to use external airflow for that. There should be orchestration for the scheduling system. Snowflake currently does not support machine learning, so it is just storage. They also need some alternatives for SQL Query. There should also be support for Spark in different languages such as Python."
"Performance of management of metadata layer (database catalog) needs improvement. We still have to have smaller customers on PostgreSQL; Vertica cannot manage thousands of schemata."
"Documentation has become much better, but can always use some improvement."
"Metadata for database files scale okay, but metadata related to tables/columns/sequences must be stored on all nodes."
"When it is about to reach the maximum storage capacity, it becomes slow."
"I have found that coding support could be simplified."
"Vertica seems to scale well, except for one use case where you are on a multi-node cluster. For example, if you had a nine-node cluster, one node goes down, then the eight nodes don't scale, because the absence of the node is very apparent, which is a problem. If you have nine nodes or multiple nodes, the whole idea is that if one of those nodes goes down, then you should not see an impact on the system if you have enough capacity. Even though we have enough capacity, you can still see the impact of the one node going down."
"Very bad support, I would rate it two out of 10."
"Fact-to-fact joins on multi-billion record tables perform poorly."
Snowflake is ranked 1st in Cloud Data Warehouse with 92 reviews while Vertica is ranked 7th in Cloud Data Warehouse with 83 reviews. Snowflake is rated 8.4, while Vertica is rated 8.2. 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 Vertica writes " A user-friendly tool that needs to improve its documentation part". Snowflake is most compared with BigQuery, Azure Data Factory, Teradata, AWS Lake Formation and Amazon EMR, whereas Vertica is most compared with SQL Server, Amazon Redshift, Teradata, Oracle Exadata and BigQuery. See our Snowflake vs. Vertica report.
See our list of best Cloud Data Warehouse vendors and best Data Warehouse vendors.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.