We compared Snowflake and Firebolt based on our user's reviews across various parameters. After reading all of the collected data, you can find our conclusion below.
Both Snowflake and Firebolt receive high marks for performance, user-friendliness, and cost-effectiveness, attracting users with their data management solutions. Snowflake is praised for its powerful performance, scalability, and speedy query execution, coupled with a positive customer service experience and a straightforward licensing model. The platform’s ability to handle large workloads and manage numerous concurrent users efficiently stands out, as does its positive return on investment. Firebolt is recognized for its swifter deployment process, exceptional query speeds, and cost-efficiency, made possible by its elastic scalability and intuitive interface. While its documentation and pricing model clarity could be improved, Firebolt's competitive pricing and flexible licensing options are well-received, along with the commendable customer support. Despite their distinct advantages and minor areas for improvement, both platforms excel in enhancing data analytics and operational efficiency for their users.
The summary above is based on 76 interviews we conducted recently with Snowflake and Firebolt users. To access the review's full transcripts, download our report.
"Firebolt is fast for analytical purposes. For example, we have analytical data in our data warehouse, and Firebolt can quickly query it to generate quick results."
"The syntax is advanced which reduces the time to write code."
"The features that I have found most valuable are the ease of use, the rapidness, how quickly the solution can be implemented, and of course that it's been very easy to move from the on-premise world to the Cloud world because Snowflake is based on SQL also."
"The best thing about Snowflake is its flexibility in changing warehouse sizes or computational power."
"The distributed architecture of Snowflake has the capacity to process huge datasets faster and allows us to scale up and down according to our needs."
"The solution's computing time is less."
"The overall ecosystem was easy to manage. Given that we weren't a very highly technical group, it was preferable to other things we looked at because it could do all of the cloud tunings. It can tune your data warehouse to an appropriate size for controlled billing, resume and sleep functions, and all such things. It was much more simple than doing native Azure or AWS development. It was stable, and their support was also perfect. It was also very easy to deploy. It was one of those rare times where they did exactly what they said they could do."
"Snowflake is faster than on-premise systems and allows for variable compute power based on need."
"It was relatively easy to use, and it was easy for people to convert to it."
"Firebolt's engine takes a long time to start because it needs to make engine calls."
"Snowflake can improve its machine learning and AI capabilities."
"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."
"This solution could be improved by offering machine learning apps."
"They should improve the reporting tools."
"There are three things that came to my notice. I am not very sure whether they have already done it. The first one is very specific to the virtual data warehouse. Snowflake might want to offer industry-specific models for the data warehouse. Snowflake is a very strong product with credit. For a typical retail industry, such as the pharma industry, if it can get into the functional space as well, it will be a big shot in their arm. The second thing is related to the migration from other data warehouses to Snowflake. They can make the migration a little bit more seamless and easy. It should be compatible, well-structured, and well-governed. Many enterprises have huge impetus and urgency to move to Snowflake from their existing data warehouse, so, naturally, this is an area that is critical. The third thing is related to the capability of dealing with relational and dimensional structures. It is not that friendly with relational structures. Snowflake is more friendly with the dimensional structure or the data masks, which is characteristic of a Kimball model. It is very difficult to be savvy and friendly with both structures because these structures are different and address different kinds of needs. One is manipulation-heavy, and the other one is read-heavy or analysis-heavy. One is for heavy or frequent changes and amendments, and the other one is for frequent reads. One is flat, and the other one is distributed. There are fundamental differences between these two structures. If I were to consider Snowflake as a silver bullet, it should be equally savvy on both ends, which I don't think is the case. Maybe the product has grown and scaled up from where it was."
"The cost of the solution could be reduced."
"Pricing is an issue for many customers."
"Support needs improvement, as it can take several days before you get some initial support."
Firebolt is ranked 14th in Cloud Data Warehouse with 1 review while Snowflake is ranked 1st in Cloud Data Warehouse with 94 reviews. Firebolt is rated 9.0, while Snowflake is rated 8.4. The top reviewer of Firebolt writes "Can quickly query it to generate quick results". On the other hand, the top reviewer of Snowflake writes "Good usability, good data sharing and elastic compute features, and requires less DBA involvement". Firebolt is most compared with Microsoft Azure Synapse Analytics and Yellowbrick Cloud Data Warehouse, whereas Snowflake is most compared with BigQuery, Azure Data Factory, Teradata, Vertica and AWS Lake Formation.
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