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 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."
"I have found the solution's most valuable features to be storage, flexibility, ease of use, and security."
"We find the data sharing and data marketplace aspects of Snowflake absolutely amazing."
"The tool is very easy to use. The solution’s desktop features are also very easy to use. Also, the product’s SQL-based connectivity is also good. It can connect with any tool."
"The most valuable features of Snowflake are that you have to pay per usage, and you don't have to worry about the maintenance of the data warehouse because it is on the cloud."
"It has great flexibility whenever we are loading data and performs ELT (extract, load, transform) techniques instead of ETL."
"The syntax is advanced which reduces the time to write code."
"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."
"Firebolt's engine takes a long time to start because it needs to make engine calls."
"There is a scope for improvement. They don't currently support integration with some of the Azure and AWS native services. It would be good if they can enhance their product to integrate with these services."
"For the Snowflake database, there should be some third-party features for the ETL. It would also be good to be able to use some kind of controls to get the data either from another database or a flat file. Its price should be improved. It should be cheaper than Microsoft."
"There is room for improvement in Snowflake's integration with Python. We do a lot of SQL programming in Snowflake, but we go to a different tool to program when we have to in Python."
"Snowflake needs to improve its programming part. Though the tool has Snowpath, it doesn’t support all features like its competitor, Databricks. Snowflake doesn’t support external data ingestion capabilities. You need to have third-party tools for that. Also, the tool needs to incorporate data integration features in its future releases."
"Every product has room for improvement, although in this case, it needs some broadening of the functionality."
"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."
"Snowflake has to build more capabilities because they have only built very few adapters, but they're growing and they're building. They should provide provisions to collect ETL pipeline capabilities, reduce developer work, and make more rapid application development, rather than some customizations. There are very few options, but they are building. I hope they will build ETL rapid application development provisions with more variety."
"The design of the product is easily misunderstood."
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, Yellowbrick Cloud Data Warehouse and Amazon Redshift, whereas Snowflake is most compared with BigQuery, Azure Data Factory, Teradata, Vertica and AWS Lake Formation.
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