We performed a comparison between Dremio and Snowflake 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."We primarily use Dremio to create a data framework and a data queue."
"Dremio gives you the ability to create services which do not require additional resources and sterilization."
"The most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it."
"Dremio allows querying the files I have on my block storage or object storage."
"I like the ability to work with a managed service on the cloud and that is easy to start with."
"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."
"It helped us to build MVP (minimum viable product) for our idea of building a data warehouse model for small businesses."
"The solution is stable."
"Snowflake is a database, and it is very good and useful. The most interesting part is that memory management is very good in Snowflake. For a business intelligence project, SQL Server is taking a lot of time for reporting services. There are a lot of calculations, and the reporting time is shown as two minutes, whereas Snowflake is taking just two seconds for the same reporting services."
"The solution is easy to use."
"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."
"The syntax is advanced which reduces the time to write code."
"Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people."
"It shows errors sometimes."
"Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake."
"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."
"The solution should offer an on-premises version also. We have some requirements where we would prefer to use it as a template."
"They need to improve its ETL functionality so that Snowflake becomes an ETL product. Snowpipe can do some pipelines and data ingestion, but as compare to Talend, these functionalities are limited. The ETL feature is not good enough. Therefore, Snowflake can only be used as a database. You can't use it as an ETL tool, which is a limitation. We have spoken to the vendor, and they said they are working on it, but I'm not sure when they will bring it to production."
"There are always a few operation updates here and there that can be made."
"The solution could improve the user interface and add functionality to the system."
"The pricing of the solution should be much easier to calculate or find by yourself."
"I see room for improvement when it comes to credit performance. The other thing I'd like to be improved is the warehouse facility."
"Its stability could be better."
"This solution could be improved by offering machine learning apps."
Dremio is ranked 11th in Cloud Data Warehouse with 6 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 94 reviews. Dremio is rated 8.6, while Snowflake is rated 8.4. The top reviewer of Dremio writes "It enables you to manage changes more effectively than any other platform". On the other hand, the top reviewer of Snowflake writes "Good usability, good data sharing and elastic compute features, and requires less DBA involvement". Dremio is most compared with Databricks, Starburst Enterprise, Amazon Redshift, Microsoft Azure Synapse Analytics and Microsoft Power BI, whereas Snowflake is most compared with BigQuery, Azure Data Factory, Teradata, Vertica and Matillion ETL. See our Dremio vs. Snowflake report.
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