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."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."
"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."
"Dremio allows querying the files I have on my block storage or object storage."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"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 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 pricing is reasonable and matches the rest of the market."
"A user-friendly and reliable solution."
"The initial setup is very simple."
"The most valuable feature of Snowflake is it's an all-in-one data warehousing solution."
"It is a very easy-to-use solution. It is user-friendly, and its setup time is very less."
"The solution is stable."
"It was relatively easy to use, and it was easy for people to convert to it."
"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."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
"It shows errors sometimes."
"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."
"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."
"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."
"I don't know about GCP, if they have connected for GCP. If they don't, they should allow for it."
"If they could bring in some tools for data integration, it would be really great."
"The complexity of the initial setup of Snowflake depends on the use case. However, Snowflake itself, we don't set it up. The difficulty comes from the ingestion patterns, depending on what data I'm putting in, what kind of enrichment, and what additional value we have to add. However, it does tend to get complex because we have a lot of semi-structured data which we need to handle in Snowflake. There have been some challenges."
"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."
"Pricing is an issue for many customers."
"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."
"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."
"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."
Dremio is ranked 11th in Cloud Data Warehouse with 6 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 92 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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