We performed a comparison between Dataiku and Dremio based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"Data Science Studio's data science model is very useful."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"The most valuable feature is the set of visual data preparation tools."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"The solution is quite stable."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"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 gives you the ability to create services which do not require additional resources and sterilization."
"We primarily use Dremio to create a data framework and a data queue."
"Dremio allows querying the files I have on my block storage or object storage."
"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."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"I think it would help if Data Science Studio added some more features and improved the data model."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"The ability to have charts right from the explorer would be an improvement."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"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."
"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."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
"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."
"It shows errors sometimes."
Dataiku is ranked 11th in Data Science Platforms while Dremio is ranked 9th in Data Science Platforms with 6 reviews. Dataiku is rated 8.2, while Dremio is rated 8.6. The top reviewer of Dataiku writes "The model is very useful". On the other hand, the top reviewer of Dremio writes "It enables you to manage changes more effectively than any other platform". Dataiku is most compared with Databricks, KNIME, Alteryx, RapidMiner and IBM SPSS Statistics, whereas Dremio is most compared with Databricks, Snowflake, Starburst Enterprise, Amazon Redshift and BigQuery. See our Dataiku vs. Dremio report.
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