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Dataiku vs Dremio comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Dataiku
Ranking in Data Science Platforms
6th
Average Rating
8.2
Reviews Sentiment
7.1
Number of Reviews
12
Ranking in other categories
No ranking in other categories
Dremio
Ranking in Data Science Platforms
10th
Average Rating
8.6
Reviews Sentiment
7.1
Number of Reviews
8
Ranking in other categories
Cloud Data Warehouse (6th)
 

Mindshare comparison

As of October 2025, in the Data Science Platforms category, the mindshare of Dataiku is 11.7%, up from 10.9% compared to the previous year. The mindshare of Dremio is 3.1%, down from 4.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Dataiku11.7%
Dremio3.1%
Other85.2%
Data Science Platforms
 

Featured Reviews

RichardXu - PeerSpot reviewer
The platform organizes workflows visually and efficiently
One of the valuable features of Dataiku is the workflow capability. It allows us to organize a workflow efficiently. The platform has a visual interface, making it much easier for educated professionals to organize their work. This feature is useful because it simplifies tasks and eliminates the need for a data scientist. If you are knowledgeable about AI, you can directly write using primitive tools like Pantera flow, PyTorch, and Scikit-learn. However, Dataiku makes this process much easier.
KamleshPant - PeerSpot reviewer
Solution offers quick data connection with an edge in computation
It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS. It is a similar experience between the based application and cloud-based application. You just get the source, connect the data, get visualization, get connected, and do whatever you want. They say data reflection is one way where they do the caching and all that. Starburst also does the caching. In Starburst, you have a data product. Here, the data product comes from a reflection perspective. The y are working on a columnar memory map, columnar computation. That will have some edge in computation.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Cloud-based process run helps in not keeping the systems on while processes are running."
"The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code."
"Our clients can easily drag and drop components and use them on the spot."
"I believe the return on investment looks positive."
"Data Science Studio's data science model is very useful."
"The most valuable feature is the set of visual data preparation tools."
"Dataiku is highly regarded as it is a leader in the Gartner ranking."
"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."
"Dremio is very easy to use for building queries."
"It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS."
"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 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."
"We primarily use Dremio to create a data framework and a data queue."
"Overall, you can rate it as eight out of ten."
"Dremio gives you the ability to create services which do not require additional resources and sterilization."
"Dremio allows querying the files I have on my block storage or object storage."
 

Cons

"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."
"The ability to have charts right from the explorer would be an improvement."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"We still encounter some integration issues."
"There is room for improvement in terms of allowing for more code-based features."
"I think it would help if Data Science Studio added some more features and improved the data model."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"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."
"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."
"They need to have multiple connectors."
"It shows errors sometimes."
"They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today."
"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."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
"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."
 

Pricing and Cost Advice

"The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."
"Pricing is pretty steep. Dataiku is also not that cheap."
"Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."
"Dremio is less costly competitively to Snowflake or any other tool."
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
Manufacturing Company
10%
Computer Software Company
9%
Energy/Utilities Company
6%
Financial Services Firm
30%
Computer Software Company
9%
Manufacturing Company
7%
Healthcare Company
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise1
Large Enterprise7
By reviewers
Company SizeCount
Small Business1
Midsize Enterprise3
Large Enterprise4
 

Questions from the Community

What is your experience regarding pricing and costs for Dataiku Data Science Studio?
I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies. However, it is a pricey solution and I primarily recommend it to bigger companies.
What needs improvement with Dataiku Data Science Studio?
There is room for improvement in terms of allowing for more code-based features. I would love for Dataiku to allow more flexibility with code-based components and provide the possibility to extend ...
What is your primary use case for Dataiku Data Science Studio?
My company sells licenses for both Dataiku and Alteryx, and we have clients who use them. I engage with several companies in telecommunications, retail, and energy to assess how our clients are uti...
What do you like most about Dremio?
Dremio allows querying the files I have on my block storage or object storage.
What is your experience regarding pricing and costs for Dremio?
The licensing is very expensive. We need a license to scale as we are currently using the community version.
What needs improvement with Dremio?
They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today. They don't have Salesforce connectivity. However, Starburst do...
 

Comparisons

 

Also Known As

Dataiku DSS
No data available
 

Overview

 

Sample Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
UBS, TransUnion, Quantium, Daimler, OVH
Find out what your peers are saying about Dataiku vs. Dremio and other solutions. Updated: September 2025.
868,787 professionals have used our research since 2012.