Try our new research platform with insights from 80,000+ expert users

Dremio vs IBM SPSS Modeler 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

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 (7th)
IBM SPSS Modeler
Ranking in Data Science Platforms
14th
Average Rating
8.0
Reviews Sentiment
6.6
Number of Reviews
39
Ranking in other categories
Data Mining (4th)
 

Mindshare comparison

As of June 2025, in the Data Science Platforms category, the mindshare of Dremio is 3.9%, up from 3.3% compared to the previous year. The mindshare of IBM SPSS Modeler is 2.5%, down from 2.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

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.
PeterHuo - PeerSpot reviewer
Good tool for extracting data from data warehouses, creating streams, and manipulating logic to extract final data
There are performance issues. Extracting data from many combined tables can take hours and occasionally crash the server due to memory leaks. This performance problem bothers people. The performance issue seems to be related to the server. We design streams on the client and submit them to the server, which generates a large SQL statement. There are two potential bottlenecks: one in the server and another in data extraction. I'm unsure about the exact mechanics of data splitting when fetching from the database. When streams become larger, performance bottlenecks may occur in the IBM SPSS Modeler server or the database. Sometimes the server crashes and needs to be restarted to release memory on both sides. I'm not sure exactly where the problem is caused, as I focus on stream design rather than server issues. The problem could be on the IBM SPSS Modeler server and database.

Quotes from Members

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

Pros

"Dremio is very easy to use for building queries."
"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."
"It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS."
"Dremio allows querying the files I have on my block storage or object storage."
"Overall, you can rate it as eight out of ten."
"We primarily use Dremio to create a data framework and a data queue."
"Compared to other tools, the product works much easier to analyze data without coding."
"We are using it either for workforce deployment or to improve our operations."
"We have integration where you can write third-party apps. This sort of feature opens it up to being able to do anything you want."
"I think the code modeling features are the most valuable and without the need to write a code back with many different possibilities to choose from. And the second one is linked to the activity of the data preparation."
"It is a great product for running statistical analysis."
"IBM was chosen because of usability. It's point and click, whereas the other out-of-the box-solution, or open-source solutions, require full-on programming and a much higher skill level."
"It is pretty scalable."
"It handles large data better than the previous system that we were using, which was basically Excel and Access. We serve upwards of 300,000 parts over a 150 regions and we need to crunch a lot of numbers."
 

Cons

"There are performance issues at times due to our limited experience with Dremio, and the fact that we are running it on single nodes using a community version."
"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."
"They need to have multiple connectors."
"They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today."
"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."
"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 would be good if IBM added help resources to the interface."
"Requires more development."
"Neural networks are quite simple, and now neural networks are evolving to these architecture related to deep learning, etc. They didn't incorporate this in IBM SPSS Modeler."
"It is not integrated with Qlik, Tableau, and Power BI."
"When I used it in the office, back in the day, we did have some stability issues. Sometimes it just randomly crashed and we couldn't get good feedback. But when I use it for my own stuff now I don't have any problems."
"We have run into a few problems doing some entity matching/analytics."
"It would be beneficial if the tool would include more well-known machine learning algorithms."
"We would like to see better visualizations and easier integration with Cognos Analytics for reporting."
 

Pricing and Cost Advice

"Dremio is less costly competitively to Snowflake or any other tool."
"Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."
"If you are in a university and the license is free then you can use the tool without any charges, which is good."
"It is a huge increase to time savings."
"The scalability was kind of limited by our ability to get other people licenses, and that was usually more of a financial constraint. It's expensive, but it's a good tool."
"Its price is okay for a company, but for personal use, it is considered somewhat expensive."
"It is an expensive product."
"$5,000 annually."
"This tool, being an IBM product, is pretty expensive."
"I am using the free version of IBM SPSS Modeler, it is the educational edition version."
report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
859,129 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
30%
Computer Software Company
9%
Manufacturing Company
6%
Healthcare Company
4%
Financial Services Firm
12%
Educational Organization
10%
Government
9%
University
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

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...
What do you like most about IBM SPSS Modeler?
Compared to other tools, the product works much easier to analyze data without coding.
What is your experience regarding pricing and costs for IBM SPSS Modeler?
The government has funds and a budget, it's hard to say if it's expensive or cheap. In Canada, they have a yearly budget. They used to encourage people to use the modeler for development. If ten us...
What needs improvement with IBM SPSS Modeler?
There are performance issues. Extracting data from many combined tables can take hours and occasionally crash the server due to memory leaks. This performance problem bothers people. The performanc...
 

Comparisons

 

Also Known As

No data available
SPSS Modeler
 

Overview

 

Sample Customers

UBS, TransUnion, Quantium, Daimler, OVH
Reisebªro Idealtours GmbH, MedeAnalytics, Afni, Israel Electric Corporation, Nedbank Ltd., DigitalGlobe, Vodafone Hungary, Aegon Hungary, Bureau Veritas, Brammer Group, Florida Department of Juvenile Justice, InSites Consulting, Fortis Turkey
Find out what your peers are saying about Dremio vs. IBM SPSS Modeler and other solutions. Updated: June 2025.
859,129 professionals have used our research since 2012.