Databricks vs IBM SPSS Modeler comparison

Databricks and IBM are both solutions in the Data Science Platforms category. Databricks is ranked #1 with an average rating of 8.3, while IBM is ranked #17 with an average rating of 7.0. Databricks holds a 15.9% mindshare in Data Science Platforms, compared to IBM’s 2.5% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 86% of IBM users who would recommend it.

Databricks

Read 91 Databricks reviews

25,542 Views
11,494 Comparison Views

96% willing to recommend

IBM SPSS Modeler

Read 39 IBM SPSS Modeler reviews

2,280 Views
1,528 Comparison Views

86% willing to recommend

Databricks

IBM SPSS Modeler

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

IBM SPSS Modeler and Databricks are data analytics competitors. Databricks has the upper hand due to its scalability and real-time processing for large-scale operations.

Features: IBM SPSS Modeler offers robust statistical tools, intuitive flowchart modeling, and effective predictive analytics. Databricks provides strong integration with big data technologies, advanced machine learning workflows, and collaboration through its notebook interface.

Room for Improvement: IBM SPSS Modeler could enhance its cloud capabilities and collaboration features. It may also benefit from more advanced machine learning tools. Databricks can improve by simplifying its initial adaptation to cloud environments, optimizing cost for smaller operations, and streamlining complex workflows for non-technical users.

Ease of Deployment and Customer Service: IBM SPSS Modeler is straightforward to deploy on-premise with minimal cloud infrastructure and offers attentive customer service. Databricks supports quick, cloud-native deployments with strong team collaboration but may require additional training for cloud acclimation.

Pricing and ROI: IBM SPSS Modeler generally presents lower initial setup costs with good ROI for small and mid-sized statistical analyses. Databricks, while involving higher upfront investment, often yields a higher long-term ROI through its scalability and big data capabilities, justifying the initial expense for extensive data operations.

To learn more, read our detailed Databricks vs. IBM SPSS Modeler Report (Updated: June 2025).

Buyer's Guide

Databricks vs. IBM SPSS Modeler

June 2025

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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

Databricks

Ranking in Data Science Platforms

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (8th), Streaming Analytics (1st)

IBM SPSS Modeler

Ranking in Data Science Platforms

17th

Average Rating

8.0

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Mining (4th)

Mindshare comparison

As of July 2025, in the Data Science Platforms category, the mindshare of Databricks is 15.9%, down from 19.8% compared to the previous year. The mindshare of IBM SPSS Modeler is 2.5%, up from 2.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

Capability to integrate diverse coding languages in a single notebook greatly enhances workflow

Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.

Read full review

PeterHuo

Application Architect at Canada Border Services Agency

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.

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Quotes from Members

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

Pros

"It is fast, it's scalable, and it does the job it needs to do."

"The fast data loading process and data storage capabilities are great."

"The ability to stream data and the windowing feature are valuable."

"The solution is an impressive tool for data migration and integration."

"The simplicity of development is the most valuable feature."

"I would rate them ten out of ten."

"It can send out large data amounts."

"The most valuable feature is the ability to use SQL directly with Databricks."

More Databricks pros

"It is pretty scalable."

"Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms."

"The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well."

"I think it is the point and drag features that are the most valuable. You can simply click at the windows, and then pull up the functions."

"A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly."

"We have been able to do some predictive modeling with it"

"It is just a lot faster. So you do not have to write a bunch of code, you can throw that stuff on there pretty quickly and do prototyping quickly."

"I was familiar with using IBM SPSS Modeler separately in the private sector before that. It's a good tool for extracting data from data warehouses, creating streams, and manipulating logic to extract final data."

More IBM SPSS Modeler pros

Cons

"I have seen better user interfaces, so that is something that can be improved."

"Databricks has a lack of debuggers, and it would be good to see more components."

"As a data engineer, I see cluster failure in our Databricks user databases as a major issue."

"Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."

"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."

"Databricks can improve by making the documentation better."

"There is room for improvement in visualization."

"Anyone who doesn't know SQL may find the product difficult to work with."

More Databricks cons

"We have run into a few problems doing some entity matching/analytics."

"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."

"It is not integrated with Qlik, Tableau, and Power BI."

"The product does not have a search function for tags."

"The standard package (personal) is not supported for database connection."

"Unstructured data is not appropriate for SPSS Modeler."

"I would like better integration into the Weather Company solution. I have raised a couple of concerns about this integration and having more time series capabilities."

"The biggest issue with the visual modeling capability is that we can't extract the SQL code under the hood."

More IBM SPSS Modeler cons

Pricing and Cost Advice

"I would rate Databricks' pricing seven out of ten."

"Databricks' cost could be improved."

"It is an expensive tool. The licensing model is a pay-as-you-go one."

"The cost is around $600,000 for 50 users."

"The solution is a good value for batch processing and huge workloads."

"Databricks is a very expensive solution. Pricing is an area that could definitely be improved. They could provide a lower end compute and probably reduce the price."

"Price-wise, I would rate Databricks a three out of five."

"I would rate the tool’s pricing an eight out of ten."

More Databricks pricing and cost advice

"It is a huge increase to time savings."

"It got us a good amount of money with quick and efficient modeling."

"$5,000 annually."

"Its price is okay for a company, but for personal use, it is considered somewhat expensive."

"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."

"If you are in a university and the license is free then you can use the tool without any charges, which is good."

"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 users use the server with ten licenses, it runs faster. But if forty users use the same appliance, everything slows down. People then think it's not easy to do things and prefer using remote tools like Python to extract data from the database. It's not about being expensive or cheap, but about people's knowledge and experience in how to do the work."

"It is an expensive product."

More IBM SPSS Modeler pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Financial Services Firm

12%

Educational Organization

11%

Government

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

See all answers

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

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Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

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What do you like most about IBM SPSS Modeler?

Compared to other tools, the product works much easier to analyze data without coding.

See all answers

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...

See all answers

What needs improvement with IBM SPSS Modeler?

See all answers

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Compared 15% of the time

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Product Reports

Buyer's Guide

Databricks

July 2025

Download Databricks product report

Buyer's Guide

IBM SPSS Modeler

July 2025

Download IBM SPSS Modeler product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

SPSS Modeler

Overview

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

Databricks

IBM SPSS Modeler is an extensive predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. By providing a range of advanced algorithms and techniques that include text analytics, entity analytics, decision management and optimization, SPSS Modeler can help you consistently make the right decisions from the desktop or within operational systems.

Buy
https://www.ibm.com/products/spss-modeler/pricing

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https://www.ibm.com/account/reg/us-en/signup?formid=urx-19947

IBM

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

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

Buyer's Guide

Databricks vs. IBM SPSS Modeler

June 2025

Free Report: Databricks vs. IBM SPSS Modeler

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