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 #13 with an average rating of 7.0. Databricks holds a 19.1% mindshare in Data Science Platforms, compared to IBM’s 2.3% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 86% of IBM users who would recommend it.

Databricks

Read 88 Databricks reviews

17,588 Views
11,896 Comparison Views

96% willing to recommend

IBM SPSS Modeler

Read 39 IBM SPSS Modeler reviews

2,062 Views
1,679 Comparison Views

86% willing to recommend

Databricks

IBM SPSS Modeler

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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: January 2025).

Buyer's Guide

Databricks vs. IBM SPSS Modeler

January 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

Streaming Analytics (1st)

IBM SPSS Modeler

Ranking in Data Science Platforms

13th

Average Rating

8.0

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Mining (4th)

Mindshare comparison

As of January 2025, in the Data Science Platforms category, the mindshare of Databricks is 19.1%, up from 18.5% compared to the previous year. The mindshare of IBM SPSS Modeler is 2.3%, down from 2.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Parag Bhosale

Senior Data Engineer at a logistics company with 51-200 employees

Integrating engineering and learning, but cost challenges arise with cluster management

We often use a single cluster to ingest Databricks, which Databricks doesn't recommend. They suggest using a no-cluster solution like job clusters. This can be overwhelming for us because we started smaller. We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly. We need to stay in sync with the DVR versions, and migrations can pose challenges. For example, issues arose when we moved a cluster from a previous version to the latest one. We could use their job clusters, however, that increases costs, which is challenging for us as a startup. Maintaining this infrastructure can be a headache.

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's great technology."

"The main features of the solution are efficiency."

"Automation with Databricks is very easy when using the API."

"Easy to use and requires minimal coding and customizations."

"Its lightweight and fast processing are valuable."

"The solution offers a free community version."

"Having one solution for everything, from data engineering to machine learning, is beneficial since everything comes under one hood."

"It is a cost-effective solution."

More Databricks pros

"The ease of use in the user interface is the best part of it. The ability to customize some of my streams with R and Python has been very useful to me, I've automated a few things with that."

"We are using it either for workforce deployment or to improve our operations."

"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."

"We had an IBM Guardium service contract where we used one of their resources to help us develop our prototype. It was a good experience, but they were helpful and responsive."

"It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler."

"The visual modeling capability is one of its attractive features."

"We have full control of the data handling process."

"The supervised models are valuable. It is also very organized and easy to use."

More IBM SPSS Modeler pros

Cons

"Databricks' technical support takes a while to respond and could be improved."

"While Databricks is generally a robust solution, I have noticed a limitation with debugging in the Delta Live Table, which could be improved."

"Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics."

"We'd like a more visual dashboard for analysis It needs better UI."

"It should have more compatible and more advanced visualization and machine learning libraries."

"The API deployment and model deployment are not easy on the Databricks side."

"They release patches that sometimes break our code. These patches are supposed to fix issues, but sometimes they cause disruptions."

"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."

More Databricks cons

"The platform that you can deploy it on needs improvement because I think it is Windows only. I do not think it can run off a Red Hat, like the server products. I am pretty sure it is Windows and AIX only."

"It is very good, but slow. The slowness may be because we have not finalized all the background information in SPSS. It still needs some tweaking."

"I can say the solution is outdated."

"The challenge for the very technical data scientists: It is constraining for them."

"The integration with sources and visualisation needs some improvement. The scalability needs improvement."

"It's not as user friendly as it could be."

"Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization."

"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."

More IBM SPSS Modeler cons

Pricing and Cost Advice

"The solution is affordable."

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

"We only pay for the Azure compute behind the solution."

"I rate the price of Databricks as eight out of ten."

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

"I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."

"I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself."

"The price of Databricks is reasonable compared to other solutions."

More Databricks pricing and cost advice

"It is a huge increase to time savings."

"I am using the free version of IBM SPSS Modeler, it is the educational edition version."

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

"This tool, being an IBM product, is pretty expensive."

"$5,000 annually."

"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."

"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

11%

Manufacturing Company

Healthcare Company

Educational Organization

13%

Financial Services Firm

12%

Computer Software Company

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...

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What needs improvement with IBM SPSS Modeler?

See all answers

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Amazon SageMaker vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

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Compared 28% of the time

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Compared 16% of the time

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Compared 15% of the time

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Compared 8% of the time

SAP Predictive Analytics vs IBM SPSS Modeler

Compared 2% of the time

More IBM SPSS Modeler Competitors

Product Reports

Buyer's Guide

Databricks

January 2025

Download Databricks product report

Buyer's Guide

IBM SPSS Modeler

January 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

Sign up for the trial
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

January 2025

Free Report: Databricks vs. IBM SPSS Modeler

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