Darwin vs Databricks comparison

SparkCognition and Databricks are both solutions in the Data Science Platforms category. SparkCognition is ranked #27, while Databricks is ranked #1 with an average rating of 8.2. SparkCognition holds a 0.3% mindshare in Data Science Platforms, compared to Databricks’s 19.1% mindshare. Additionally, 100% of SparkCognition users are willing to recommend the solution, compared to 96% of Databricks users who would recommend it.

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Executive Summary

We performed a comparison between Darwin and Databricks 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.

To learn more, read our detailed Darwin vs. Databricks Report (Updated: October 2024).

Buyer's Guide

Darwin vs. Databricks

October 2024

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Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

As of November 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.8%, up from 2.6% compared to the previous year. The mindshare of Darwin is 0.3%, up from 0.3% compared to the previous year. The mindshare of Databricks is 19.1%, up from 19.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

AbakarAhmat

Software Engineer at a university with 10,001+ employees

Sep 21, 2023

Enhancing survey analysis that provides valued insightfulness

I use it to analyze questionnaire surveys related to a product, solution, or application, such as open data services, which I provide to consumers and end-users. These surveys contain evaluation assessments, and I use SPSS to analyze the responses The most valuable feature is its robust…

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AaronCooke

Founder at Helio Summit

Jun 11, 2021

Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows.

There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do. Because it's so much better than traditional methods, we don't get a ton of complaints of, "Oh, we wish we could do that." Most people are happy to see that they can build models that quickly, and that it can be done by the people who actually understand the problem, i.e. SMEs, rather than having to rely on data scientists. There's a small learning curve, but it's shorter for an SME in a given industry to learn Darwin than it takes for data scientists to learn industry-specific problems. The industry I work in deals with tons and tons of data and a lot of it lends itself to Darwin-created solutions. Initially, there were some limitations around the size of the datasets, the number of rows and number of columns. That was probably the biggest challenge. But we've seen the Darwin product, over time, slowly remove those limitations. We're happy with the progress they've made.

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Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Jul 10, 2024

Process large-scale data sets and integrates with Apache Spark with notebook environment

I primarily use Databricks to process large-scale data sets with Apache Spark. My main use case is processing large data sets, such as 600 GB or 800 GB Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of…

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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

"The most valuable feature is its robust statistical analysis capabilities."

"SPSS can handle whatever you throw at it, whether your data set contains 10,000, 100,000, or a million objects. It's like the heavy artillery of analytical tools."

"The most valuable features mainly include factor analysis, correlation analysis, and geographic analysis."

"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."

"The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can into multidimensional setup space. It's the multidimensional space facility that is most useful."

"Custom tables and macros: They allow us to create useful reports quickly for a broad audience."

"They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do."

"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."

More IBM SPSS Statistics pros

"Darwin has increased efficiency and productivity for our company. With our risk management team, there were models that took them more than three days to process each, only to see the outcome. Now, it takes minutes for Darwin to process the current model. So, we can have it in minutes. We don't have to wait three days for all the models to be tested, then make a decision."

"I liked the data checking feature where it looks at your data and sees how viable it is for use. That's a really cool feature. Automatic assessment of the quality of datasets, to me, seems very valuable."

"The key feature is the automated model-building. It has a good UI that will let people who aren't data scientists get in there and upload datasets and actually start building models, with very little training. They don't need to have any understanding of data science."

"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."

"The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."

"The thing that I find most valuable is the ability to clean the data."

"In terms of streamlining a lot of the low-level data science work, it does a few things there."

"The most valuable feature is the model-generation. With a nice dataset, Darwin gives you a nice model. That's a really nice feature because, if we're doing that ourselves, it's trial and error; we change the parameters a little and try again. We save time by just giving the dataset to Darwin and letting Darwin generate a model. We find the models it generates are good; better than we can generate."

"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."

"Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things."

"A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem."

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

"Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes."

"The load distribution capabilities are good, and you can perform data processing tasks very quickly."

"I like that Databricks is a unified platform that lets you do streaming and batch processing in the same place. You can do analytics, too. They have added something called Databricks SQL Analytics, allowing users to connect to the data lake to perform analytics. Databricks also will enable you to share your data securely. It integrates with your reporting system as well."

"The setup was straightforward."

More Databricks pros

Cons

"SPSS slows down the computer or the laptop if the data is huge; then you need a faster computer."

"The statistics should be more self-explanatory with detailed automated reports."

"In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options."

"There is a learning curve; it's not very steep, but there is one."

"Technical support needs some improvement, as they do not respond as quickly as we would like."

"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."

"Perhaps in terms of visualization. It's not really easy to do some data visualization, just simple, descriptive analysis in SPSS. I think that could be an area for improvement."

"The solution needs to improve forecasting using time series analysis."

More IBM SPSS Statistics cons

"Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin. It would be great if there was an API to connect our repository to Darwin."

"An area where Darwin might be a little weak is its automatic assessment of the quality of datasets. The first results it produces in this area are good, but in our experience, we have found that extra analysis is needed to produce an extra-clean set of data."

"Something they are working on, which is great, is to have an API that can access data directly from the source. Currently, we have to create a specific dataset for each model."

"The analyze function takes a lot of time."

"There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do."

"The challenge is very big toward making models operational or to industrialize them. E.g., what we want to do is to make unique credit models for each customer. So, we are preparing the types of customers who we can try new credit models on Darwin. But, I see this still very challenging to be able to get the data sets so Darwin can work. At this point, we are working it to get the data sets ready for Darwin."

"The Read Me's and the tutorials need to be greatly improved to get customers to understand how things work. It might be helpful to have some sample data sets for people to play around with, as well as some tutorial videos. It was very hard to find information on this in the time crunch that we had, to see how it worked and then make it work, while interfacing with folks at SparkCognition."

"There are issues around the ethics of artificial intelligence and machine learning. You need to have a lot of transparency regarding what is going on under the hood in order to trust it. Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets."

"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."

"It's not easy to use, and they need a better UI."

"A lot of people are required to manage this solution."

"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."

"Databricks can improve by making the documentation better."

"The initial setup is difficult."

"Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster."

"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

Pricing and Cost Advice

"It's quite expensive, but they do a special deal for universities."

"More affordable training for new staff members."

"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."

"We think that IBM SPSS is expensive for this function."

"I rate the tool's pricing a five out of ten."

"The price of this solution is a little bit high, which was a problem for my company."

More IBM SPSS Statistics pricing and cost advice

"The license cost is not cheap, especially not for markets like Mexico. But sometimes, you do have to make these leap of faith for some tools to see if they can get you the disruption that you are aiming for. The investment has paid off for us very well."

"In just six months, we calculated six million pesos that we have prevented in revenue from going away with another customer because of this solution. Thanks to Darwin, we didn't lose those six million pesos."

"I believe our cost is $1,000 per month."

"As far as I understand, my company is not paying anything to use the product."

"The price is okay. It's competitive."

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

"The billing of Databricks can be difficult and should improve."

"We pay as we go, so there isn't a fixed price. It's charged by the unit. I don't have any details detail about how they measure this, but it should be a mix between processing and quantity of data handled. We run a simulation based on our use cases, which gives us an estimate. We've been monitoring this, and the costs have met our expectations."

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

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

"I'm not involved in the financing, but I can say that the solution seemed reasonably priced compared to the competitors. Similar products are usually in the same price range. With five being affordable and one being expensive, I would rate Databricks a four out of five."

"We're charged on what the data throughput is and also what the compute time is."

More Databricks pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

University

10%

Computer Software Company

Manufacturing Company

Computer Software Company

24%

Government

12%

Real Estate/Law Firm

12%

Educational Organization

11%

Financial Services Firm

16%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about IBM SPSS Statistics?

The software offers consistency across multiple research projects helping us with predictive analytics capabilities.

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What is your experience regarding pricing and costs for IBM SPSS Statistics?

While the pricing of the product may be higher, the accompanying service and features justify the investment. However...

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What needs improvement with IBM SPSS Statistics?

In some cases, the product takes time to load a large dataset. They could improve this particular area.

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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 ...

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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 designe...

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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 analyti...

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Comparisons

Alteryx vs IBM SPSS Statistics

Compared 14% of the time

IBM SPSS Modeler vs IBM SPSS Statistics

Compared 13% of the time

TIBCO Statistica vs IBM SPSS Statistics

Compared 13% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 12% of the time

Google Cloud Datalab vs IBM SPSS Statistics

Compared 6% of the time

More IBM SPSS Statistics Competitors

IBM Watson Studio vs Darwin

Compared 100% of the time

More Darwin Competitors

Dataiku vs Databricks

Compared 11% of the time

Informatica PowerCenter vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 8% of the time

Dremio vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

Compared 6% of the time

More Databricks Competitors

Product Reports

Buyer's Guide

IBM SPSS Statistics

October 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Darwin

November 2024

Download Darwin product report

Buyer's Guide

Databricks

November 2024

Download Databricks product report

Also Known As

SPSS Statistics

No data available

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

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IBM

SparkCognition

Databricks

Overview

IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.

IBM SPSS Statistics Benefits

Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:

Ease of use. SPSS Statistics enables users to simply and intuitively take control of their statistical needs. The solution is designed so that analysts who do not know how to code can easily make full use of the various tools and capabilities that SPSS Statistics has to offer. Its command language is so straightforward that it does not require users to undergo special training before they use it.

Comprehensive and flexible build. SPSS Statistics is designed to be both a comprehensive and highly flexible analytics solution. It enables users to utilize a variety of integrations that make it easy for users to add features that they might feel they are missing.

Automation. SPSS Statistics makes it simple for users to automate basic tasks that they might otherwise devote too much time worrying about. Tasks like calculation or data gathering can be delegated to the system while more conceptual tasks like data analysis are given to an organization’s analysts to handle.

IBM SPSS Statistics Features

Intuitive user interface. SPSS Statistics enables users to deploy an intuitive interface that makes the process of system management simple. Among the other components of this interface is a drag-and-drop feature that makes analysis and management possible for anyone who wants to use it.

Advanced data visualizations. Analysts that employ SPSS Statistics gain access to tools that empower them to create and export data visualizations. These visualizations can be formatted in many different ways depending on what the user needs.

Local data storage. SPSS Statistics has the ability to securely store data on a user’s computer. This enables them to add layers of security that would not necessarily be present if the data was stored in the cloud.

Reviews from Real Users

IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.

An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."

A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”

SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.

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.

Sample Customers

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA

Hunt Oil, Hitachi High-Tech Solutions

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

Buyer's Guide

Darwin vs. Databricks

October 2024

Free Report: Darwin vs. Databricks

Find out what your peers are saying about Darwin vs. Databricks and other solutions. Updated: October 2024.

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