Databricks vs IBM Watson Studio 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 #12 with an average rating of 8.4. Databricks holds a 17.2% mindshare in Data Science Platforms, compared to IBM’s 2.0% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Databricks

Read 89 Databricks reviews

14,742 Views
10,545 Comparison Views

96% willing to recommend

IBM Watson Studio

Read 14 IBM Watson Studio reviews

1,997 Views
1,233 Comparison Views

100% willing to recommend

Databricks

IBM Watson Studio

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Executive SummaryUpdated on Dec 5, 2024

Databricks and IBM Watson Studio compete in data science and analytics. Databricks seems to have an advantage in performance for large-scale data processing and Spark integration, while IBM Watson Studio excels in AI capabilities and offers a comprehensive suite of data science tools.

Features: Databricks is known for its ease in running large-scale analytics, built-in optimization recommendations, and support for collaboration in notebooks. It integrates seamlessly with Spark and offers notable machine learning capabilities. The scalability and support for multiple programming languages enhance its versatility with cloud integrations. IBM Watson Studio stands out for its AI capabilities, built-in data science tools like SPSS Modeler, and use of Jupyter notebooks, making it powerful for machine learning and data integration tasks.

Room for Improvement: Databricks could improve by expanding advanced visualization and machine learning libraries, enhancing ETL tool integration, and providing more user-friendly documentation and interfaces for popular BI tools. IBM Watson Studio could enhance its intuitive user experience, ease of deployment, and support for new AI features. Users often cite navigation challenges, desiring a more integrated interface.

Ease of Deployment and Customer Service: Databricks offers versatile deployment options with support for public, private, and hybrid clouds. It provides comprehensive documentation, though technical support experiences vary. IBM Watson Studio also supports cloud and on-premises deployments. Its documentation is praised for clarity, with users reporting reliability that reduces the need for frequent technical support.

Pricing and ROI: Databricks operates on a pay-as-you-go model, seen as costly for extensive data usage but flexible and offering positive ROI by reducing infrastructure costs. IBM Watson Studio is viewed as reasonably priced for its features, although complex workloads may incur higher costs. Its pricing is generally considered straightforward, delivering beneficial ROI for large-scale data projects.

To learn more, read our detailed Databricks vs. IBM Watson Studio Report (Updated: April 2025).

Buyer's Guide

Databricks vs. IBM Watson Studio

April 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 (7th), Streaming Analytics (1st)

IBM Watson Studio

Ranking in Data Science Platforms

12th

Average Rating

8.4

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

AI Development Platforms (10th)

Mindshare comparison

As of May 2025, in the Data Science Platforms category, the mindshare of Databricks is 17.2%, down from 19.5% compared to the previous year. The mindshare of IBM Watson Studio is 2.0%, down from 2.3% 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

Abilio Duarte

Senior Solutions Architect at Self-employed

A highly robust and well-documented platform that simplifies the complex world of AI

The main challenge lies in visibility and ease of use. Providing training sessions can be immensely helpful in helping users navigate and understand the tool's potential. This approach would empower users to explore and make the most of the tools and technologies at their disposal. Another area where IBM could enhance its offering is by providing more visibility to end users regarding the vast potential that Watson offers.

Read full review

Quotes from Members

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

Pros

"The integration with Python and the notebooks really helps."

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

"The solution offers a free community version."

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

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

"Databricks has a Unified Catalog that assists with secured access and governance."

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

"I work in the data science field and I found Databricks to be very useful."

More Databricks pros

"It is a very stable and reliable solution."

"It has a lot of data connectors, which is extremely helpful."

"The solution is very easy to use."

"The system's ability to take a look at data, segment it and then use that data very differently."

"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."

"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."

"Watson Studio is the most complete tool for AI projects."

"The main benefit is the ease of use. We see a lot of engineers in our site and customers that really like the way the tools are able to work with the people."

More IBM Watson Studio pros

Cons

"There should be better integration with other platforms."

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

"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."

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

"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."

"Scalability is an area with certain shortcomings. The solution's scalability needs improvement."

"Costs can quickly add up if you don't plan for it."

"The product needs samples and templates to help invite users to see results and understand what the product can do."

More Databricks cons

"One area that could be improved is the backup and restoration of the database and the overall database configuration."

"Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge."

"So a better user interface could be very helpful"

"The initial setup was complex."

"It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs."

"The decision making in their decision making feature is less good than other options."

"I want IBM's technical support team to provide more specific answers to queries."

"The main challenge lies in visibility and ease of use."

More IBM Watson Studio cons

Pricing and Cost Advice

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

"The solution uses a pay-per-use model with an annual subscription fee or package. Typically this solution is used on a cloud platform, such as Azure or AWS, but more people are choosing Azure because the price is more reasonable."

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

"We have only incurred the cost of our AWS cloud services. This is because during this period, Databricks provided us with an extended evaluation period, and we have not spent much money yet. We are just starting to incur costs this month, I will know more later on the full cost perspective."

"The solution is based on a licensing model."

"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."

"The solution requires a subscription."

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

More Databricks pricing and cost advice

"Watson Studio's pricing is reasonable for what you get."

"The pricing is generally reasonable and straightforward but can vary significantly depending on the specific workloads in use."

"IBM Watson Studio is an expensive solution."

"IBM Watson Studio is a reasonably priced product"

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Financial Services Firm

15%

Computer Software Company

12%

Manufacturing Company

10%

Educational Organization

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...

See all answers

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...

See all answers

What do you like most about IBM Watson Studio?

Stability-wise, it is a great tool.

See all answers

What is your experience regarding pricing and costs for IBM Watson Studio?

The pricing of Watson Studio is justified by the benefits and power it offers.

See all answers

What needs improvement with IBM Watson Studio?

One area that could be improved is the backup and restoration of the database and the overall database configuration. There were also challenges with programming the network extension in the last p...

See all answers

Comparisons

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Compared 11% of the time

Microsoft Power BI vs Databricks

Compared 9% of the time

Dremio vs Databricks

Compared 8% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Tableau vs Databricks

Compared 5% of the time

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Azure OpenAI vs IBM Watson Studio

Compared 14% of the time

Microsoft Azure Machine Learning Studio vs IBM Watson Studio

Compared 14% of the time

Google Vertex AI vs IBM Watson Studio

Compared 12% of the time

Amazon Comprehend vs IBM Watson Studio

Compared 4% of the time

Alteryx vs IBM Watson Studio

Compared 4% of the time

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Product Reports

Buyer's Guide

Databricks

April 2025

Download Databricks product report

Buyer's Guide

IBM Watson Studio

April 2025

Download IBM Watson Studio product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Watson Studio, IBM Data Science Experience, Data Science Experience, DSx

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 Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.

IBM

Sample Customers

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

GroupM, Accenture, Fifth Third Bank

Buyer's Guide

Databricks vs. IBM Watson Studio

April 2025

Free Report: Databricks vs. IBM Watson Studio

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