Databricks vs TIBCO Data Science comparison

Databricks and TIBCO are both solutions in the Data Science Platforms category. Databricks is ranked #1 with an average rating of 8.4, while TIBCO is ranked #25. Databricks holds a 13.9% mindshare in Data Science Platforms, compared to TIBCO’s 0.7% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of TIBCO users who would recommend it.

Databricks

Read 91 Databricks reviews

20,775 Views
8,933 Comparison Views

96% willing to recommend

TIBCO Data Science

Read 4 TIBCO Data Science reviews

425 Views
408 Comparison Views

100% willing to recommend

Databricks

TIBCO Data Science

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Databricks and TIBCO Data Science compete in the data science and analytics space. Databricks appears to have an advantage in scalability and collaborative features, whereas TIBCO Data Science is favored for its comprehensive analytics and integration capabilities.

Features: Databricks offers collaborative features, seamless integration with data sources, and strong machine learning capabilities. TIBCO Data Science focuses on providing analytics options, real-time insights, and robust integration within enterprise environments.

Room for Improvement: Databricks could improve integration with enterprise systems, offer more advanced analytics options, and enhance its real-time data processing capabilities. TIBCO Data Science may need to work on reducing deployment time, improving its cost-effectiveness, and offering more agile scaling options.

Ease of Deployment and Customer Service: Databricks provides a cloud-based deployment model that allows quick setup and flexible scaling, with a responsive customer support system. TIBCO Data Science offers both on-premise and cloud-deployment models with robust integration with enterprise systems, which may require more initial setup time, along with dedicated service support.

Pricing and ROI: Databricks presents a flexible pricing model with lower initial setup costs, focusing on delivering improved ROI through scalability. TIBCO Data Science may involve higher initial investment but delivers high ROI for companies needing comprehensive data solutions, potentially offering greater long-term value for complex data needs.

To learn more, read our detailed Databricks vs. TIBCO Data Science Report (Updated: September 2025).

Buyer's Guide

Databricks vs. TIBCO Data Science

September 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 (9th), Streaming Analytics (1st)

TIBCO Data Science

Ranking in Data Science Platforms

25th

Average Rating

7.6

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2025, in the Data Science Platforms category, the mindshare of Databricks is 13.9%, down from 19.2% compared to the previous year. The mindshare of TIBCO Data Science is 0.7%, up from 0.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Databricks	13.9%
TIBCO Data Science	0.7%
Other	85.4%

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.

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Veeresh-SHRINGARI

Portfolio Manager, AI & ML at a healthcare company with 10,001+ employees

A straightforward initial setup and good reporting but needs better documentation

It would be ideal if it could be put onto the NMP where you can make more of an analysis. Right now, people don't have enough time to go through the report and make an analysis. It should provide the information of what is on the report into some kind of a dialogue form. Then, a person can ask certain questions and it could interactively give the required report. In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues. If you are doing certain operations of RDBMS, you suffer in terms of the latency of the data. That can be improved upon. Users should be able to cross tables of the web pages they are developing on Spotfire and this needs to be really easy and convenient. Right now, you need to do a lot of tweaks. The solution should be more user-friendly and require fewer tweaks, extensions, and workarounds.

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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 features of the solution are the hardware and the resources it quickly provides without much hassle."

"It is a cost-effective solution."

"I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature."

"There are good features for turning off clusters."

"Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user."

"We have the ability to scale, collaborate and do machine learning."

"The time travel feature is the solution's most valuable aspect."

"The most valuable feature of Databricks is the integration with Microsoft Azure."

More Databricks pros

"We like the way we can drill down into each report to get back data on each project. From the portfolio level, I can see what is happening on it. That is a really important feature. I can look at indirect costs, for example, which are hitting each CIO portfolio. It's good to be able to see actual resources in terms of time as well as cost."

"The most valuable feature is the performance."

"The idea that you don't have to generate reports each day but they are sent automatically is great."

"The most valuable feature is the ease of setting up visualizations."

Cons

"Implementation of Databricks is still very code heavy."

"The initial setup is difficult."

"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."

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

"It would be better if it were faster. It can be slow, and it can be super fast for big data. But for small data, sometimes there is a sub-second response, which can be considered slow. In the next release, I would like to have automatic creation of APIs because they don't have it at the moment, and I spend a lot of time building them."

"I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement."

"The integration and query capabilities can be improved."

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

More Databricks cons

"In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues."

"The scripting for customization could be improved."

"I would like the visualization for the map of countries to be more easily configurable."

"Additional templates would help to get things moving more quickly in terms of getting the reports out."

Pricing and Cost Advice

"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."

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

"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."

"The pricing depends on the usage itself."

"My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month."

"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."

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

"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."

More Databricks pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

Manufacturing Company

Healthcare Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	25
Midsize Enterprise	12
Large Enterprise	56

No data available

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 ...

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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 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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Comparisons

Microsoft Power BI vs Databricks

Compared 9% of the time

Dataiku vs Databricks

Compared 8% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Dremio vs Databricks

Compared 5% of the time

Tableau Enterprise vs Databricks

Compared 4% of the time

More Databricks Competitors

SAS Enterprise Miner vs TIBCO Data Science

Compared 25% of the time

Alteryx vs TIBCO Data Science

Compared 24% of the time

Amazon SageMaker vs TIBCO Data Science

Compared 22% of the time

More TIBCO Data Science Competitors

Product Reports

Buyer's Guide

Databricks

September 2025

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Buyer's Guide

Data Science Platforms

September 2025

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Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Alpine Data Chorus

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

TIBCO Spotfire Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms.

TIBCO

Sample Customers

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

Havas Media, Tipping Point Community, eviCore

Buyer's Guide

Databricks vs. TIBCO Data Science

September 2025

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