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Darwin vs Databricks comparison

SparkCognition and Databricks are both solutions in the Data Science Platforms category. SparkCognition is ranked #26, while Databricks is ranked #1 with an average rating of 8.3. SparkCognition holds a 0.3% mindshare in Data Science Platforms, compared to Databricks’s 16.5% mindshare. Additionally, 100% of SparkCognition users are willing to recommend the solution, compared to 96% of Databricks users who would recommend it.
Darwin
Read 8 Darwin reviews
  • 298 Views
  • 89 Comparison Views
100% willing to recommend
Databricks
Read 91 Databricks reviews
  • 12,650 Views
  • 7,192 Comparison Views
96% willing to recommend
 

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Databricks and Darwin compete in the data analytics and machine learning platform category. Databricks has the upper hand with its versatility and scalability, while Darwin excels in automated model generation.

Features: Databricks offers robust capabilities such as Delta data format optimization, collaborative notebooks, and efficient machine learning libraries. It supports multiple programming languages and integrates with Azure Machine Learning. Darwin provides automated model generation, streamlining the process for non-data scientists to build and iterate models efficiently.

Room for Improvement: Databricks could enhance its visualization capabilities and deeper integration with Power BI and Tableau. Its pricing is high, and integration with data sources could be improved. Darwin's dashboards need to be more user-friendly for broader use, and its ability to handle unsupervised models requires enhancement.

Ease of Deployment and Customer Service: Databricks supports various deployment options, including public, private, and hybrid clouds but faces scalability challenges. Its customer service has mixed reviews. Darwin's documentation reduces the need for technical support but has limited deployment flexibility, primarily operating on public cloud environments.

Pricing and ROI: Databricks' pay-per-use model can be expensive but offers ease of use and scalability, positively impacting ROI. Darwin is considered cost-effective, especially compared to hiring data scientists, providing value through streamlined model development and deployment while typically only incurring licensing costs.

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To learn more, read our detailed Darwin vs. Databricks Report (Updated: June 2025).
Buyer's Guide
Darwin vs. Databricks
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:
 

ROI

Sentiment score
6.2
Companies find Darwin efficient, preventing revenue loss and enhancing machine learning, with returns two to three times higher.
Sentiment score
6.5
Databricks enhances efficiency and ROI, offering scalable solutions and cost savings over traditional Hadoop with easy setup.
No quotes available
For more quotes and insights, download the Darwin report
When it comes to big data processing, I prefer Databricks over other solutions.
For a lot of different tasks, including machine learning, it is a nice solution.
For more quotes and insights, download the Databricks report
 

Customer Service

Sentiment score
8.4
Darwin's support is highly responsive and efficient, quickly resolving issues and providing valuable guidance, ensuring customer satisfaction.
Sentiment score
7.2
Databricks' support is generally praised for responsiveness, though some note delays, with resources often sufficient for independent problem-solving.
No quotes available
For more quotes and insights, download the Darwin report
Whenever we reach out, they respond promptly.
As of now, we are raising issues and they are providing solutions without any problems.
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
For more quotes and insights, download the Databricks report
Parag Bhosale - PeerSpot reviewer
BP
LK
 

Scalability Issues

Sentiment score
6.7
Darwin scales well with challenges on large datasets; plans for expansion need internal changes for wider departmental adoption.
Sentiment score
7.5
Databricks is praised for its adaptability, scalability, automation features, and performance across industries but needs improved autoscaling control.
No quotes available
For more quotes and insights, download the Darwin report
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
I would rate the scalability of this solution as very high, about nine out of ten.
Databricks is an easily scalable platform.
For more quotes and insights, download the Databricks report
Parag Bhosale - PeerSpot reviewer
LK
BP
 

Stability Issues

Sentiment score
7.0
Darwin's stability has improved, boasting 99% availability, though some issues persist; support is responsive, yet enhancements continue.
Sentiment score
7.7
Databricks is highly rated for stability and performance, with occasional minor issues often due to user or external factors.
No quotes available
For more quotes and insights, download the Darwin report
They release patches that sometimes break our code.
Databricks is definitely a very stable product and reliable.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
For more quotes and insights, download the Databricks report
Parag Bhosale - PeerSpot reviewer
AC
BP
 

Room For Improvement

Darwin users seek API integration, improved functionality, educational resources, and better automation for precision and transparency in AI processes.
Databricks should improve visualization, integration, user experience, and scalability, addressing concerns about pricing, error messages, and onboarding.
No quotes available
For more quotes and insights, download the Darwin report
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
They're now coming up with their IBI dashboard, and I think they're on the right track to improve that even further.
It would be beneficial to have utilities where code snippets are readily available.
For more quotes and insights, download the Databricks report
Rama Subba Reddy Thavva - PeerSpot reviewer
LK
IshwarSukheja - PeerSpot reviewer
 

Setup Cost

Darwin's licensing costs are significant yet often seen as valuable, with predictable setup fees and optional costs for integrations.
Databricks offers flexible, often expensive pricing, mitigated by cloud deployment and tiered licensing, with varied user cost experiences.
 

Valuable Features

Darwin excels in data cleaning, model-building, and integration, enhancing productivity and accessibility for non-experts in machine learning.
Databricks offers an intuitive interface for data processing, integrating SQL, Python, and features like Delta Lake and MLflow.
No quotes available
For more quotes and insights, download the Darwin report
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
Databricks' capability to process data in parallel enhances data processing speed.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
For more quotes and insights, download the Databricks report
LK
ShubhamSharma7 - PeerSpot reviewer
BP
 

Categories and Ranking

Darwin
Ranking in Data Science Platforms
26th
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
8
Ranking in other categories
No ranking in other categories
Databricks
Ranking in Data Science Platforms
1st
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
91
Ranking in other categories
Cloud Data Warehouse (8th), Streaming Analytics (1st)
 

Mindshare comparison

As of June 2025, in the Data Science Platforms category, the mindshare of Darwin is 0.3%, up from 0.3% compared to the previous year. The mindshare of Databricks is 16.5%, down from 19.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

AC
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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ShubhamSharma7 - PeerSpot reviewer
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
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
18%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

Ask a question
Earn 20 points
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
 

Comparisons

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IBM Watson Studio vs Darwin
Compared 50% of the time
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Dataiku vs Databricks
Compared 11% of the time
Microsoft Power BI vs Databricks Logo
Microsoft Power BI vs Databricks
Compared 9% of the time
Informatica PowerCenter vs Databricks Logo
Informatica PowerCenter vs Databricks
Compared 7% of the time
Dremio vs Databricks Logo
Dremio vs Databricks
Compared 7% of the time
Tableau vs Databricks Logo
Tableau vs Databricks
Compared 5% of the time
More Databricks Competitors
 

Product Reports

Buyer's Guide
Darwin
June 2025
Darwin reportDownload Darwin product report
Buyer's Guide
Databricks
June 2025
Databricks reportDownload Databricks product report
 

Also Known As

No data available
Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
 

Overview

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.

SparkCognition

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
 

Sample Customers

Hunt Oil, Hitachi High-Tech Solutions
Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
Buyer's Guide
Darwin vs. Databricks
June 2025
Free Report: Darwin vs. Databricks
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