Darwin vs Databricks comparison

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Darwin

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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.

To learn more, read our detailed Darwin vs. Databricks Report (Updated: January 2025).

Darwin vs. Databricks

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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

Users reported financial savings and enhanced performance by shifting workloads to Databricks, spending less than on Hadoop.

No quotes available

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For a lot of different tasks, including machine learning, it is a nice solution.

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

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

7.1

Databricks customer service is praised for proactive support and quick responses, with comprehensive documentation reducing direct assistance needs.

No quotes available

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Whenever we reach out, they respond promptly.

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

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

7.4

Databricks is scalable and flexible, enabling efficient data processing and resource adjustment across diverse cloud platforms, despite cost concerns.

No quotes available

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The patches have sometimes caused issues leading to our jobs being paused for about six hours.

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

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 stable and reliable, though occasional update issues are quickly resolved, rating 8-9 in stability.

No quotes available

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They release patches that sometimes break our code.

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

Room For Improvement

Darwin users seek API integration, improved functionality, educational resources, and better automation for precision and transparency in AI processes.

Databricks users seek better visualization, integration, user interface, documentation, and scalability, while desiring improvements in pricing and features.

No quotes available

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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.

For more quotes and insights, download the Databricks report

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

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, pay-per-use pricing that varies by usage and platform, considered competitive yet sometimes expensive.

No quotes available

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No quotes available

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Valuable Features

Darwin excels in data cleaning, model-building, and integration, enhancing productivity and accessibility for non-experts in machine learning.

Databricks excels in data analytics with a user-friendly interface, SQL-Python integration, collaboration, scalability, and diverse language support.

No quotes available

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No quotes available

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Categories and Ranking

Darwin

Ranking in Data Science Platforms

27th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

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

Ranking in other categories

Streaming Analytics (1st)

Mindshare comparison

As of January 2025, in the Data Science Platforms category, the mindshare of Darwin is 0.3%, down from 0.3% compared to the previous year. The mindshare of Databricks is 19.1%, up from 18.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

AaronCooke

Founder at Helio Summit

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

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

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.

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Top Industries

By visitors reading reviews

Computer Software Company

25%

Real Estate/Law Firm

15%

Financial Services Firm

13%

Educational Organization

11%

Financial Services Firm

17%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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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 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

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Compared 100% of the time

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Amazon SageMaker vs Databricks

Compared 8% of the time

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Compared 7% of the time

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Product Reports

Darwin

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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.

Sample Customers

Hunt Oil, Hitachi High-Tech Solutions

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

Darwin vs. Databricks