Azure Databricks vs Darwin comparison

Microsoft and SparkCognition are both solutions in the Data Science Platforms category. Microsoft is ranked #20 with an average rating of 9.0, while SparkCognition is ranked #25. Additionally, 66% of Microsoft users are willing to recommend the solution, compared to 100% of SparkCognition users who would recommend it.

Azure Databricks

Read 3 Azure Databricks reviews

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97 Comparison Views

66% willing to recommend

Darwin

Read 8 Darwin reviews

844 Views
836 Comparison Views

100% willing to recommend

Azure Databricks

Darwin

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Executive Summary

We performed a comparison between Azure Databricks and Darwin 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 Azure Databricks vs. Darwin Report (Updated: March 2026).

Buyer's Guide

Azure Databricks vs. Darwin

March 2026

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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

Azure Databricks

Ranking in Data Science Platforms

20th

Average Rating

8.0

Reviews Sentiment

3.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Darwin

Ranking in Data Science Platforms

25th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

VishnuReddy2

Consulting Enterprise Architect at R2V2.ai

Unified data platform has supported real-time analytics and advanced machine learning workflows

The real-time processing with Azure Databricks is supported through integration from external systems, for which we have to go with tools such as Matillion's HVR or Kafka. I have experience using HVR, high-volume replication. You get real-time data replicated into Azure Databricks using these tools. When looking for performance metrics in Azure Databricks, it depends on the processing. It can process millions of records quickly, and it is driven by the Spark framework, which is pretty strong in terms of framework perspective. The columnar database is another strong feature which helps enhance its performance. Prior to the introduction of Unity Catalog, there was no metadata capability in Azure Databricks. It was very simplistic, but now with the Unity Catalog introduction and Delta Sharing capabilities, Azure Databricks is at the top-notch at this point in time. In comparison, SAP BW is a little bit more mature because apart from RBAC, it gives data-level authorization, which is a little bit not that great in Azure Databricks at this point in time.

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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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Quotes from Members

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

Pros

"The best features in Azure Databricks for me are that it's easy to use, flexible, and has fast processing, and you can use multiple data types."

"Regarding the learning curve, it is a good technology; it is the first time I am working on a cloud platform, and before that, I have not worked on any data engineering tool that is on cloud, so it is good learning."

"Azure Databricks gives the capability to handle a lot of big data use cases and machine learning use cases, but machine learning use cases need quite a lot of compute power, and that is where the cost spikes up."

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

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

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

"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."

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

"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."

Cons

"I have given the product a rating of six out of ten just because I do not use all of the functionalities, and I see some direction for improvement as well; also, every product has something to improve, and I have not used many features in this product."

"At this point, I cannot comment on the cost being ideal; it is on the higher side, but in the cloud-based environment, compared to on-premise, it could be far lesser in cost."

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

"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."

"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."

"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 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 analyze function takes a lot of time."

"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."

Pricing and Cost Advice

Information not available

"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."

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

"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."

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

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Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	6
Large Enterprise	2

Questions from the Community

What is your experience regarding pricing and costs for Azure Databricks?

Regarding the licensing cost of Azure Databricks, it has evolved quite a lot. The compute is the biggest cost, as with any other big data solutions. The storage cost is almost minimal or negligible...

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What needs improvement with Azure Databricks?

Overall, my experience has been positive with Azure Databricks; they have many features, but there is no use case for me to use those features, such as Delta Live Tables and Genie. In my opinion, I...

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What is your primary use case for Azure Databricks?

The primary use cases for me are the reportings I have to do, so I need to ingest data from the file and create reports. I do not utilize it for real-time data processing. I have not integrated Azu...

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Data Science Platforms

March 2026

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Darwin

March 2026

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Overview

Azure Databricks is an advanced analytics platform combining the best of Microsoft's Azure and Apache Spark. It provides a powerful solution for big data processing, machine learning, and collaborative data projects, designed to help organizations unlock insights and foster innovation.

Azure Databricks integrates seamlessly with Azure services, offering end-to-end data solutions for enterprises. Its collaborative environment supports data engineers and scientists, facilitating faster data preparation and model training. The platform enhances productivity with automated cluster management and simplified data workflows, fostering data-driven decision-making.

What are the key features of Azure Databricks?

Collaborative Workspace: Enables teamwork between data engineers and scientists through shared notebooks and real-time collaboration.
Auto-scaling Clusters: Automatically scales computing resources up or down based on demand, optimizing performance and cost-efficiency.
Machine Learning Support: Provides tools to develop and deploy machine learning models efficiently, leveraging the power of Apache Spark.
Azure Integration: Seamlessly connects with Azure services like Azure Data Lake Storage, Azure Synapse Analytics, and Power BI.

What benefits and ROI should users consider?

Scalability: Easily scales to accommodate growing data needs without substantial infrastructure changes.
Cost Efficiency: Optimizes resource usage through automated scaling, reducing operational costs.
Increased Productivity: Enhances team productivity with collaborative features and streamlined data processing workflows.
Data-Driven Insights: Empowers organizations to derive actionable insights from big data through effective analytics and machine learning capabilities.

Azure Databricks is widely implemented across industries like finance, healthcare, and retail. It supports financial institutions in fraud detection and risk analytics, healthcare providers in patient data analysis and operational efficiency, and retailers in predictive analytics for inventory and customer engagement. Its ability to handle massive datasets and provide real-time analytics solutions makes it invaluable in transforming industry-specific processes.

Microsoft

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

Sample Customers

Information Not Available

Hunt Oil, Hitachi High-Tech Solutions

Buyer's Guide

Azure Databricks vs. Darwin

March 2026

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