Darwin vs Dremio comparison

SparkCognition and Dremio are both solutions in the Data Science Platforms category. SparkCognition is ranked #27, while Dremio is ranked #9 with an average rating of 8.6. SparkCognition holds a 0.3% mindshare in Data Science Platforms, compared to Dremio’s 4.3% mindshare. Additionally, 100% of SparkCognition users are willing to recommend the solution, compared to 100% of Dremio users who would recommend it.

Darwin

Read 8 Darwin reviews

324 Views
155 Comparison Views

100% willing to recommend

Dremio

Read 8 Dremio reviews

2,546 Views
1,908 Comparison Views

100% willing to recommend

Darwin

Dremio

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 5, 2024

Darwin and Dremio compete in the data processing and analytics space. Darwin seems to have the upper hand due to its advanced model processing capabilities and automatic dataset quality assessments, enhancing productivity and efficiency.

Features: Darwin offers advanced model processing capabilities, automatic assessments of dataset quality, and interactive suggestions for data improvements. Dremio integrates with various data storage systems, manages data lineage, and enables flexible query building and data visualization.

Room for Improvement: Darwin requires better data integration options, improved dashboards, and enhanced user-defined controls. Dremio needs stronger support for Delta connectors, better performance for complex queries, and more comprehensive documentation.

Ease of Deployment and Customer Service: Darwin provides flexible deployment options across private, public, and on-premises environments with highly praised technical support. Dremio also offers flexible deployment in public, hybrid, and on-premises environments, with commendable technical support.

Pricing and ROI: Darwin is cost-effective compared to hiring data scientists, showing significant ROI despite being perceived as expensive in some markets. Dremio is competitively priced but considered costly due to licensing, offering value through its flexible data management capabilities.

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

Buyer's Guide

Darwin vs. Dremio

March 2025

Download the complete report

Helped 846,617 peers since 2012

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

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

Dremio

Ranking in Data Science Platforms

9th

Average Rating

8.6

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (9th)

Mindshare comparison

As of April 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 Dremio is 4.3%, up from 2.8% 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.

Read full review

KamleshPant

Senior Software Architect at USEReady

Solution offers quick data connection with an edge in computation

It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS. It is a similar experience between the based application and cloud-based application. You just get the source, connect the data, get visualization, get connected, and do whatever you want. They say data reflection is one way where they do the caching and all that. Starburst also does the caching. In Starburst, you have a data product. Here, the data product comes from a reflection perspective. The y are working on a columnar memory map, columnar computation. That will have some edge in computation.

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

"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 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."

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

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

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

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

"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 most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory."

"It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS."

"Overall, you can rate it as eight out of ten."

"We primarily use Dremio to create a data framework and a data queue."

"Dremio gives you the ability to create services which do not require additional resources and sterilization."

"Dremio allows querying the files I have on my block storage or object storage."

"Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it."

"Dremio is very easy to use for building queries."

More Dremio pros

Cons

"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."

"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."

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

"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."

"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."

"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 analyze function takes a lot of time."

"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."

"Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake."

"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."

"They need to have multiple connectors."

"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people."

"Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement."

"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."

"It shows errors sometimes."

"They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today."

More Dremio cons

Pricing and Cost Advice

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

"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."

"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."

"Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."

"Dremio is less costly competitively to Snowflake or any other tool."

See which vendors are best for you

Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.

See recommendations

846,617 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

No data available

Financial Services Firm

32%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

Ask a question

Earn 20 points

What do you like most about Dremio?

Dremio allows querying the files I have on my block storage or object storage.

See all answers

What is your experience regarding pricing and costs for Dremio?

The licensing is very expensive. We need a license to scale as we are currently using the community version.

See all answers

What needs improvement with Dremio?

They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today. They don't have Salesforce connectivity. However, Starburst do...

See all answers

Comparisons

IBM Watson Studio vs Darwin

Compared 100% of the time

More Darwin Competitors

Databricks vs Dremio

Compared 51% of the time

Snowflake vs Dremio

Compared 15% of the time

Starburst Enterprise vs Dremio

Compared 6% of the time

Microsoft Power BI vs Dremio

Compared 4% of the time

Dataiku vs Dremio

Compared 4% of the time

More Dremio Competitors

Product Reports

Buyer's Guide

Darwin

April 2025

Download Darwin product report

Buyer's Guide

Dremio

April 2025

Download Dremio product report

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

Dremio is a data analytics platform designed to simplify and expedite the data analysis process by enabling direct querying across multiple data sources without the need for data replication. This solution stands out due to its approach to data lake transformation, offering tools that allow users to access and query data stored in various formats and locations as if it were all in a single relational database.

At its core, Dremio facilitates a more streamlined data management experience. It integrates easily with existing data lakes, allowing organizations to continue using their storage of choice, such as AWS S3, Microsoft ADLS, or Hadoop, without data migration. Dremio supports SQL queries, which means it seamlessly integrates with familiar BI tools and data science frameworks, enhancing user accessibility and reducing the learning curve typically associated with adopting new data technologies.

What Are Dremio's Key Features?

Data Reflections: Reduces query times by creating optimized representations of source data, which can accelerate performance without the complexity of traditional data warehousing solutions.
Semantic Layer: Allows users to define business metrics and dimensions centrally, ensuring consistency and governance across all analytics tools.
Built-in Security Features: Provides robust security measures, including column- and row-level security, ensuring compliance with data governance and privacy standards.
Support for Multiple Data Formats and Sources: Enables querying directly against a variety of data formats (Parquet, JSON, etc.) and sources without the need for conversion or replication.

What Benefits Should Users Expect?

When evaluating Dremio, potential users should look for feedback on its query performance, especially in environments with large and complex data sets. Reviews might highlight the efficiency gains from using Dremio’s data reflections and its ability to integrate with existing BI tools without significant changes to underlying data structures. Also, check how other users evaluate its ease of deployment and scalability, particularly in hybrid and cloud environments.

How is Dremio Implemented Across Different Industries?

Dremio is widely applicable across various industries, including finance, healthcare, and retail, where organizations benefit from rapid, on-demand access to large volumes of data spread across disparate systems. For instance, in healthcare, Dremio can be used to analyze patient outcomes across different data repositories, improving treatment strategies and operational efficiencies.

What About Dremio’s Pricing, Licensing, and Support?

Dremio offers a flexible pricing model that caters to different sizes and types of businesses, including a free community version for smaller teams and proof-of-concept projects. Their enterprise version is subscription-based, with pricing varying based on the deployment scale and support needs. Customer support is comprehensive, featuring dedicated assistance, online resources, and community support.

Dremio

Sample Customers

Hunt Oil, Hitachi High-Tech Solutions

UBS, TransUnion, Quantium, Daimler, OVH

Buyer's Guide

Darwin vs. Dremio

March 2025

Free Report: Darwin vs. Dremio

Find out what your peers are saying about Darwin vs. Dremio and other solutions. Updated: March 2025.

DOWNLOAD NOW

846,617 professionals have used our research since 2012.

See our Darwin vs. Dremio report.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.