Databricks vs SAS Enterprise Miner comparison

Databricks and SAS are both solutions in the Data Science Platforms category. Databricks is ranked #1 with an average rating of 8.4, while SAS is ranked #21. Databricks holds a 7.2% mindshare in Data Science Platforms, compared to SAS’s 2.1% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 93% of SAS users who would recommend it.

Databricks

Read 94 Databricks reviews

24,375 Views
9,506 Comparison Views

96% willing to recommend

SAS Enterprise Miner

Read 13 SAS Enterprise Miner reviews

2,341 Views
1,849 Comparison Views

93% willing to recommend

Databricks

SAS Enterprise Miner

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

SAS Enterprise Miner and Databricks are competing products in the data analysis space. Databricks is often viewed as a superior product due to its advanced features and scalability, offering more flexibility in various data environments.

Features: SAS Enterprise Miner offers robust predictive analytics, extensive model management capabilities, and a vast range of statistical operations. Databricks, meanwhile, provides seamless integration with big data tools, a collaborative workspace for data engineering tasks, and advanced machine learning functionalities.

Room for Improvement: SAS Enterprise Miner could enhance its scalability and cloud integration. An improvement in the user interface for modern usage patterns would be beneficial. Additionally, expanding machine learning features could add value. For Databricks, better offline capabilities would augment its utility in areas with limited internet access. Improving the intuitive nature of certain advanced settings could be beneficial, as well as offering more detailed built-in analytics reporting.

Ease of Deployment and Customer Service: SAS Enterprise Miner is traditionally deployed on-premise, offering comprehensive support services, extensive documentation, and direct phone support, which benefits enterprise setups. Databricks provides cloud-based deployment, enabling faster scalability and updates. Its wide range of online resources and community support reflect a cloud-native approach, offering flexibility and community-driven solutions.

Pricing and ROI: SAS Enterprise Miner generally involves a higher upfront setup cost with predictable long-term expenses, catering to enterprises seeking stable financial commitments. Databricks employs a pay-as-you-go pricing model, which can be more economical for dynamic workloads and provides greater flexibility, potentially leading to higher returns through efficient resource use.

To learn more, read our detailed Databricks vs. SAS Enterprise Miner Report (Updated: June 2026).

Buyer's Guide

Databricks vs. SAS Enterprise Miner

June 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

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 (4th), Data Management Platforms (DMP) (5th), Streaming Analytics (1st)

SAS Enterprise Miner

Ranking in Data Science Platforms

21st

Average Rating

7.6

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

Data Mining (7th)

Mindshare comparison

As of August 2026, in the Data Science Platforms category, the mindshare of Databricks is 7.2%, down from 15.3% compared to the previous year. The mindshare of SAS Enterprise Miner is 2.1%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
Databricks	7.2%
SAS Enterprise Miner	2.1%
Other	90.7%

Data Science Platforms

Featured Reviews

SimonRobinson

Governance And Engagement Lead

Improved data governance has enabled sensitive data tracking but cost management still needs work

I believe we could improve Databricks integration with cloud service providers. The impact of our current integration has not been particularly good, and it's becoming very expensive for us. The inefficiencies in our implementation, such as not shutting down warehouses when they're not in use or reserving the right number of credits, have led to increased costs. We made several beginner mistakes, such as not taking advantage of incremental loading and running overly complicated queries all the time. We should be using ETL tools to help us instead of doing it directly in Databricks. We need more experienced professionals to manage Databricks effectively, as it's not as forgiving as other platforms such as Snowflake. I think introducing customer repositories would facilitate easier implementation with Databricks.

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reviewer1352853

Executive Head of analytics at a retailer with 5,001-10,000 employees

A stable product that is easy to deploy and can be used for structured and unstructured data mining

We use the solution for predictive analytics to do structured and unstructured data mining I like the way the product visually shows the data pipeline. The product must provide better integration with cloud-native technologies. I have been using the solution for 20 years. The product is very…

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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

"Compared to other companies, they offer great support to their clients."

"It helps integrate data science and machine learning capabilities."

"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."

"It's easy to increase performance as required."

"What I like about Databricks is that it's one of the most popular platforms that give access to folks who are trying not just to do exploratory work on the data but also go ahead and build advanced modeling and machine learning on top of that."

"One of the newest features brought into this solution provides you with a way to solve, deploy, and train models using the platform itself, or it can connect to your Azure Machine Learning in order to train, deploy, and productionalize some of the machine learning models."

"I think what I value is more about the technology itself because you don't need to have too much knowledge to be able to use the solution."

"The technical support is good."

More Databricks pros

"The solution is able to handle quite large amounts of data beautifully."

"Most of the features, especially on the data analysis tool pack, are really good. The way they do clustering and output is great. You can do fairly elaborate outputs. The results, the ensembles, all of these, are fantastic."

"Overall it is a good solution."

"The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them."

"The most valuable feature is the decision tree creation."

"The setup is straightforward. Deployment doesn't take more than 30 minutes."

"I found the ease of use of the solution the most valuable. Additionally, other valuable features include: the user interface, power to extract data, compatibility with other technologies (specifically with PS400), and automation of several tasks."

"The data processing of the solution is very good, easy to use, both for enterprise and personal use."

More SAS Enterprise Miner pros

Cons

"Performance could be improved."

"Databricks can improve by making the documentation better."

"It would be better if it were faster. It can be slow, and it can be super fast for big data."

"I would like to see improvement with the UI. It is functional and useful, but it's a bit clunky at times."

"I think the automatic categorization of variables needs to be improved; the current functionality is not always efficiently identifying the features of the data that is collected."

"The solution has some scalability and integration limitations when consolidating legacy systems."

"It would be very helpful if Databricks could integrate with platforms in addition to Azure."

"I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data."

More Databricks cons

"Price of the product"

"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."

"The preparation of both the mining and modeling process could be improved. The solution requires data and will reflect data, but the preparation of the data is not useful for end-users; we ended up having to do the preparation in another tool."

"The stability isn't perfect. We have issues with accuracy in some AI forecasting areas, and the accuracy is not as good as the clients need it to be."

"The user interface of the solution needs improvement. It needs to be more visual."

"The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch."

"The initial setup is challenging if doing it for the first time."

"The license is really expensive. This solution is for large corporations because not everybody can afford it."

More SAS Enterprise Miner cons

Pricing and Cost Advice

"Databricks' cost could be improved."

"Databricks are not costly when compared with other solutions' prices."

"Databricks is a very expensive solution. Pricing is an area that could definitely be improved. They could provide a lower end compute and probably reduce the price."

"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."

"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."

"The solution is affordable."

"The billing of Databricks can be difficult and should improve."

"The cost is around $600,000 for 50 users."

More Databricks pricing and cost advice

"This solution is for large corporations because not everybody can afford it."

"The solution is expensive for an individual, but for an enterprise/institution (purchasing bulk licenses), it is not a high price for the use that will come from it."

"The solution must improve its licensing models."

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Manufacturing Company

10%

Computer Software Company

Healthcare Company

Financial Services Firm

19%

Construction Company

13%

Educational Organization

11%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	26
Midsize Enterprise	12
Large Enterprise	58

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	4
Large Enterprise	7

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 ...

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

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Compared 6% of the time

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Product Reports

Buyer's Guide

Databricks

August 2026

Download Databricks product report

Buyer's Guide

Data Mining

July 2026

Download SAS Enterprise Miner product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Enterprise Miner

Overview

Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.

Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.

What features make Databricks unique?

Notebook: Enables collaborative work among team members.
Delta Lake: Optimizes data management operations.
Unity Catalog: Provides governance over data assets.
Cloud Integration: Seamlessly connects with major cloud platforms.

What benefits can users expect from Databricks?

Versatility: Supports diverse applications in data science and engineering.
Performance: Delivers efficient handling of large-scale analytics tasks.
Collaboration: Enhances teamwork in data projects.
Unified Environment: Centralizes machine learning and analytics activities.

In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.

Databricks

SAS Enterprise Miner enables comprehensive data management and analytics, handling extensive data volumes with diverse algorithms for model creation. Its integration and flexibility in SAS code usage make it suitable for both enterprise and personal use.

SAS Enterprise Miner is recognized for its data pipeline visualization, data processing, and statistical modeling capabilities. Its user-friendly GUI and automation support data mining tasks, decision tree creation, and clustering. However, improvements are needed in its interface visualization, affordability, technical support, and integration with languages like Python and cloud-native tech. Enhanced performance, visualization, and model development auditing, along with text analytics in the main license, are desirable upgrades. Integration with Microsoft SQL and combined offerings remains a priority.

What are SAS Enterprise Miner's most important features?

Data Management: Handles large data volumes efficiently.
Analytics: Integrates diverse algorithms for accurate model creation.
Integration: Supports flexible incorporation of SAS code.
Automation: Enhances data mining tasks through automation.
Clustering: Facilitates extensive clustering operations.

What benefits or ROI should users look for in reviews?

Performance: Delivers remarkable efficiency in data processing tasks.
Compatibility: Seamless integration with other technologies.
Flexibility: Adaptability for enterprise and personal use cases.
User-Friendly: Intuitive GUI aids in simplifying complex tasks.

SAS Enterprise Miner is applied across industries like banking, insurance, and healthcare for data mining, machine learning, and predictive analytics. It aids in activities such as text mining, fraud modeling, and forecasting model creation, handling structured and unstructured data, and performing ad hoc analysis to model business processes and analyze data clusters.

SAS

Sample Customers

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

Generali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare

Buyer's Guide

Databricks vs. SAS Enterprise Miner

June 2026

Free Report: Databricks vs. SAS Enterprise Miner

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