What users are saying about
50 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>Score 8.7 out of 100
Based on 50 reviews and ratings
83 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>Score 7.8 out of 100
Based on 83 reviews and ratings
Feature Set Ratings
BI Standard Reporting
Databricks Lakehouse Platform
Feature Set Not Supported
N/A
9.5
JMP Statistical Discovery Software from SAS
95%
JMP Statistical Discovery Software from SAS ranks higher in 2/2 features
JMP Statistical Discovery Software from SAS ranks higher in 2/2 features
Pixel Perfect reports
N/A
0 Ratings
10.0
100%
1 Rating
Customizable dashboards
N/A
0 Ratings
9.0
90%
9 Ratings
Ad-hoc Reporting
Databricks Lakehouse Platform
Feature Set Not Supported
N/A
7.5
JMP Statistical Discovery Software from SAS
75%
JMP Statistical Discovery Software from SAS ranks higher in 4/4 features
JMP Statistical Discovery Software from SAS ranks higher in 4/4 features
Drill-down analysis
N/A
0 Ratings
7.7
77%
13 Ratings
Formatting capabilities
N/A
0 Ratings
6.6
66%
12 Ratings
Integration with R or other statistical packages
N/A
0 Ratings
7.7
77%
10 Ratings
Report sharing and collaboration
N/A
0 Ratings
8.0
80%
13 Ratings
Report Output and Scheduling
Databricks Lakehouse Platform
Feature Set Not Supported
N/A
8.6
JMP Statistical Discovery Software from SAS
86%
JMP Statistical Discovery Software from SAS ranks higher in 4/4 features
JMP Statistical Discovery Software from SAS ranks higher in 4/4 features
Publish to Web
N/A
0 Ratings
8.9
89%
9 Ratings
Publish to PDF
N/A
0 Ratings
8.5
85%
12 Ratings
Report Versioning
N/A
0 Ratings
7.0
70%
1 Rating
Report Delivery Scheduling
N/A
0 Ratings
10.0
100%
1 Rating
Data Discovery and Visualization
Databricks Lakehouse Platform
Feature Set Not Supported
N/A
8.4
JMP Statistical Discovery Software from SAS
84%
JMP Statistical Discovery Software from SAS ranks higher in 3/3 features
JMP Statistical Discovery Software from SAS ranks higher in 3/3 features
Pre-built visualization formats (heatmaps, scatter plots etc.)
N/A
0 Ratings
8.0
80%
16 Ratings
Location Analytics / Geographic Visualization
N/A
0 Ratings
9.0
90%
13 Ratings
Predictive Analytics
N/A
0 Ratings
8.0
80%
13 Ratings
Attribute Ratings
- Databricks Lakehouse Platform (Unified Analytics Platform) is rated higher in 1 area: Likelihood to Recommend
- JMP Statistical Discovery Software from SAS is rated higher in 2 areas: Usability, Support Rating
Likelihood to Recommend
8.7
Databricks Lakehouse Platform
87%
15 Ratings
7.4
JMP Statistical Discovery Software from SAS
74%
28 Ratings
Likelihood to Renew
Databricks Lakehouse Platform
N/A
0 Ratings
10.0
JMP Statistical Discovery Software from SAS
100%
16 Ratings
Usability
9.0
Databricks Lakehouse Platform
90%
3 Ratings
10.0
JMP Statistical Discovery Software from SAS
100%
5 Ratings
Availability
Databricks Lakehouse Platform
N/A
0 Ratings
10.0
JMP Statistical Discovery Software from SAS
100%
2 Ratings
Performance
Databricks Lakehouse Platform
N/A
0 Ratings
10.0
JMP Statistical Discovery Software from SAS
100%
2 Ratings
Support Rating
7.5
Databricks Lakehouse Platform
75%
2 Ratings
9.2
JMP Statistical Discovery Software from SAS
92%
14 Ratings
Online Training
Databricks Lakehouse Platform
N/A
0 Ratings
7.9
JMP Statistical Discovery Software from SAS
79%
3 Ratings
Implementation Rating
Databricks Lakehouse Platform
N/A
0 Ratings
9.6
JMP Statistical Discovery Software from SAS
96%
4 Ratings
Contract Terms and Pricing Model
8.0
Databricks Lakehouse Platform
80%
1 Rating
JMP Statistical Discovery Software from SAS
N/A
0 Ratings
Product Scalability
Databricks Lakehouse Platform
N/A
0 Ratings
10.0
JMP Statistical Discovery Software from SAS
100%
1 Rating
Professional Services
10.0
Databricks Lakehouse Platform
100%
1 Rating
JMP Statistical Discovery Software from SAS
N/A
0 Ratings
Likelihood to Recommend
Databricks Lakehouse Platform
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.

Verified User
Engineer in Engineering
Computer Software Company, 1001-5000 employeesJMP Statistical Discovery Software from SAS
It is perfectly suited for statistical analyses, but I would not recommend JMP for users who do not have a statistical background. As previously stated, the learning curve is exceptionally steep, and I think that it would prove to be too steep for those without statistical background/knowledge

Verified User
Analyst in Research & Development
Education Management Company, 10,001+ employeesPros
Databricks Lakehouse Platform
- Process raw data in One Lake (S3) env to relational tables and views
- Share notebooks with our business analysts so that they can use the queries and generate value out of the data
- Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
- Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers

Verified User
Team Lead in Engineering
Financial Services Company, 10,001+ employeesJMP Statistical Discovery Software from SAS
- JMP is designed from the ground-up to be a tool for analysts who do not have PhDs in Statistics without in anyway "dumbing down" the level of statistical analysis applied. In fact, JMP operationalizes the most advanced statistical methods. JMP's design is centred on the JMP data table and dialog boxes. It is data focused not jargon-focussed. So, unlike other software where you must choose the correct statistical method (eg. contingency, ANOVA, linear regression, etc.), with JMP you simply assign the columns in a dialog into roles in the analysis and it chooses the correct statistical method. It's a small thing but it reflects the thinking of the developers: analysts know their data and should only have to think about their data. Analyses should flow from there.
- JMP makes most things interactive and visual. This makes analyses dynamic and engaging and obviates the complete dependence on understanding p-values and other statistical concepts(though they are all there) that are often found to be foreign or intimidating.
- One of the best examples of this is JMP's profiler. Rather than looking at static figures in a spreadsheet, or a series of formulas, JMP profiles the formulas interactively. You can monitor the effect of changing factors (Xs) and see how they interact with other factors and the responses. You can also specify desirability (maximize, maximize, match-target) and their relative importances to find factor settings that are optimal. I have spent many lengthy meetings working with the profiler to review design and process options with never a dull moment.
- The design of experiments (DOE) platform is simply outstanding and, in fact, the principal developers of it have won several awards. Over the last 15 years, using methods broadly known as an "exchange algorithm," JMP can create designs that are far more flexible than conventional designs. This means, for example, that you can create a design with just the interactions that are of interest; you can selectively choose those interactions that are not of interest and drop collecting their associated combinations.
- Classical designs are rigid. For example, a Box-Benhken or other response surface design can have only continuous factors. What if you want to investigate these continuous factors along with other categorical factors such as different categorical variables such as materials or different furnace designs and look at the interaction among all factors? This common scenario cannot be handled with conventional designs but are easily accommodated with JMP's Custom DOE platform.
- The whole point of DOE is to be able to look at multiple effects comprehensively but determine each one's influence in near or complete isolation. The custom design platform, because it produces uniques designs, provides the means to evaluate just how isolated the effects are. This can be done before collecting data because this important property of the DOE is a function of the design, not the data. By evaluating these graphical reports of the quality of the design, the analyst can make adjustments, adding or reducing runs, to optimize cost, effort and expected learnings.
- Over the last number of releases of JMP, which appear about every 18 months now, they have skipped the dialog boxes to direct, drag-and-drop analyses for building graphs and tables as well as Statistical Process Control Charts. Interactivity such as this allows analysts to "be in the moment." As with all aspects of JMP, they are thinking of their subject matter without the cumbersomeness associated with having to think about statistical methods. It's rather like a CEO thinking about growing the business without having to think about every nuance and intricacy of accounting. The statistical thinking is burned into the design of JMP.
- Without data analysis is not possible. Getting data into a situation where it can be analyzed can be a major hassle. JMP can pull data from a variety of sources including Excel spreadsheets, CSV, direct data feeds and databases via ODBC. Once the data is in JMP it has all the expected data manipulation capabilities to form it for analysis.
- Back in 2000 JMP added a scripting language (JMP Scripting Language or JSL for short) to JMP. With JSL you can automate routine analyses without any coding, you can add specific analyses that JMP does not do out of the box and you can create entire analytical systems and workflows. We have done all three. For example, one consumer products company we are working with now has a need for a variant of a popular non-parametric analysis that they have employed for years. This method will be found in one of the menus and appear as if it were part of JMP to begin with. As for large systems, we have written some that are tens of thousands of lines that take the form of virtual labs and process control systems among others.
- JSL applications can be bundled and distributed as JMP Add-ins which make it really easy for users to add to their JMP installation. All they need to do is double-click on the add-in file and it's installed. Pharmaceutical companies and others who are regulated or simply want to control the JMP environment can lock-down JMP's installation and prevent users from adding or changing functionality. Here, add-ins can be distributed from a central location that is authorized and protected to users world-wide.
- JMP's technical support is second to none. They take questions by phone and email. I usually send email knowing that I'll get an informed response within 24 hours and if they cannot resolve a problem they proactively keep you informed about what is being done to resolve the issue or answer your question.
President
Predictum Inc.Information Technology and Services, 11-50 employees
Cons
Databricks Lakehouse Platform
- Better Localized Testing
- When they were primarily OSS Spark; it was easier to test/manage releases versus the newer DB Runtime. Wish there was more configuration in Runtime less pick a version.
- Graphing Support went non-existent; when it was one of their compelling general engine.

Verified User
Director in Information Technology
Financial Services Company, 201-500 employeesJMP Statistical Discovery Software from SAS
- In general JMP is much better fit for a general "data mining" type application. If you want a specific statistics based toolbox, (meaning you just want to run some predetermined test, like testing for a different proportion) then JMP works, but is not the best. JMP is much more suited to taking a data set and starting from "square 1" and exploring it through a range of analytics.
- The CPK (process capability) module output is shockingly poor in JMP. This sticks out because, while as a rule everything in JMP is very visual and presentable, the CPK graph is a single-line-on-grey-background drawing. It is not intuitive, and really doesn't tell the story. (This is in contrast with a capability graph in Minitab, which is intuitive and tells a story right off.) This is also the case with the "guage study" output, used for mulivary analysis in a Six Sigma project. It is not intuitive and you need to do a lot of tweaking to make the graph tell you the story right off. I have given this feedback to JMP, and it is possible that it will be addressed in future versions.
- I've never heard of JMP allowing floating licenses in a company. This will ALWAYS be a huge sticking point for small to middle size companies, that don't have teams people dedicated to analytics all day. If every person that would do problem solving needs his/her own seat, the cost can be prohibitive. (It gets cheaper by the seat as you add licenses, but for a small company that might get no more than 5 users, it is still a hard sell.)
Continuous improvement Manager
MorindaOil & Energy, 501-1000 employees
Pricing Details
Databricks Lakehouse Platform
General
Free Trial
—Free/Freemium Version
—Premium Consulting/Integration Services
—Entry-level set up fee?
No
Starting Price
$0.07 Per DBU
Databricks Lakehouse Platform Editions & Modules
Edition
Standard | $0.071 |
---|---|
Premium | $0.101 |
Enterprise | $0.131 |
- Per DBU
Additional Pricing Details
—JMP Statistical Discovery Software from SAS
General
Free Trial
Yes
Free/Freemium Version
—Premium Consulting/Integration Services
—Entry-level set up fee?
No
Starting Price
$125 per month
JMP Statistical Discovery Software from SAS Editions & Modules
Edition
Personal License | $125.001 |
---|---|
Corporate License | $1,510.002 |
- per month
- Per Month Per Unit
Additional Pricing Details
—Likelihood to Renew
Databricks Lakehouse Platform
No score
No answers yet
No answers on this topic
JMP Statistical Discovery Software from SAS
JMP Statistical Discovery Software from SAS 10.0
Based on 16 answers
JMP has been good at releasing updates and adding new features and their support is good. Analytics is quick and you don't need scripting/programming experience. It has been used organization wide, and works well in that respect. Open source means that there are concerns regarding timely support. Cheap licensing and easy to maintain.

Verified User
Engineer in Engineering
Semiconductors Company, 10,001+ employeesUsability
Databricks Lakehouse Platform
Databricks Lakehouse Platform 9.0
Based on 3 answers
Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.
in terms of graph generation and interaction it could improve their UI and UX
in terms of graph generation and interaction it could improve their UI and UX

Verified User
Manager in Product Management
Financial Services Company, 201-500 employeesJMP Statistical Discovery Software from SAS
JMP Statistical Discovery Software from SAS 10.0
Based on 5 answers
The overall usability of JMP is extremely good. What I really love about it is its ability to be useable for novices who have no coding experience, which is not the case with most other, similar, programs. It can output a fast and easy analysis without too much prior coding or statistical knowledge.
Graduate Research Assistant
Western Kentucky UniversityHigher Education, 1001-5000 employees
Support Rating
Databricks Lakehouse Platform
Databricks Lakehouse Platform 7.5
Based on 2 answers
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
Director Data Science
CNTransportation/Trucking/Railroad, 10,001+ employees
JMP Statistical Discovery Software from SAS
JMP Statistical Discovery Software from SAS 9.2
Based on 14 answers
Support is great and give ease of contact, rapid response, and willingness to 'stick to the task' until resolution or acknowledgement that the problem would have to be resolved in a future build. Basically, one gets the very real sense that another human being is sensitive to your problems - great or small.

Verified User
Professional in Research & Development
Medical Practice Company, 10,001+ employeesOnline Training
Databricks Lakehouse Platform
No score
No answers yet
No answers on this topic
JMP Statistical Discovery Software from SAS
JMP Statistical Discovery Software from SAS 7.9
Based on 3 answers
I have not used your online training. I use JMP manuals and SAS direct help.
Chief Science Officer
Solan PVSemiconductors, 51-200 employees
Alternatives Considered
Databricks Lakehouse Platform
Databricks has a much better edge than Synapse in hundred different ways. Databricks has Photon engine, faster available release in cloud and databricks does not run on Open source spark version so better optimization, better performance and better agility and all kind of performance boost can be achieved in Databricks rather Open source synapse spark

Verified User
Director in Information Technology
Hospitality Company, 10,001+ employeesJMP Statistical Discovery Software from SAS
It is great because it has UI menus but it costs money whereas the other programs are free. That makes it ideal for beginners but I think that RStudio and Python are going to make someone a lot more marketable for future opportunities since most companies won't pay for the software when there is a great free option.
Research Analyst
Imagine LearningHigher Education, 1001-5000 employees
Contract Terms and Pricing Model
Databricks Lakehouse Platform
Databricks Lakehouse Platform 8.0
Based on 1 answer
The problem with this tool and all other ones that are at the top of the industry, it's so expensive that soon as another one will be on the market and deliver the same or different value, it will be catastrophic for them. So you get the fact that they are cashing every dime right now like SAS or Hadoop once did. Now, look at them
Director Data Science
CNTransportation/Trucking/Railroad, 10,001+ employees
JMP Statistical Discovery Software from SAS
No score
No answers yet
No answers on this topic
Professional Services
Databricks Lakehouse Platform
Databricks Lakehouse Platform 10.0
Based on 1 answer
Again, another level of professional services, this is not their biggest strength but this is the cherry on top. I couldn't think about any other professional services like this one. Now I'm talking about meaningful services that really help out our project and delivery.
Director Data Science
CNTransportation/Trucking/Railroad, 10,001+ employees
JMP Statistical Discovery Software from SAS
No score
No answers yet
No answers on this topic
Return on Investment
Databricks Lakehouse Platform
- Machine learning is a very new concept and not many universities offer to teach it. My school and a few others have been utilizing Databricks as one of the tools to teach and learn machine learning. By doing this, my university is creating a strong future workforce for the job market.
Freelance Translator
ZOO Digital Group plcEntertainment, 501-1000 employees
JMP Statistical Discovery Software from SAS
- ROI: Even if the cost can be high, the insights you get out of the tool would definitely be much more valuable than the actual cost of the software. In my case, most of the results of your analysis were shown to the client, who was blown away, making the money spent well worth for us.
- Potential negative: If you are not sure your team will use it, there's a chance you will just waste money. Sometimes the IT department (usually) tries to deploy a better tool for the entire organization but they keep using the old tool they are used too (most likely MS Excel).
Marketing Analytics Consultant
Whirlpool CorporationConsumer Electronics, 51-200 employees