Sigma Computing headquartered in San Francisco provides a suite of data services such as code free data modeling, data search and explorating, and related BI and data visualization services.
N/A
Tableau Desktop
Score 8.5 out of 10
N/A
Tableau Desktop is a data visualization product from Tableau. It connects to a variety of data sources for combining disparate data sources without coding. It provides tools for discovering patterns and insights, data calculations, forecasts, and statistical summaries and visual storytelling.
$1,380
per year (purchased via a Creator license)
Mathematica
Score 7.0 out of 10
N/A
Wolfram's flagship product Mathematica is a modern technical computing application featuring a flexible symbolic coding language and a wide array of graphing and data visualization capabilities.
$1,520
per year
Pricing
Sigma Computing
Tableau Desktop
Wolfram Mathematica
Editions & Modules
No answers on this topic
Tableau Creator License
$115
per month (billed annually) per user
Standard Cloud
$1,520
per year
Standard Desktop
$3,040
one-time fee
Standard Desktop & Cloud
$3,344
one-time fee
Mathematica Enterprise Edition
$8,150.00
one-time fee
Offerings
Pricing Offerings
Sigma
Tableau Desktop
Mathematica
Free Trial
Yes
No
No
Free/Freemium Version
No
Yes
No
Premium Consulting/Integration Services
No
Yes
No
Entry-level Setup Fee
Optional
No setup fee
No setup fee
Additional Details
Contact us for pricing.
All pricing plans are billed annually. A Creator license includes Tableau Desktop, Tableau Prep Builder, and Tableau Pulse. Discounts sometimes available for volume.
Discounts available for students and educational institutions. The Network Edition reduce per-user license costs through shared deployment across any number of machines on a local-area network.
Sigma beats them all in terms of ease of use and interface. Tableau is more customizable than Sigma, particularly with custom graphics. Sigma is far more feature-rich than Metabase as a basic reporting tool. Sigma makes PowerBI look like a 1980s desktop tool. Dataiku is more …
With Looker, to be effective, a substantial amount of coding & modeling needs to happen in LookML. Being another language to learn, users have to context switch again from at a minimum either SQL or Python into LookML. The concept of being able to source control, code review, …
Verified User
Contributor
Chose Sigma Computing
I have found that Tableau can be used to create a greater variety of custom and complex visuals, though these visuals are far more difficult to create in a quick turnaround. While Sigma may be more limited in terms of what types of visuals can be create or how customized they …
Sigma Computing had better functionality and is beginner friendly. While Tableau is a more well known product, Sigma Computing has a better user interface that is easier to comprehend for those without a non-technical background. This makes it easier to showcase dashboards to …
Sigma has a better view of tables and it is much easier to create new data sets/aggregations. Tableau is better in some visual aspects because there is more customization available, albeit more confusing than Sigma to do. Sigma is very intuitive and did not take long to learn …
I'd rate Sigma to be extremely similar to Sisense except it looks not as nice. I would say that as a tool, Sigma is more user-friendly than Tableau, Power BI, Trevor, and Metabase.
I do feel that Looker is far more powerful and looks great, but I also recognize that Looker does …
Sigma Computing exclusively uses Snowflake as its data source, which enhances data security by not caching or extracting data locally. Tableau, on the other hand, allows a broader range of internal databases and files like SQL Server, Postgres, etc., and supports extracted …
I am not an expert in any of these, though from my brief exposure to Looker it felt like a steeper learning curve, more appropriate to companies with dedicated and skilled BI engineers, whereas Sigma (and Tableau, and Looker Studio) offer a quicker and more intuitive interface …
maintianed is very user friendly. Its various ways of embedding helped us in various aspects. The usage of control ids of the filters as parameters helped us in optimizing very longSQL queries. The live Support team every weekday is a very great intiative that helped in quick …
Sigma is definitely more user-friendly and has powerful built-in functions and capabilities for analyzing and visualizing data in a low-code fashion. It allows our non-technical users to jump in and use data to answer the questions they are asking without having to wait for …
Sigma computing has better pricing than the competitors. We're always looking for what is good for the price but also gives us all we need to complete our reporting. It also brings about a lot of updates that are nice to see. The embedding helps other BI tools sometimes.
Sigma is the easiest to use from a workbook developer perspective, and from a non-technical end user perspective. Everything from administration, semantic layer setup, to creating dashboards is easier in Sigma than these other tools. Developing content in Sigma is enjoyable, …
Sigma has the capabilities of the other BI tools. I think it's pretty user friendly and easy to learn. Many of our stakeholders are used to using Excel so it's nice that it is a smooth onboarding process for them. We haven't looked into much of the visualization capabilities so …
Less visually appealing. Feels like fewer pixels. Harder to make graphs and visuals. Really good integration with Snowflake and intuitive usage for custom equations and filters.
sorta in the middle. One thing that differs than Domo or power bi, is that those softwares bring in the data into the platform, instead of how sigma runs a query against our data warehouse each time a user interacts with the dashboard (there is some small caching, so not always)
Sigma is by far the best. It is easiest to learn and easiest to use on a day to day basis. I never have to wait for dashboards to load and it's very easy to understand the variables that are going into my visualizations. Best of all I can manipulate the data within Sigma …
flexibility, works really well with Snowflake, export capability, level of support, the fact that Sigma Computing is a start up and improving so quickly. Web based software
Sigma is user-friendly and target non technical users as well. Sigma focuses on native cloud integration. Sigma Computing also emphasizes collaboration, enabling multiple users to work together on the same data sets and share insights.
Verified User
Vice-President
Chose Sigma Computing
Much easier to navigate and create the visualizations I need to conduct business.
ease of implementation , easy to train the resources to get used to the tool as it has very user friendly user interface, the 14 days trial where sales and sigma technical team helped us understand the advantages and the helpline chat which is always helpful . licensing when it …
I think IBM Watson Analytics is good alternative to Wolfram Mathematica. A few advantages of Mathematica over IBM Analytics is that Mathematica comes with a lot of inbuilt things like neural networks, predictive analysis geometry. And IBM analytics does not show the step by …
We were able to set up client-facing embedded reports with ease and security. The interface is not difficult to learn, although we may not be aware of or lack the necessary expertise to utilize more advanced features that would likely benefit us.
The best scenario is definitely to collect data from several sources and create dedicated dashboards for specific recipients. However, I miss the possibility of explaining these reports in more detail. Sometimes, we order a report, and after half a year, we don't remember the meaning of some data (I know it's our fault as an organization, but the tool could force better practices).
We are the judgement that Wolfram Mathematica is despite many critics based on the paradigms selected a mark in the fields of the markets for computations of all kind. Wolfram Mathematica is even a choice in fields where other bolide systems reign most of the market. Wolfram Mathematica offers rich flexibility and internally standardizes the right methodologies for his user community. Wolfram Mathematica is not cheap and in need of a hard an long learner journey. That makes it weak in comparison with of-the-shelf-solution packages or even other programming languages. But for systematization of methods Wolfram Mathematica is far in front of almost all the other. Scientist and interested people are able to develop themself further and Wolfram Matheamatica users are a human variant for themself. The reach out for modern mathematics based science is deep and a unique unified framework makes the whole field of mathematics accessable comparable to the brain of Albert Einstein. The paradigms incorporated are the most efficients and consist in assembly on the market. The mathematics is covering and fullfills not just education requirements but the demands and needs of experts.
Mathematica is incompatible with other systems for mCAx and therefore the borders between the systems are hard to overcome. Wolfram Mathematica should be consider one of the more open systems because other code can be imported and run but on the export side it is rathe incompatible by design purposes. A better standard for all that might solve the crisis but there is none in sight. Selection of knowledge of what works will be in the future even more focussed and general system might be one the lossy side. Knowledge of esthetics of what will be in the highest demand in necessary and Wolfram is not a leader in this field of science. Mathematics leves from gathering problems from application fields and less from the glory of itself and the formalization of this.
An excellent tool for data visualization, it presents information in an appealing visual format—an exceptional platform for storing and analyzing data in any size organization.
Through interactive parameters, it enables real-time interaction with the user and is easy to learn and get support from the community.
It allows straightforward integration of analytic analysis of algebraic expressions and their numerical implemented.
Supports varying programmatic paradigms, so one can choose what best fits the problem or task: pure functions, procedural programming, list processing, and even (with a bit of setup) object-oriented programming.
The extensive and rich tools for graphical rendering make it very easy to not just get 2D and 3D renderings of final output, but also to do quick-and-dirty 2D and 3D rendering of intermediate results and/or debugging results.
Sigma Computing does not allow custom ordering of pivot fields in pivot tables easily
Sigma Computing lacks functionality for creating tables or sections that dynamically adjust to the browser window's height while maintaining a fixed height textbox at the bottom
Sigma Computing does not provide straightforward options for formatting totals in tables, such as renaming 'Total' to 'Average', 'Team Total', etc
Sigma Computing does not support searching by individual tab names within a workbook
Sigma has helped us a lot and has become an integral part of our daily workflow. It would be difficult to switch to another platform and have to rebuild the numerous metrics and performance reports that we have already established
Our use of Tableau Desktop is still fairly low, and will continue over time. The only real concern is around cost of the licenses, and I have mentioned this to Tableau and fully expect the development of more sensible models for our industry. This will remove any impediment to expansion of our use.
It has a clean and modern interface. However, it is not completely intuitive. I think it would be better and easier to navigate with more Windows style drop down menus and/or tabls. There is a significant learning curve, but that may be due in part to the technical nature of this type of software tool.
Tableau Desktop has proven to be a lifesaver in many situations. Once we've completed the initial setup, it's simple to use. It has all of the features we need to quickly and efficiently synthesize our data. Tableau Desktop has advanced capabilities to improve our company's data structure and enable self-service for our employees.
When used as a stand-alone tool, Tableau Desktop has unlimited uptime, which is always nice. When used in conjunction with Tableau Server, this tool has as much uptime as your server admins are willing to give it. All in all, I've never had an issue with Tableau's availability.
Tableau Desktop's performance is solid. You can really dig into a large dataset in the form of a spreadsheet, and it exhibits similarly good performance when accessing a moderately sized Oracle database. I noticed that with Tableau Desktop 9.3, the performance using a spreadsheet started to slow around 75K rows by about 60 columns. This was easily remedied by creating an extract and pushing it to Tableau Server, where performance went to lightning fast
They are very friendly and informative. They are quick in resolving our queries and help us understand very minute things as well. They are quick in creating feature tickets based on our custom requirements, and they would also create a bug ticket if there is any discrepancy and get that checked on time.
Tableau support has been extremely responsive and willing to help with all of our requests. They have assisted with creating advanced analysis and many different types of custom icons, data formatting, formulas, and actions embedded into graphs. Tableau offers a weekly presentation of features and assists with internal company projects.
Wolfram Mathematica is a nice software package. It has very nice features and easy to install and use in your machine. Besides this, there is a nice support from Wolfram. They come to the university frequently to give seminars in Mathematica. I think this is the best thing they are doing. That is very helpful for graduate and undergraduate students who are using Mathematica in their research.
It is admittedly hard to train a group of people with disparate levels of ability coming in, but the software is so easy to use that this is not a huge problem; anyone who can follow simple instructions can catch up pretty quickly.
I think the training was good overall, but it was maybe stating the obvious things that a tech savvy young engineer would be able to pick up themselves too. However, the example work books were good and Tableau web community has helped me with many problems
Again, training is the key and the company provides a lot of example videos that will help users discover use cases that will greatly assist their creation of original visualizations. As with any new software tool, productivity will decline for a period. In the case of Tableau, the decline period is short and the later gains are well worth it.
With Looker, to be effective, a substantial amount of coding & modeling needs to happen in LookML. Being another language to learn, users have to context switch again from at a minimum either SQL or Python into LookML. The concept of being able to source control, code review, and deploy your models is a plus though.
Tableau is the gold standard for data visualization, no question. Power users will be able to create dazzling content that Sigma won't necessarily be able to easily match. However, since development usually happens via an extract, helping other users troubleshoot is an arduous process. Trying to re-do or un-do all the transformations and calculations that cause a certain number is very difficult.
With Sigma, all the queries happen directly against Snowflake and you can see the query logs. The data modeling happens right in a tabular, spreadsheet-like manner, so within only a few minutes, substantial transformations can happen, with visualizations just a few more clicks away.
I have used Power BI as well, the pricing is better, and also training costs or certifications are not that high. Since there is python integration in Power BI where I can use data cleaning and visualizing libraries and also some machine learning models. I can import my python scripts and create a visualization on processed data.
We have evaluated and are using in some cases the Python language in concert with the Jupyter notebook interface. For UI, we using libraries like React to create visually stunning visualizations of such models. Mathematica compares favorably to this alternative in terms of speed of development. Mathematica compares unfavorably to this alternative in terms of license costs.
Tableau Desktop's scaleability is really limited to the scale of your back-end data systems. If you want to pull down an extract and work quickly in-memory, in my application it scaled to a few tens of millions of rows using the in-memory engine. But it's really only limited by your back-end data store if you have or are willing to invest in an optimized SQL store or purpose-built query engine like Veritca or Netezza or something similar.
Monitoring health of cloud platform has allowed the company to anticipate issues before they affect customers – Sigma prompted us building a canary monitoring process that provides customer container health.
Customer success has used an activity report to discover customers running runaway processes that they were unaware of, creating an alert to contact the customer and prevent an embarrassing situation.
Customer success uses the activity report to prompt conversations regarding increases or declines in behavior that led to increasing contract limits or addressing churn concerns.
Tableau was acquired years ago, and has provided good value with the content created.
Ongoing maintenance costs for the platform, both to maintain desktop and server licensing has made the continuing value questionable when compared to other offerings in the marketplace.
Users have largely been satisfied with the content, but not with the overall performance. This is due to a combination of factors including the performance of the Tableau engines as well as development deficiencies.
