CloudFoundry vs. Tableau Desktop

It's well suited if:

• The organization has large number of applications that needs to be deployed frequently.
• The organization is tied to the DevOps mindset.
• The organization has programs in different languages.
• The applications does not need EJB's support that servers like web logic provide.

It's less suited if:

• The applications needs security configuration within the same CloudFoundry instance.
• The organization, for whatever reason does not want developers to manage the instances.

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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).

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• Support for Orgs and Spaces that allow for managing users and deployables within a large organization.
• Easy deployment, deploying code is as simple as executing single line from CLI, thanks to build-packs.
• Solid and rich CLI, that allows for various operations on the instance.
• Isolated Virtual Machines called Droplets, that provide clean run time environment for the code. This used to be a problem with Weblogic and other application servers, where multiple applications are run on the same cluster and they share resources.
• SSH capability for the droplet (isolated VM's are called droplets), that allows for real time viewing of the App code while the application is running.
• Support for multiple languages, thanks to build-packs.
• Support for horizontal scaling, scaling an instance horizontally is a breeze.
• Support for configuring environment variable using the service bindings.
• Supports memory and disk space limit allocation for individual applications.
• Supports API's as well as workers (processes without endpoints)
• Supports blue-green deployment with minimal down time

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• 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.

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• Does not support stateful containers and that would be a nice to have.
• Supports showing logs, but does not persist the logs anywhere. This makes relying on Cloud Foundry's logs very unreliable. The logs have to be persisted using other third party tools like Elk and Kibana.

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• Pricing should be more user-friendly and usage-driven
• Making edits to the production reports is fairly tough and has a vast scope of additional capabilities
• Tableau Desktop should be able to differentiate itself from the Tableau server else there is no major meaning of two different products being offered

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.

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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

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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.

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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

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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.

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While Docker shines in providing support for volumes and stateful instances, Cloud foundry shines in providing support for deploying stateless services. Heroku shines in integrating with Git and using commits to git as hooks to trigger deployments right from the command line. But it does not provide on-premise solution that Cloud foundry provides.

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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.

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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.

• Positive impact, since it simplifies the deployment time by a huge margin. Without cloud foundry, deploying a code needs coordination with infrastructure teams, while with cloud foundry, its a simple one line command. This reduces the deployment time from at least few hours to few minutes. Faster deployments promote faster dev cycle iterations.
• Code maintenance such as upgrading a Node or Java version is as simple as updating the build-pack. Without cloud foundry, using web logic, the specific version only supports a specific version of Java. So updating the version involves upgrading the version of web logic that needs to involve few teams. So without cloud foundry, it takes at least few days, with cloud foundry, its a matter of few mins.
• Overall, happier Developers and thats harder to quantify.

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• 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.

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