AWS Cloud9 vs. Enthought Canopy

When I am working with a large team of developers. Also, when a security policy, you are not allowed to install any app on your laptop. Cloud 9 is well integrated with Cloud commit. So we don't have to spend time in configurations.

Incentivized

Enthought Canopy is best suites for scripting data analytical concepts. It has a wide range of data analytical libraries and also is good for data visualization. I would not recommend using Enthought Canopy only as an IDE, there may be better options available. If you're looking for a good data simulation & visualization package, Canopy it is.

Incentivized

• It provides an extremely intuitive and easy-to-use interface, so adapting to it does not take much time.
• It contains the most common programming languages.
• It allows the export of files, either directly to the repository or to the team where you want it.
• It is housed in the cloud so you can work with it from anywhere.
• Allows you to share code or work on them with others in real-time.
• Does not require installation.

• Providing scientific libraries, both open source and Enthought's own libraries which are excellent.
• Training. They provide several courses in python for general use and for data analysis.
• Debugging tools. Several IDEs provides tools for debugging, but I think they are insufficient or too general. Canopy has a special debugging tool, specially design for python.

• Confusing documentation - AWS's documentation remains quite confusing, and the layout of other services/settings that you have to use with Cloud9 can be a bit of a handful.
• Sometimes slow - As the size of a project increases, the editor gets increasingly slower, and starts slowing down the browser overall.
• Long setup process - The setup for Cloud9 can be hard and tough, especially since the documentation is quite hard to understand.

Incentivized

• Canopy does not support Python 3
• There were times the Python shell crashed, and I would have to restart it
• Some Python libraries are slow.

Incentivized

The interface for Cloud9 needs some improvement. It is simply not as powerful and intelligent as a local text editor would be and thus it lacks the capabilities of fast filling when coding. Otherwise, I think it has a fair interface that they have tried mimicking an IDE.

Incentivized

Customer support is managed by the Cloud support team. I won't be able to comment on details, but it's fast.

[AWS] Cloud9 offers specific features not available in the competition: Code collaboration using the chat features is the highlight which sets it apart. [The] code completion feature makes [it] very similar to the offline IDE like eclipse. It's much easier to use compared to Codeanywhere. It provides terminal access to EC2 instances and hence other amazon services.

Incentivized

Before Canopy with its python we were working with Matlab. We decided for Canopy against Matlab for two reasons: First, we believe that python together with NumPy or SciPy can achieve the objectives with less code and therefore less training, and second the prizes are much lower than matlab which is most robust, expensive and less intuitive. It's clear we are making the comparison with python and it has nothing to with canopy. But with Canopy you feel you have all those tools close together without the problem of configuration, besides a lot of personalized libraries that complements a typical python environment.

• We save time since we do not need to configure files or our equipment.
• We have the most appropriate tools for software development in one place.
• We can work from anywhere so if that day we could not get to our office or something we can do from home.
• We can code with a single browser.

Incentivized

• Its easier to define KPI's with Enthought
• It is good for reiteration and building on top of existing scripts
• Its dedicated Python console makes it easier to execute projects.

Incentivized