Austin based Enthought offers their flagship scientific Python distribution, Canopy. The Canopy Geoscience (or Canopy Geo) variant of the product is a data analysis, exploration and visualization package optimized for geologists & geophysicists, and researchers in petroleum science.
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GitLab
Score8.7 out of 10
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GitLab DevSecOps platform enables software innovation by aiming to empower development, security, and operations teams to build better software, faster. With GitLab, teams can create, deliver, and manage code quickly and continuously instead of managing disparate tools and scripts. GitLab helps teams across the complete DevSecOps lifecycle, from developing, securing, and deploying software. Differentiators, as described by Gitlab:
Simplicity: With GitLab, DevSecOps can…
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
GitLab is good if you work a lot with code and do complex repository actions. It gives you a very good overview of what were the states of your branches and the files in them at different stages in time. It's also way easier and more efficient to write pipelines for CI\CD. It's easier to read and it's easier to write them. It takes fewer clicks to achieve the same things with GitLab than it does for competitor products.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
I really feel the platform has matured quite faster than others, and it is always at the top of its game compared to the different vendors like GitHub, Azure pipelines, CircleCI, Travis, Jenkins. Since it provides, agents, CI/CD, repository hosting, Secrets management, user management, and Single Sign on; among other features
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
I find it easy to use, I haven't had to do the integration work, so that's why it is a 9/10, cause I can't speak to how easy that part was or the initial set up, but day to day use is great!
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
I've never had experienced outages from GItlab itself, but regarding the code I have deployed to Gitlab, the history helps a lot to trace the cause of the issue or performing a rollback to go back to a working version
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
GItlab reponsiveness is amazing, has never left me IDLE. I've never had issues even with complex projects. I have not experienced any issues when integrating it with agents for example or SSO
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
At this point, I do not have much experience with Gitlab support as I have never had to engage them. They have documentation that is helpful, not quite as extensive as other documentation, but helpful nonetheless. They also seem to be relatively responsive on social media platforms (twitter) and really thrived when GitHub was acquired by Microsoft
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
Gitlab seems more cutting-edge than GitHub; however, its AI tools are not yet as mature as those of CoPilot. It feels like the next-generation product, so as we selected a tool for our startup, we decided to invest in the disruptor in the space. While there are fewer out-of-the-box templates for Gitlab, we have never discovered a lack of feature parity.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
