DbVisualizer vs. Enthought Canopy

Whenever you have to access data sources from more than one database engine provider, you should consider using DbVisualizer for the task. Switching between multiples IDE consumes time and resources (memory), and you also need to learn menus, shortcuts, etc., for each IDE, while using DbVisualizer allows you to work consistently no matter which database engine you are connecting. You can use it to simply navigate through data structures, write SQL sentences or even create new tables and views, develop stored procedures, etc.

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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.

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• Timed queries that add their result to the result grid for every execution are helpful. Creation of graphs also extremely helpful.
• Query history and auto-recover feature makes sure we don't lose our queries.
• Actions like exporting grids to Excel or other tools is a very easy task.

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• 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.

• Small, but noticeable: it would be nice to be able to highlight just one column of a data output for copy/paste by clicking on the column header (a feature some of us are accustomed to from SQL Server Management Studio)
• Is there an ability to bulk import an excel file to a table in a connected SQL Server in DbVis? If so, I haven't figured that out yet. I still largely use Microsoft SSMS to import data to our team's personal SQL Server, but can easily query that server via DbVis)
• (I really don't have any major complaints - our company has tried to steer users toward DBeaver for YEARS, which I cannot stand. DbVis knocks the beaver out of the water)

• 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.

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We never had problems with it. It is able to connect to all data sources (Oracle, Informix, DB2, SQL Server) that we currently use.

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I use this tool for several hours each day, spanning many years in various projects. It's wide support for various database types while keeping consistency within the UI for each is important when working with various databases day in and day out.

Never used

Incentivized

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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

DbVisualizer provides a good value when compared against other 3rd party products with similar features as well as products with similar functionality made by the database vendors. The user interface is good and user friendly / intuitive and easy to learn. It doesn't take too long for an experienced DBA / database developer to come up to speed.

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.

• Positive: Real time saver when working across multiple databases. At least 30-40 mins a day are saved
• Positive: Helpful for the 'quick DBA' tasks without switching to the native tools: approx 1-2 hours saved weekly
• Positive: Easy switch between environments / schemas / etc without hassle: saved at least an hour daily

• 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

Independently Invited by TrustRadius

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