Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and research. Anaconda also provides enterprise-grade security to open-source software through the Premium Repository.
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Dataiku
Score 7.9 out of 10
N/A
Dataiku is a French startup and its product, DSS, is a challenger to market incumbents and features some visual tools to assist in building workflows.
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Dataiku
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$9
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$50
per month per user
Enterprise Tier
60.00+
per month per user
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Yes
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Suitable for Python development where there’s internal supporting for Python; otherwise, other platform offers similar capabilities with lower cost.
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the …
As a Data Analyst, it is my job to analyze large datasets using complex mathematical models. Anaconda provides a one-stop destination with tools like PyCharm, Jupyter, Spyder, and RStudio. One case where it is well suited is for someone who has just started his/her career in this field. The ability to install Anaconda requires zero to little skills and its UI is a lot easier for a beginner to try. On the other hand, for a professional, its ability to handle large data sets could be improved. From my experience, it has happened a lot that the system would crash with big files.
Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.
Although I have generally had positive experiences with Anaconda, I have had trouble installing specific python libraries. I tried to remedy the solution by updating other packages, but in the end, things got really messed up, and I ended up having to uninstall and reinstall a total of about 4 times over the past 2 years.
If you have the free version of Anaconda, there is not much support. Googling questions and error messages are helpful, but there were times when I wished I would have been able to ask technical support to help me troubleshoot issues.
There were a few times when I tried to install tensorflow and tensorboard via Anaconda on a PC, but I could not get them to install properly. Anaconda allows you to create 'environments' , which allow you to install specific versions of python and associated libraries. You can keep your environments separate so they do not conflict with one another. Anyway, I ended up having to create several 'conda envrionments' just so I could use tensforflow/tensorboard and a few other utilities to avoid errors. This was somewhat annoying, because every time I wanted to run a specific model, I'd have to open up the specific conda environment with the appropriate python libraries.
It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.
Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.
The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.
ANACONDA VS Alteryx Analytics: Even though I find Alteryx to be an excellent tool for managing extremely massive data, Anaconda is much better and easy for analytics. Anaconda VS. MicroStrategy Analytics: Compared with Anaconda, MicroStrategy Analytics is very difficult to use and counter-intuitive Anaconda VS. Power BI For Office 365: One of the main advantages of BI for Office 364 is its capacity to data connectivity. However, it's very hard to edit data connections, once BI for Office is deployed in other platforms
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
