Anaconda vs. Astrato Analytics

I have asked all my juniors to work with Anaconda and Pycharm only, as this is the best combination for now. Coming to use cases: 1. When you have multiple applications using multiple Python variants, it is a really good tool instead of Venv (I never like it). 2. If you have to work on multiple tools and you are someone who needs to work on data analytics, development, and machine learning, this is good. 3. If you have to work with both R and Python, then also this is a good tool, and it provides support for both.

Incentivized

Astrato Analytics provides a feature-rich application that it easy to use for the non-technical user yet robust enough to create a sophisticated SaaS application. Astrato Analytics provides native integration to all the major large cloud data platforms and the implementation is very straight forward. The value provided through their feature-rich platform and competitive pricing is unmatched.

• Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
• Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
• Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.

Incentivized

• Writeback to the database is simple and straightforward and allows us to build apps in the tool with a level of interation that is not possible with other BI tools
• Version control that is built into the workbooks allows us to quickly deploy changes without the fear of losing the older version and maintianing both a historcal updates and the ability to rollback
• Easy connectivity to a wide range of data platforms including Snowflake, Databricks, and Google Workspace within the same tenant allows us to leverage data from each of these platforms for specific use cases

• It can have a cloud interface to store the work.
• Compatible for large size files.
• I used R Studio for building Machine Learning models, Many times when I tried to run the entire code together the software would crash. It would lead to loss of data and changes I made.

Incentivized

• The product is still young, as a consequence, some features aren't as mature as competitors. Astrato has worked to catch up quickly, by rolling out new features regularly
• Better ability to control icons and themes from a central point, instead of having to add/change them on each report

Incentivized

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.

Incentivized

I am giving this rating because I have been using this tool since 2017, and I was in college at that time. Initially, I hesitated to use it as I was not very aware of the workings of Python and how difficult it is to manage its dependency from project to project. Anaconda really helped me with that. The first machine-learning model that I deployed on the Live server was with Anaconda only. It was so managed that I only installed libraries from the requirement.txt file, and it started working. There was no need to manually install cuda or tensor flow as it was a very difficult job at that time. Graphical data modeling also provides tools for it, and they can be easily saved to the system and used anywhere.

Incentivized

It is a very intuitive system, a pleasure to use, practical and simple, for medium to advanced activities. BI supervisors are delighted with much of the functionality, but highlight some weaknesses that they still have, such as the loading speed in HEIC-type files.

Without a doubt, it is a tool that has become essential in our business routine.

Incentivized

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.

Incentivized

Very friendly, with online help, blog and community. The template documentation could be more complete, however.

Incentivized

I have experience using RStudio oustide of Anaconda. RStudio can be installed via anaconda, but I like to use RStudio separate from Anaconda when I am worin in R. I tend to use Anaconda for python and RStudio for working in R. Although installing libraries and packages can sometimes be tricky with both RStudio and Anaconda, I like installing R packages via RStudio. However, for anything python-related, Anaconda is my go to!

Incentivized

To scale PowerBi, we would have needed a staff of technical folks and a substantial budget, and we still probably could not have created the robust SaaS application we did with Astrato Analytics. Astrato Analytics allowed for a staff of two to create and rollout a SaaS Application in under 60 days and provided hands on support to us as needed. There really is no comparison.

• It has helped our organization to work collectively faster by using Anaconda's collaborative capabilities and adding other collaboration tools over.
• By having an easy access and immediate use of libraries, developing times has decreased more than 20 %
• There's an enormous data scientist shortage. Since Anaconda is very easy to use, we have to be able to convert several professionals into the data scientist. This is especially true for an economist, and this my case. I convert myself to Data Scientist thanks to my econometrics knowledge applied with Anaconda.

Incentivized

• The ability to unify multiple platforms in Astrato provides cost savings.