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Anaconda OverviewUNIXBusinessApplication

Anaconda is #11 ranked solution in top Data Science Platforms. PeerSpot users give Anaconda an average rating of 10 out of 10. Anaconda is most commonly compared to Databricks: Anaconda vs Databricks. The top industry researching this solution are professionals from a computer software company, accounting for 23% of all views.
What is Anaconda?

Anaconda makes it easy for you to install and maintain Python environments. Our development team tests to ensure compatibility of Python packages in Anaconda. We support and provide open source assurance for packages in Anaconda to mitigate your risk in using open source and meet your regulatory compliance requirements.

Python is the fastest growing language for data science. Anaconda includes 720+ Python open source packages and now includes essential R packages. This powerful combination allows you to do everything you want from BI to advanced modeling on complex Big Data

Anaconda Buyer's Guide

Download the Anaconda Buyer's Guide including reviews and more. Updated: January 2022

Anaconda Customers

LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone

Anaconda Video

Anaconda Pricing Advice

What users are saying about Anaconda pricing:
"The product is open-source and free to use."

Anaconda Reviews

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Data Engineer at a government with self employed
Real User
Responsive, sleek and had a beautiful interface that is pleasant to use
Pros and Cons
  • "The product is responsive, sleek and has a beautiful interface that is pleasant to use. It helps users to easily share code."
  • "One thing that hurts the product is that the company is not doing more to advertise it as a solution and make it more well known."

What is our primary use case?

l began using it because it was open source and it was free and I knew other people who were using it. I just installed it and I got on with my testing. It was very useful for me because I could save my coding and present it to my assessor.  

What is most valuable?

There are several things that I think are valuable in the product. My first impressions were the product was fairly responsive, sleek and had a beautiful interface that was pleasant to use. It helped me to be able to easily share code between me and my colleagues.  

I had R installed at that time as well. It worked with R as well as Python. R is good for statistics and visualization. I've used R with Tableau as well and for my situation at the time, Anaconda was a bit superior in respect to this integration.  

What needs improvement?

The product can be improved in a few ways. It would be possible to simplify the installation although it was not a problem in my case because of my experience. One thing that hurts the product is that the company is not doing more to advertise it as a solution and make it more well known that I have seen. I do not really feel it is as known as it should be in our market.  

The features I would like to see in the next release are more packages. That is, it would be nice to have more libraries added by default.  

For how long have I used the solution?

I have used it in one of my assignments from the university for several months.  

What do I think about the stability of the solution?

I have never experienced bugs or crashes or loss of work, so it is stable.  

What do I think about the scalability of the solution?

I have not seen any issues with scalability.  

How are customer service and technical support?

I have never yet had to contact technical support for Anaconda or Continuum Analytics.  

Which solution did I use previously and why did I switch?

I have used quite a few products in this category and sometimes I choose one or another depending on what I think seems best for me at the time. I used Notebooks by Jupyter. I've used the R Markdown, which is on the cloud, by RStudio. I've used Tableau software. I used Power BI, which is Microsoft. I used QlikView by Qlik. Those are the main ones that I use more often.  

The main differences are the designs are different and sometimes the features or focus. Each of these products is developing quite well from one release to another. Power BI especially is picking up. One or two years ago it was not very developed but now it seems to be more mature and competitive. I can see why people who are working within a Microsoft environment tend to use Power BI because it is practically free and it is part of Office 365. 

Tableau is sleeker than QlikView and it looks better. Both have different options, but in general, I can not really pinpoint why in some situations I prefer Tableau over QlikView. On the other hand, it was easy to point to why I was using Anaconda.  

How was the initial setup?

The initial setup really only takes minutes, but it is not an easy application to install. I have a technical background so that is not a problem for me. I have also worked in IT support. But I do see why some people might encounter some issues during the installation. Some issues might occur because it is a large installation file. I can not really remember if I needed some dependencies like .NET installed or something else. I probably can't remember that because I probably already had the necessary dependencies installed already. I do install quite a few products on my machine and there is a good chance that some other product already required what was needed so it was already there.  

What's my experience with pricing, setup cost, and licensing?

The product is open-source and free to users.  

What other advice do I have?

My only advice to people considering this type of solution is just to use Anaconda. It is a good product. Other products are good as well, but this is one you should try in this category.  

On a scale of one to ten where one is the worst and ten is the best, I would rate Anaconda in comparison to other products as between nine and ten. It is a very good solution. I will rate it a nine as there is always room for improvement.  

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Engineer at a university with 51-200 employees
Reseller
Top 5Leaderboard
Good virtualization, great documentation, and has an active supportive community
Pros and Cons
  • "The documentation is excellent and the solution has a very large and active community that supports it."
  • "When you install Anaconda for the first time, it's really difficult to update it."

What is most valuable?

The best part of the solution is the virtualization. You can use Python within the virtual environment. It gives us more than the local environment. In there you can do lots of useful things. 

The documentation is excellent and the solution has a very large and active community that supports it.

What needs improvement?

The solution's support is important and needs to be better. I don't have the last update due to the fact that when I tried to update it I had an error and ran into issues. It's not just me; lots of people in the community don't have the last update. If support was better they may be able to address issues like this faster.

The stability could be improved. Stability is very important because if you develop some product or some program, you want a very, very stable software that you can use for more than two or three years. 

When you install Anaconda for the first time, it's really difficult to update it.

I can't think of any features that are lacking. Overall, it works quite well for me.

For how long have I used the solution?

I have been using the solution for about two years now.

What do I think about the stability of the solution?

The stability is difficult to determine. I've heard of many people having issues. And, right now, a lot of people can't deploy the latest update. The stability could be better, in all honesty.

What do I think about the scalability of the solution?

The solution can scale.

I'm a data science student, so I haven't actually had to scale it myself. I know of others who use it and work with it, and they've never had issues.

How are customer service and technical support?

The documentation is very, very good for this product. Python and Anaconda have very, very big communities, similar to Stack Overflow and GitHub. If you have a problem or you want some answers, or if you have a request for more information on a certain topic, you can easily find exactly what you need.

How was the initial setup?

In the beginning, the initial setup was complex due to the fact that I began with the virtual environment and the virtual environment is very different than the normal environment. With Anaconda it's very different than the normal Python. We use a document to code like JupyterLab. It's not like normal python code. That makes it a bit tricky.

The installation only took a few hours. It wasn't a lengthy process. It's very quick to deploy.

What other advice do I have?

I can't do an update on the solution, so I don't have the latest version. I'm one version behind the latest.

I'm a developer. I work in data science. I work with different data science libraries like Pandas, NumPy, etc., and I use it for analyzing data. Therefore, I'm more of a customer than I am a partner. I don't have a business relationship with the company.

I'd recommend the solution to others.

Overall, I'd rate the solution eight out of ten. It's quite good. It just needs to be more stable and easier to update.

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Learn what your peers think about Anaconda. Get advice and tips from experienced pros sharing their opinions. Updated: January 2022.
564,729 professionals have used our research since 2012.
Analytics Analyst at a tech services company with 10,001+ employees
Real User
Top 20
Interesting, user friendly, and outstanding among the other competitors
Pros and Cons
  • "It's interesting. It's user friendly. That's what makes it outstanding among the others. It has a collection of R, Python, and others. Their platform strategy has a collection of many other visualization tools, apart from Spyder and RStudio, which is really helpful for data science. For any data science professional, Anaconda is really handy. It has almost all the tools for data science."
  • "It crashes once in a while. In case of a reboot or something unexpected, the unseen code part will get diminished, and it relatively takes longer than other applications when a reboot is happening. They can improve in these areas. They can also bring some database software. They have software for analytics and virtualization. However, they don't have any software for the database."

What is our primary use case?

In Anaconda, we get everything: RStudio, Spyder, and Jupyter. R Studio is for R, and Spyder and Jupyter are for Python. Using these, we will be doing data wrangling and data modeling for a developing project.

What is most valuable?

It's interesting. It's user friendly. That's what makes it outstanding among the other competitors.

It has a collection of R, Python, and others. Their platform strategy has a collection of many other visualization tools, apart from Spyder and RStudio, which is really helpful for data science. For any data science professional, Anaconda is really handy. It has almost all the tools for data science.

What needs improvement?

It crashes once in a while. In case of a reboot or something unexpected, the unseen code part will get diminished, and it relatively takes longer than other applications when a reboot is happening. They can improve in these areas. 

They can also bring some database software. They have software for analytics and virtualization. However, they don't have any software for the database.

For how long have I used the solution?

I have been using this solution for the last one year since I joined this company. It was suggested by some of my seniors because it would be better for the database and a one-stop solution that pays for all things.

What do I think about the stability of the solution?

It is stable. 

What do I think about the scalability of the solution?

I didn't get an opportunity to test this feature. I haven't yet come across an area where I can test the scalability of this platform.

A lot of people who work for data science projects will use Anaconda on a daily basis or at least twice or thrice a week. I use Anaconda almost daily, like for at least half an hour daily. On some days, it can also be for five, six hours.

How are customer service and technical support?

There weren't many issues for which I needed support from external people. So far, it's good. 

How was the initial setup?

It's easy to set up. You download the EXT file and follow the instructions. It's as simple as that. It's not a big thing. It took around five minutes.

What other advice do I have?

I would recommend it to anyone willing to work in data science. This will be a starting place that covers data-wrangling aspects, user relation aspects, and everything. It is a one-stop solution for everything. 

Anaconda is the main go-to place for analytics. This solution is very handy for almost all data science people. A lot of people I know nowadays use Anaconda. I don't think any other product can even come near Anaconda for data science.

I would rate Anaconda a nine out of ten. The long reboot time and once in a while crash are the two things that lack in Anaconda. Apart from that, I don't see any issues with Anaconda.

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Ariful Mondal
Consulting Practice Partner - Data, Analytics & Artificial Intelligence at Wipro Ltd
Real User
ExpertModerator
Supported by multiple IDEs, easy to install and manage packages
We use Anaconda for most of our Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Statistical Modeling, and data engineering use cases. These include application development, package management, data extraction, web-scrapping, intelligent automation, and API development. With Anaconda Navigator, we have been able to use multiple IDEs such as JupyterLab, Jupyter Notebook, Spyder, Visual Studio Code, and RStudio in one place. The platform-agnostic package manager, "Conda", makes life easy when it comes to managing and installing packages. Anaconda Navigator and the Conda package manager are fantastic features with workflow visualization. Cons/improvements required: Anaconda should be optimized for RAM consumption An individual version is…

We use Anaconda for most of our Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Statistical Modeling, and data engineering use cases. These include application development, package management, data extraction, web-scrapping, intelligent automation, and API development.

With Anaconda Navigator, we have been able to use multiple IDEs such as JupyterLab, Jupyter Notebook, Spyder, Visual Studio Code, and RStudio in one place. The platform-agnostic package manager, "Conda", makes life easy when it comes to managing and installing packages.

Anaconda Navigator and the Conda package manager are fantastic features with workflow visualization.

Cons/improvements required:

  • Anaconda should be optimized for RAM consumption
  • An individual version is not scalable for large projects, and it should be able to scale like Visual Studio, PyCharm, etc.
  • DevOps compatibility should be improved
  • The UI can be improved to make it more interactive and lightweight
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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