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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

Archived Anaconda Reviews (more than two years old)

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R&D Program Manager at a manufacturing company with 10,001+ employees
Real User
Good virtual environment and helpful documentation, but could use better automation features
Pros and Cons
  • "The virtual environment is very good."
  • "The solution would benefit from offering more automation."

What is our primary use case?

We primarily use the solution for the data science class. It's used for people to build their data science models.

What is most valuable?

The solution's most valuable aspects include the repositories, the way we log, the way we install that repository management, and how we can easily integrate the solution with Jupyter Notebook.

The virtual environment is very good.

What needs improvement?

Anaconda has a platform for data science professionals, but if they would have something for an on-prem solution for the data science platform, it would be more valuable to Anaconda

The user interface could be improved. There should be easy provisioning on AWS.

Configuring clusters could be easier.

The solution would benefit from offering more automation.

For how long have I used the solution?

I've been using the solution for eight months.

What do I think about the stability of the solution?

It's too early to comment on the solution's stability; we've only been using it for eight months.

What do I think about the scalability of the solution?

We're still in the early stages of using the solution, so we haven't tried to scale. We're planning to have 50 users on the solution.

How are customer service and technical support?

We haven't reached out to technical support.

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

We did previously use a different solution. We switched to this solution because it offered higher security.

How was the initial setup?

The initial setup was straightforward. Deployment took a few days.

What about the implementation team?

I browsed through the documentation and handled the implementation myself.

What other advice do I have?

We use the on-premises deployment model.

I'd rate the solution seven out of ten.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Sr PHP Developer at a manufacturing company with 10,001+ employees
Real User
Quick to deploy and all of the add-on tools are available in one place, but it needs to have a video available on the tool itself
Pros and Cons
  • "The most valuable feature is the Jupyter notebook that allows us to write the Python code, compile it on the fly, and then look at the results."
  • "Having a small guide or video on the tool would help learn how to use it and what the features are."

What is our primary use case?

I was using this solution for buildings some PoCs, as well as during a hackathon.

What is most valuable?

The most valuable feature is the Jupyter notebook that allows us to write the Python code, compile it on the fly, and then look at the results.

I found the navigators and the other features to be very good for quick development.

There is a system that is similar to a marketplace where we can search for and install all the tools that we need, and they have been collated in one place.

What needs improvement?

Having a small guide or video on the tool would help learn how to use it and what the features are.

For how long have I used the solution?

I have been using Anaconda for about six months.

What do I think about the scalability of the solution?

We have not yet had to scale this solution.

Currently, we have between ten and fifteen users, and they are all technicians.

How are customer service and technical support?

We have not been in contact with technical support.

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

This was our first experience with the Jupyter notebook and I do not know of any competing solutions.

How was the initial setup?

The initial setup of Anaconda is straightforward.

I used the instructions that I received from our consultants and the deployment took between one and two hours.

What other advice do I have?

My advice to anybody who is researching this tool is to download and install it, then start exploring the different tools that are available. I suggest starting with Jupyter because it is easy to figure out, then move to Python tasks.

This is a good tool that is quick to deploy with and pretty easy to use. I have not fully explored it yet, so I can only give it an average rating.

I would rate this solution a five out of ten.

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.
564,322 professionals have used our research since 2012.
Head - Data Science (Senior Program Manager) at a tech services company with 51-200 employees
Real User
A framework with an extensive set of libraries for building predictive models
Pros and Cons
  • "The most valuable feature is the set of libraries that are used to support the functionality that we require."
  • "I think that the framework can be improved to make it easier for people to discover and use things on their own."

What is our primary use case?

We use different data science platforms for customer-specific projects. Whatever is being requested by, or is required by the customer, we learn it. Python is one of the technologies that we have a lot of experience with, and it is part of Anaconda.

Our primary use case is analytics. We use Anaconda to build models that predict the probability of an event, or it can be used for classification purposes. There are various uses for this tool.

One of the things that we do is subrogation and I can explain by using the example of a car accident. When an accident happens, you take your car to your insurance company and give them details about what happened. Also, the advisor at a service center will write down relevant information and supply it to the insurance company as well. At this point, the insurance company reimburses expenses for all of the damages that you have incurred. At the same time, they would like to find out if there is any fault that can be attributed to another person. If so, then they want to know whether it is possible to make any kind of recovery from that person or their insurance company.

With thousands of these claims coming into the insurance companies, it is very difficult for somebody to read all of the information and decide whether there is a potential for recovery or not. This is where our application comes into effect. We read all of the data into our software, which is built with Python using Anaconda, and try to gain an understanding of each and every case. This includes many details, even claim history, and we try to assess what the chances are of recovery or what the chances are of subrogation in each case.

This is just an example from one of our several clients. Each customer has different requirements and we customize a solution based on their needs.

What is most valuable?

The most valuable feature is the set of libraries that are used to support the functionality that we require. We use different libraries for finance and numbers, and we use the scikit-learn library for machine learning. A few of these libraries are very helpful and there is a very long list of them.

What needs improvement?

I think that the framework can be improved to make it easier for people to discover and use things on their own.

They need a better interface because currently, we have to do everything through coding. It would be nice to have a simple description of what each library is used for and how to use it.

I would like to see additional libraries included to support computer vision and natural language processing. The framework gives us the ability to create them, but having more in place would mean that we would need to do less coding.

What do I think about the stability of the solution?

Stability is not something that we really consider for this solution. When we are using Anaconda, we have to develop most of the things from scratch. It's a framework, and it is one of the tools that we use so that we do not have to think about dependencies. When I have Anaconda in my environment, I do not have to think about any prerequisites that may be required.

How are customer service and technical support?

We have not been in contact with technical support to this point.

How was the initial setup?

The initial setup is straightforward and not too difficult.

The length of time required for deployment changes after the first time. If somebody has to build everything then it takes longer. However, once all of the libraries are built, it takes one person perhaps three hours to deploy into production if it is done without interruption.

What about the implementation team?

We have our own team for deploying this solution.

What other advice do I have?

This is a great tool to work with, even if you are starting your career in analytics or another stream like data engineering or data science. This is a tool for everyone because you don't need to think about many things, such as what needs to be installed.

I would rate this solution an eight out of ten.

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.
Maruf-Hossain
Data Scientist Chapter Lead, Workflow & Automation at ANZ Banking Group
Real User
Good notebook features, but multi-language support is needed
Pros and Cons
  • "The notebook feature is an improvement over RStudio."
  • "One feature that I would like to see is being able to use a different language in a different cell, which would allow me to mix R and Python together."

What is our primary use case?

We use Anaconda to develop machine learning models. Use primarily use Scikit-learn and TensorFlow.

What is most valuable?

The notebook feature is an improvement over RStudio.

What needs improvement?

One feature that I would like to see is being able to use a different language in a different cell, which would allow me to mix R and Python together.

For example, using R tidyverse to wrangle data, R ggplot to visualise data and then use Python sci kit-learn to build machine leanring model on that data.

Multi-language support would allow all of the data science languages to be in one place, and we could become a hub for it.

For how long have I used the solution?

I have been using Anaconda for the last four or five years.

What do I think about the stability of the solution?

Anaconda is very stable, which is something that I'm happy about.

What do I think about the scalability of the solution?

The scalability is not great, but it's pretty good for medium-sized projects.

Currently, we have about 50 users on the platform, which is medium-sized. We have more users lined up but we are not able to scale enough to accommodate all of them. We do have plans to increase our usage.

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

We are also trying Cloudera Workbench and I think that in terms of ease of use, it is somewhat better than Anaconda. However, I wouldn't swap our users because using it is not massively different.

How was the initial setup?

In terms of the initial setup, there is a fair bit of configuration involved and it is not very straightforward.

What about the implementation team?

Our engineering team handled the deployment.

We have eight people in our maintenance team.

Which other solutions did I evaluate?

We have used and are using several products, and we are still in the evaluation stage.

What other advice do I have?

This is a product that I encourage people to use.

This is a good solution, but I would like to see multi-language support.

I would rate this solution a seven out of ten.

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.
Senior Software Design Engineer at a financial services firm with 5,001-10,000 employees
Real User
Stable and scalable but needs a better user interface
Pros and Cons
  • "The solution is stable."
  • "The interface could be improved. Other solutions, like Visual Studio, have much better UI."

What is our primary use case?

I use the solution for learning purposes only. I don't use it for any production standard quota, and have not deployed it.

What is most valuable?

When I try to develop an implementer model, I can just do some trial and error and see the output immediately. Then later I can move them onto the Python file. Until that happens, I can see that it's there. That's the most useful feature for me in Anaconda.

We use Jupyter notebooks and it was all available in one package when we installed Python.

What needs improvement?

The interface could be improved. Other solutions, like Visual Studio, have much better UI.

In Visual Studio, we can create projects. I'm not sure it's that kind of feature is possible on Anaconda. In Visual Studio, we can bring a solution and then we can keep multiple projects organized and into it. If there are any libraries, we can have them as well and we can just call them. We can easily link the libraries into the project. This should be possible on Anaconda as well.

For how long have I used the solution?

I've been using the solution for two years.

What do I think about the stability of the solution?

The solution is stable.

What do I think about the scalability of the solution?

The scalability of the solution is good. I'm not sure if we'll be increasing usage in the future.

How was the initial setup?

The initial setup was straightforward. We haven't made a deployment; we just use it for learning purposes.

What other advice do I have?

I'd rate the solution seven out of ten. I haven't been using the solution for too long, and I'm still learning things, so I need to explore more before rating it higher.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Solution Architect/Technical Manager - Business Intelligence at a tech services company with 5,001-10,000 employees
Real User
Includes lots of pre-built libraries and has good community support
Pros and Cons
  • "The most advantageous feature is the logic building."
  • "The ability to schedule scripts for the building and monitoring of jobs would be an advantage for this platform."

What is our primary use case?

I use this solution for some of my assignments. Basically, it is used to take data from our database, analyze it, and make predictions.

What is most valuable?

The most advantageous feature is the logic building.

The Python libraries are all readily available and there is no need to install anything separately.

There are many good things that are pre-built, and including even more of these would be a great benefit to the developer community. It would allow us to try specific models and use cases, then customize them as per a particular activity.

What needs improvement?

I would like to see the inclusion of some statistical modeling functionality.

Having some examples built-in that we can customize based on the use case, rather than having to build the entire model, would really be an advantage.

Additional support for the visualizations would be an improvement.

The ability to schedule scripts for the building and monitoring of jobs would be an advantage for this platform.

For how long have I used the solution?

I have been using Anaconda for about a year.

What do I think about the stability of the solution?

The stability of the platform is quite good.

What do I think about the scalability of the solution?

I have not tried to scale Anaconda, but we will be working on that shortly. At this time we have between ten and twelve users, and we are looking at how to extend that and still be compliant.

All of our users are technicians.

How are customer service and technical support?

I have not contacted the technical support directly because to this point, we have relied on help from friends, colleagues, and the community. Most of the problems, we have been able to sort out ourselves.

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

Prior to Anaconda, we were using SAS for some of our predictive analytics.

The main reason we switched is because of the licensing cost that is incurred for these kinds of specialized software solutions. In addition, functionality is limited to predictive modeling and some specific types of analysis. In Anaconda, there are a lot of other aspects that you can try. 

How was the initial setup?

I found the initial setup to be moderate. It was not too complex nor too easy. We had a couple of people who were working on it and we were able to sort it out with assistance from the community help channels.

It takes between three and four hours to complete the setup entirely.

What about the implementation team?

We implemented this solution ourselves.

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

The licensing costs for Anaconda are reasonable.

What other advice do I have?

Our team is working on expanding the use of Anaconda. They're doing some research with respect to some of the libraries and modules, trying to do different things with existing datasets. I have been doing some slicing and analysis based on what has already been developed, and we are trying new things now.

My advice for anybody who is implementing this solution is to start with a straightforward deployment. However, if they want to start with deep learning immediately, the functionality is there, but I would recommend the full deployment.

This is a good solution, but there is a little room for improvement.

I would rate this solution an eight out of ten.

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.
DanishAhmad
Master Data at a pharma/biotech company with 1,001-5,000 employees
Real User
Enabled me to plot the data on a graph and find the optimal area for where our warehouse should be but it needs better documentation
Pros and Cons
  • "It helped us find find the optimal area for where our warehouse should be located."
  • "I think better documentation or a step-by-step guide for installation would help, especially for on-premise users."

What is our primary use case?

My position is master of data and we are a customer of Anaconda. Our primary use case was to find technological solutions to manage our warehouse in conjunction with our customer base. Anaconda enabled me to plot the data on a graph and find the optimal area for where our warehouse should be located.

What is most valuable?

I mainly used the product for the libraries. 

What needs improvement?

I hit some contribution issues and attribution problems, so the product could be improved in that area. There's always room for improvement. It's one thing if you have an IT guy with the solution, but there were some cases when it wasn't so simple. There were a lot of typos in the documentation and because we were using the product on-premise, the solution had to be implemented by the IT team here and they had some difficulty fixing problems, particularly from the wall sheet. I think better documentation or a step-by-step guide for installation would help, especially for on-premise users. That would be great.

I haven't used it enough to think about additional features and I didn't hit any roadblocks that made me think about that. It worked well for me. 

For how long have I used the solution?

I've used it for a couple of months. 

What do I think about the stability of the solution?

I think it's pretty stable compared to the RStudio solution. There are some good tools which work better and quickly. 

How are customer service and technical support?

I generally don't use technical support, so I don't have any experience with that.

How was the initial setup?

The setup was quite complex. It was easy to get the whole way through, but we had some issues getting the correct function we needed and getting it properly. Deployment took around ten days to two weeks because our IT guys weren't able to work on it full-time. We didn't use any external help, it was our IT team who did the job. 

What other advice do I have?

I would recommend having a good background so that you know what you're getting into and whether Anaconda is the right solution for you. If you have a strong IT team to support the solution it's a very good tool to work on.

I would rate the solution a seven out of 10. 

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.
Senior tech architect at a computer software company with 1,001-5,000 employees
Real User
Top 5
Easy to use, quick to implement, and stable
Pros and Cons
  • "The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly."

    What is our primary use case?

    We primarily use the solution for deep learning and machine learning.

    What is most valuable?

    The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly. 

    We extensively use TensorFlow and Pytorch as the modules. 

    What needs improvement?

    I have nothing to say about improvements; I love this product. It's our bread and butter and we use it every day.

    We did have issues with virtual environments in the past, but that has worked itself out.

    For how long have I used the solution?

    I've been using the solution for 15 years.

    What do I think about the stability of the solution?

    The solution is pretty stable. A lot of our production is on Anaconda only.

    What do I think about the scalability of the solution?

    The scalability of the solution is good. We have about 100 technicians on it. At this time, we don't plan to increase usage.

    How are customer service and technical support?

    We've never had to reach out to technical support.

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

    We did not use a different solution.

    How was the initial setup?

    The initial setup was straightforward. Deployment takes a few hours.

    What about the implementation team?

    We handled the deployment ourselves.

    What other advice do I have?

    We use various deployment models but mostly work with hybrid models.

    We don't rely on Anaconda's deployment defense a lot. We use the solution mainly for Python distribution and deployment monitoring internals. We deploy in Docker and scale it.

    It's one of the best tools available out there. If you have to get started very quickly, it's great. Almost everything is ready for you to use. I think it's a wonderful tool for developers to get started with.

    I'd rate the solution nine out of ten.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    Nikhil Gaikwad
    Assistant Professor at Veermata Jijabai Technological Institute (VJTI)
    Real User
    Many data science applications on one single platform

    What is our primary use case?

    The best platform for a data scientist for development purposes. It supports applications which are needed for data analytics like Jupiter and predictive analytics like R.

    How has it helped my organization?

    It creates a good environment for a data science student to teach predictive and data analytics on one platform. 

    What is most valuable?

    Many data science applications on one single platform. This provides a platform for learning and development in the data science domain.

    What needs improvement?

    • Currently, it's working perfectly on Microsoft Windows but lags in open source OS like Ubuntu, Mint.
    • File uploading feature needs to be improved.

    For how long have I used the solution?

    One to three years.

    What do I think about the stability of the solution?

    Stability is good.

    What do I think about the scalability of the solution?

    Scalability is the best.

    How are customer service and technical support?

    Technical support is good.

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

    Previously, we were using RStudio, PyCharm for data science domain, but with this software, we've got a perfect platform to teach data science.

    How was the initial setup?

    The initial setup was complex.

    What about the implementation team?

    In-house one.

    What was our ROI?

    Good.

    Which other solutions did I evaluate?

    We did not evaluate other options.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.