Engineer at a university with 51-200 employees
Reseller
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.

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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 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.
PeerSpot user
Master Data at a energy/utilities 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.
PeerSpot user
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March 2024
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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.
PeerSpot user
Analytics Analyst at a tech services company with 10,001+ employees
Real User
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.
PeerSpot user
PeerSpot user
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.
PeerSpot user
Cluster Manager - Risk at a financial services firm with 10,001+ employees
Real User
Offers free version and is helpful to handle small-scale workloads
Pros and Cons
  • "I can use Anaconda for non-heavy tasks."
  • "Anaconda can't handle heavy workloads."

What needs improvement?

Anaconda can't handle heavy workloads. From an improvement perspective, I want Anaconda to be able to handle heavy workloads.

For some enterprise versions or wherever there is a need for cloud-based tools to deal with large amounts of data, I feel that it would be good if Anaconda has a partnership or is able to integrate with Databricks.

For how long have I used the solution?

I have experience with Anaconda for years.

What do I think about the scalability of the solution?

In my company, around 10 to 30 people were using the product.

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

In my company, I use Databricks 90 percent of the time.

How was the initial setup?

I have not encountered any challenges during the deployment process of Anaconda, especially considering that I haven't worked on heavy data.

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

My company uses the free version of the tool. There is also a paid version of the tool available.

Which other solutions did I evaluate?

In terms of development, Anaconda is better than Databricks because computing costs are involved while using the latter tool. If the data is not too large and if a company can work on sample scripts while ensuring that within the organization, everything gets standardized, development can be done on Anaconda, and then users can run production scripts on Databricks because it is popularly used considering the heavy data it can manage.

What other advice do I have?

I have used the product for data engineering and for ML models.

Anaconda's ability to streamline our company's workflow in data analysis has pros and cons attached to it. In terms of pros, Anaconda's advantage over Databricks revolves around the use of system resources. Everything in Databricks is on an online computing basis, where our company uses the product's resources, but our own resources aren't utilized. In our company, we have heavy machines with us, but they aren't used when we use Databricks. I think some small-scale workloads can be handled in Anaconda. In terms of the entire lifecycle, I think Databricks has a lot of advantages over Anaconda. You have features that help you revive old models or deploy your models within the same Databricks. Databricks offers an end-to-end lifecycle over Anaconda.

Working with the integrations of various libraries and tools within Anaconda, I have not faced any issues. Anaconda offers advantages to its users when the workload or data is not much. I am not sure if the paid version of the product is on a computing basis, but if it is, then there is not much of a difference between Anaconda and the other products in the market. As per my understanding, even the enterprise version can be hosted on the company servers, so there are not many costs involved.

I recommend the product to those who plan to use it. The product can be useful in multiple sectors other than the financial sector. In the financial sector, Anaconda can be useful if the workloads are very low, there are many non-priority tasks, and the data is not much used. Issues occur when teams working in collaboration want to use Anaconda and Databricks together. I can use Anaconda for non-heavy tasks. I can go with Databricks for heavy tasks. It would be good if Anaconda and Databricks could have integration capabilities. For computing, you can use Anaconda and the resources from Databricks.

I rate the tool an eight out of ten.

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