Anaconda Business Reviews

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.

I have experience with Anaconda for years.

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

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

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

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

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.

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.

We use Anaconda for data science model and development, specifically for coding in Python. We use it mainly for forecasting and predicting models within the environment of Anaconda Python.

Anaconda has been very beneficial for our organization. The integration between different utility projects and the overall efficiency for data science and machine learning tasks has been very helpful. It provides a coding environment that saves time in setting up individual tools.

Anaconda has multiple valuable features. It provides a unified platform where you can install Jupyter, Python Spider, and other related tools without needing separate installations. The ability to work on multiple programming languages like Python, R, and Ruby is also significant. One of the best aspects is the community support.

Anaconda consumes a significant amount of processing memory when working on it. This is something that needs improvement as it can impact performance.

I have been using Anaconda for four years.

Anaconda is stable ninety percent of the time. However, there are occasional delays due to high memory consumption.

There is generally no need to scale Anaconda. The tool has minimum system requirements that need to be met for optimal performance.

Anaconda has great community support, and its technical support is also helpful.

Positive

The initial setup of Anaconda is straightforward. One person is enough for the installation and deployment.

Anaconda is cost-saving as it is open-source and the installation is easy. There is no need for a pricing structure for the basic version.

Anaconda does not require a pricing structure, and it is available as an open-source tool. The features of Python, Jupyter, and others are free to use.

I'd rate the solution nine out of ten.

I have been very enthusiastic about artificial intelligence and machine learning since my first year. I started learning Python in my first year and was using a MacBook with the M1 chip, which didn't have native Python support. 

I discovered Anaconda, which developed Python for Mac, so I started using it for Python. Later, I realized its use cases in machine learning and data science.

The best thing is that it provides all the frameworks and makes it easy to create environments for multiple projects using Anaconda.

It is easy for a beginner to learn to use Anaconda. Comparatively, it is easier than using virtual environments or other environments because of the Conda environment. 

However, there are many things in Anaconda that people need to be aware of, so it can be challenging.

A lot of people and companies are investing in creating automated data cleaning and processing environments. Anaconda is a bit behind in that area. 

Maybe a graphical user interface where we can just input our dataset, and it will handle everything graphically and automatically, could improve Anaconda.

I have been using Anacondas since my second year in college. This year, in 2024, I just graduated with my computer science degree, so it's been about two or three years.

So, I have been using it for two to three years. 

Everything new has some bugs, but it is quite stable for machine learning applications.

In my college, almost 70 to 80 percent of people used Anaconda.

I contacted the support when I was creating a hand gesture product, and some libraries were not working because of the Python version mismatch with the library version. So, I contacted Anaconda support, and they were helpful, replying within a day.

Positive

It is easy to install, set up, and deploy Anaconda.

Anaconda is free to use, but in terms of hardware costs, you might need heavy GPUs to run CUDA and other demanding tasks, which can be expensive. It works on all systems and is not subscription-based.

If you're into machine learning and data science, I would absolutely recommend it because it's essential for those fields. But if you're just exploring and learning Python, it might be too heavy for your computer. 

However, if you're dedicated, I would recommend it.

Overall, I would rate the solution a ten out of ten because it has a lot of functionality available, supports many libraries, and the developers are continually improving it. It suits my needs best.

If I had to go back, I would use Anaconda again.

I have been using Anaconda Business for data science practice over the last two years. Anaconda Business serves as my primary tool for data analysis of data science projects. I specifically use Anaconda Business for Jupyter notebooks, where I employ the Python language for predictive modeling and data analysis.

Anaconda Business offers excellent visualization tools including Visual Studio, comprehensive analysis tools, and Python language analysis that significantly helps with building analysis and machine learning models. I have found that Jupyter notebook within Anaconda Business provides the best support for outlier detection and scatter plot work. Anaconda Business has positively impacted my organization by reducing analysis time, which allows me to build machine learning models in a short timeframe.

I am still becoming familiar with Anaconda Business tools after using them for only two years, so I am not utilizing Anaconda Business extensively in data analysis and data science projects yet. I cannot confidently recommend specific improvements at this stage, as I am still in the learning phase with the platform.

I have been working in my current field for the last six years.

In my experience, Anaconda Business is stable.

Anaconda Business scalability is quite useful as compared to other tools.

I have not used customer support for Anaconda Business yet.

For data science, I am using only Anaconda Business and Jupyter notebook, with no other tools.

I do not have experience with pricing, setup cost, and licensing for Anaconda Business, as I am not a decision-taker in my company and these matters are not my concern.

I am not currently familiar with whether I have seen a return on investment from using Anaconda Business.

Before choosing Anaconda Business, I evaluated Power BI as an alternative option.

My advice to others looking into using Anaconda Business is that it offers different tools that are usable for data analysis projects and predictive modeling, making it a great tool to use. I would rate this review a 7.

I use the tool for Jupyter Notebook.

The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors.

Anaconda could benefit from improvement in its user interface to make it more attractive and user-friendly. Currently, it's boring. 

I have been using the product for five years.

Four to five developers use the product. 

I haven't contacted the tool's support team for any help or questions. I primarily use it for one of its services, and I write my own code within that service, so I haven't felt the need to contact support.

The product is straightforward and self-explanatory. Users can easily follow the next steps provided and install it without much difficulty.

The tool is open-source. 

Some basic tutorials can be found on YouTube, which can help you understand how Anaconda services work. Watching these tutorials can make it easier for someone to use the product. Using it for the first time can be considered at a medium difficulty level, neither too easy nor too difficult. The package management system has greatly improved my development process. I can easily install and incorporate any package I need into my work. I rate the overall product an eight out of ten.

Voice Configuration and Environmental Management Capabilities are the most valuable features. 

If we want to do some big applications, then we need to install a lot of packages. It also takes up a lot of space.

I have been using Anaconda for six years. 

It is a scalable solution. 

The initial setup is straightforward. The deployment took twenty to thirty minutes.

Overall, I rate the solution a nine out of ten. 

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

It is stable. 

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.

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

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.

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.

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.  

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.  

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.  

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

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

I have not seen any issues with scalability.  

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

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.  

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.  

The product is open-source and free to users.  

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.