Anaconda vs. JFrog Artifactory

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

It works at scale and a large number of accessible pipelines for searching, repository updates and indexing will become easier. JFrog provides end-to-end solutions for all DevOps needs. With this, Jfrog Artifactory specifically implements the management of highly available repositories, with a smooth interface and integration with all the main CI tools on the market.

Incentivized

• 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

• Artifactory Management acting as a repository manager of docker images, application and component dependencies
• Automate pipelines and thereby releasing changes faster
• Supports high availability and scalability with multi site replication

Incentivized

• 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

• We can always use support for more different types of packages in Artifactory.
• We also would like to see the Artifactory X-Ray produce continue to mature.

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

The main problem that seems intractable is getting the checksum of the artifact. Managing container artifacts is a game changer for us during project execution, as the container artifact type exposes all base image and Docker file steps. This makes debugging or analysis easier. Jfrog Artifactory provides promotion feature and can automated from one environment repo to another environment repo before the deployment occurs.

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

Support tickets take days to respond. The most basic of questions that should be knocked out in a few hours don't get answers for days. Tickets are also closed without resolution.

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

JFrog Artifactory has a much more friendly GUI, making package exploration less of a chore to do. Other than that, their features are pretty much comparable to each other. Both support multiple types of packages; both have API that can integrate well with CI/CD pipelines.

Incentivized

• 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

• So many times it happens at the time of dependency resolution some of the servers are down e.g NPM, Maven central, PiPy in that cause our builds starts failing. By proxying these repositories with JFrog this is never happened again.
• It reduced the additional cost of container image registry and management effort.
• Support of integration with Build, Monitoring, and CI tools resulted in smooth automation and management.

Incentivized