Anaconda vs. AWS CodeArtifact

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

We have a small team with limited resources and it worked well for us. Hence I can conclude that AWS Code Artifact are well suited for organizations which have limited resources in terms of hardware and access to administrators for setting up artifact repository in-house. AWS Code Artifact is also suited particularly well for organization(s) which are already using AWS Services/Infrastructure (eg. EC2) . It works quite well with existing AWS services and completes the gap which existed in AWS offering for quite some time. Organizations can move their entire DevOps toolchain and infrastructure to Amazon. It is less appropriate for organization(s) which rely on artifacts like Debian, C/C++, Go etc as AWS does not support those fully.

Incentivized

• Installing packages is very easy with Anaconda. Anaconda comes with 'anaconda navigator', a terminal-like utility from which you can easily install R packages and python libraries.
• Launching R and python IDEs as well as Jupyter notebooks from anaconda navigator is simple, and Anaconda makes it very easy to keep these packages up-to-date.
• I really like the fact that if you don't want to install the full version of Anaconda, you can opt to install a lightweight version (called Miniconda) that includes less python libraries and only core conda. I've installed it when I didn't want to take up as much disk space as Anaconda requires, but it works just the same.

Incentivized

• Code Artifact is a cloud based artifact repository so there is no installation required.
• Code Artifact comes with out of the box security. Using RBAC and encryption
• Total cost of usage is less than setting up in-house servers
• It is accessible from any where.. so no additional network setup is required.

Incentivized

• More graphics need in Spyder book. If you work for couple of years then you will be bored with the graphics.
• Extra tools are required for making it secure. We uses extra tools for adding Username /Password to Jupyter.
• R Studio Hangs a lot when open from Anaconda Navigator.

Incentivized

• CodeArtifact does not support packages like debian. It will be nice to see them support that.
• Does not provide security scanning of the packages
• Lacks support for third party CI/CD toolchain like Jenkins

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

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

One of the main competitors to Anaconda can be Google products such as Colab. Colab gives you the flexibility to handle large datasets gives it an edge over Anaconda. But again, the ease of access and usability of Anaconda stacks up against Colab. Besides, Anaconda relies more on your machine which makes it safe to use.

Incentivized

AWS CodeArtifact is an excellent choice for organization(s) which are looking to move their infrastructure and devops toolchain to Amazon. It is very useful for teams/organizations on limited budget or do not want to take on infrastructure and maintenance costs associated with the artifact repository. Other software solutions require resources for setting up and need ongoing maintenance.

Incentivized

• Positive impact - Multiple options for data presenting , visualizing and sharing. (Eg: R-Markdown).
• Positive impact - Ease of access to build complex machine learning models. (I work in NLP, it has multiple built in models to analyze the various contexts).
• Positive impact - Conda package let's to deal with external packages which can be used in Jupyter.

Incentivized

• Overall CodeArtifact has positive ROI on the our team. We had limited budget for procurement of server/administrators. With CodeArtifact we were able to get some savings.
• We were able to deliver faster hence customers were quite happy. That led to customer satisfaction
• We didnt have to invest on maintaining network infrastructure/uptime and security. That saved us quite a bit of hassle and funds.

Incentivized