Anaconda vs. AWS IoT Core

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

End-to-end encryption is an amazing feature because we use IoT to connect to various devices in order to gather data/ stats in real-time. We're able to publish solutions with ease and at a faster rate because of AWS IoT Core. However, its inability to interact with other IoT tools is a big con that I would like them to improve upon.

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

• AWS IoT Core integrates power analytics and an AI solution for sensor data processing.
• Great protocol support including HTTPs and MQTT required to connect resource constraint IoT devices to cloud
• High security standards during data transmission
• Scalability and a great user community

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

• Ability to integrate with Ethercalc software solution
• Ability to introduce NFC toolchains and systems which could be utilized in iot modular gateway solutions like drones
• Ability to reduce latency as the solution scales is very much needed
• Hybrid solution which can integrate other existing iot solutions into AWS iot core

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

I give AWS IoT Core's overall usability this rating because it is very easy to use and is enjoyed by all of our staff. The only problem is that it sometimes glitches and it freezes a lot. So overall, the usability of AWS IoT Core is very good, and we will continue to use it.

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

It covers all the aspects of IoT services required for an IoT company. It supports all the industry-wide protocols for secure data transmission and integrates powerful AL and ML technology for data analytics. For data storage, Amazon S3 is a great solution. Strong tech support and user community. Since it is widely used as compared to other products, there is an abundance of training and learning material on the web.

Incentivized

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

Azure IoT service provides more or less the same services as compared to AWS IoT core, however the costing of AWS lead us to continued usage of IoT core over Azure IoT services. Also, considering our existing technology stack is on AWS, it was a natural selection for better integration and ease of use.

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

• Gives confidence to prototype iot solutions across lots of devices in little time
• Ability to meet requirements for managing a variety of communication protocols
• Learning iot analytics and visualization for faster insights enablement
• Onboarding devices and managing quickly without building inhouse solutions

Incentivized