Google Cloud Virtual Private Cloud (VPC) vs. Jupyter Notebook

An effective pricing strategy is in place. Google Cloud VPC is the most secure since it runs on a private network and never contacts the public network. Google is well-known for its AI/ML and Kubernetes engines, both of which have a leg up on the competition. Google Cloud VPC's database services are yet to be improved.

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I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.

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• Serverless connector helps Cloud Run services to communicate privately.
• Serverless connector supports auto scaling.
• Being serverless, the cost is based on usage for serverless connector.

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• Simple and elegant code writing ability. Easier to understand the code that way.
• The ability to see the output after each step.
• The ability to use ton of library functions in Python.
• Easy-user friendly interface.

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• Sometimes the group settings tab lags a little
• Although overall it's easy to use, some of the sections lack documentation
• Routes section is very poorly implemented, requires a lot of digging to learn how to use

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• Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
• Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.

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VPC is a difficult concept to grasp and recommendations for configuring it would be helpful in educating the users to make the right choice while using the product in configuring networking for their cloud deployments. Also, the user interface can be intuitively designed so as to suggest templates to perform common configurations with regard to VPC.

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Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.

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I haven't had a need to contact support. However, all required help is out there in public forums.

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Google VPC and networking infrastructure is very matured and is built later after Amazon VPC. It made sure to address all the limitations faced by amazon VPC. Google Virtual private cloud is across regions while amazon VPC covers only one region but multiple zones. Google VPC is a global resource while AWS is a regional resource.

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With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.

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• Using Google Cloud VPC cloud storage has saved us on infra budget and per TB storage cost
• Using MySQL databases on Google Cloud VPC has improved the performance and cost of operation and maintenance
• Better pricing model has improvement on operational cost and increased revenue

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• Positive impact: flexible implementation on any OS, for many common software languages
• Positive impact: straightforward duplication for adaptation of workflows for other projects
• Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info