My advice to new users would be to hire a good data engineer since it's only user-friendly for experienced users. They should also think about what is the main trigger for moving to cloud and if they have a precise plan for doing that. 

Using Compute Engine is just the first step. After, you can decide whether you will migrate your database to cloud or not. 

I recommend having at least one data engineer and a solution architect to help create a plan and reach targets.

Overall, I would rate it an eight out of ten.

The solution meets all the requirements. I highly recommend Google Compute Engine. Looking at it from the architecture point of view, GCE is as good as working with an on-prem workload. It is very transparent to the end user, who wouldn't know whether it is on Google Cloud or whether it is on Azure or on-premises.

I rate Google Compute Engine a ten out of ten.

First of all, you have to analyze your workload and sign which kind of machine type, how much memory you need, and what kind of traffic you expect. Analyze your workload and your requirements. Make a cost explanation of how much you can spend for your service in a month. 

Once you analyze your workload and determine your cost explanation, use the pricing estimation with the price target. And because you are the amount you plan to spend in a month. Find the cost provided by the price package. Compare both. If there is a minor or no difference, both are comparable. Everything is matching, you can go with your requirements and build your infrastructure based on your economy. 

If there is a huge difference, you can reassess your resources in pricing guidelines and make resource changes. But the things you have to come there. If there is no significant difference, you can proceed. Otherwise, change the option. 

And then you will get a solution, how to make both things like we've both things, like the infrastructure, it's preferred in your pricing estimation, and your actual costs. If both are almost similar, you can create it, use it, and deploy it.

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

I rate the product a nine out of ten. 

So, there are three key takeaways. One is to meet all your use cases from a functionality perspective; the solution is robust enough.

Second is that it is quite intuitive and easy to use from a development perspective as well as from an operational aspect as well. So, the second is from the ease of usage perspective.

Third, from the innovation side of things, GCP has been at the forefront. Whether it is about cost or the features; this solution always been on top of its game. So, they were the ones who brought this per-minute pricing itself. So, the solution has always been on the innovation side of the cloud.

Overall, I would rate the solution an eight out of ten.

I would suggest new users take a look at Spot instance since it can help you significantly reduce costs. I would like to see dedicated and better UX for container deployment in the tool’s future releases.

Google Compute Engine is easy to use and has great authentication management for Google APIs. The solution can directly escalate and it's easy to get started with.

I rate Google Compute Engine 10 out of 10. 

I rate Google Compute Engine a nine out of ten.

I rate the product a ten out of ten. 

Don't hesitate to try Google Cloud Datalab if you are in need of an interactive data visualization tool. Follow the quick start documentation and don't be afraid to get your feet wet. If you prefer a structured learning environment, there are also Google Approved paid courses available.

I could not have found a better product to perform interactive data analysis and begin my career as a Data Engineer. The are so many sample Datalab notebooks which makes it really easy for someone new to run and modify a Datalab notebook regardless of their level of knowledge of big data or python. After launching Datalab, simply click on the help icon in the navigation bar and then click the "Samples and Tutorials" link. Google Cloud Datalab is an open source project so reporting bugs and submitting feature requests is easy. If you're feeling brave, you can even submit a pull request in the GitHub project to fix a bug or modify Datalab functionality. The project maintainers are really welcoming and encourage participation from new contributors. In addition, the Cloud Datalab community is very responsive on StackOverflow.

As long as you stick to the actual web console, the GUI, you can use it even if you have little understanding about what is sectionally going on.

Overall, I would rate the solution a seven out of ten. It's pretty customizable; for example, when you are using the CLI, you can get pretty much anything done. 

But I didn't give it a ten because it has some limitations. For example, you don't get through layer two connectivity. So, I've had some difficulty deploying custom VMs. For example, you can't deploy a KVM file to file directly on GCP.

The advice that I would give to someone considering this solution is that you have to take a close look at what your costs are, where you want scalability and consider the reliability issues because it's not always obvious where . There are a number of places where commercial cloud applications work very well. There are places where it doesn't. For applications where you have large dynamic changes in load and storage, the cloud environment can be great because you pay for what you use and can take advantage of running up resources on demand. For static environments where you've got a reasonable amount of infrastructure, a cloud solution can be highly expensive and no more reliable.

If the focus is cost reduction, they can migrate to the GCE without the fear that it will not bring results. The migration is simple and fluid, and there is nothing complex about it.