Google Compute Engine Reviews

We use Google Compute Engine as a VM. 

The main motive for choosing Google Compute Engine is pricing. 

The solution is highly available, and you can choose your security, such as creating firewall rules. 

I have been working with the product for eight years. 

I rate the product's stability around five to six out of ten. 

The node can be scaled 70 to 80 percent. 

Two people are enough to handle the tool's deployment. The deployment takes one hour to complete. 

I rate the product a ten out of ten. 

One of the most valuable features that Cloud Datalab has is that it integrates seamlessly with other Google Cloud Platform products. You can use Google Cloud Datalab to explore, analyze, transform and visualize data and build machine learning models using existing data in Google Cloud Storage or BigQuery. Cloud Datalab is interactive, so you can run portions of your code and see the results immediately as you work through your Datalab notebook.

Google Cloud Datalab has made it really easy for me to perform data exploration and analysis. Previously, I would have to wait for my data to transfer to a local server with a SQL database before beginning exploration. With larger data sets, the entire data transfer and analysis process could take hours if not days. With Google Cloud Datalab and Google Cloud Platform, I can receive processed results in seconds, not hours or days, without significant delays transferring data, since my data is already in Google Cloud Storage and easily accessible for consumption in Google Cloud Platform. In addition, I am starting to leverage the Google Cloud Machine Learning APIs built into Google Cloud Datalab to receive better insights on my data.

You can already run BigQuery SQL queries in Google Cloud Datalab but one thing I would like to see improved is the user experience when constructing these SQL queries, similar the tools that the Big Query web console offers. For example, I would like to see improved support for automatically formatting SQL queries. It would also be helpful to have a button that will check a query for SQL syntax errors before running the query which is also available in the BigQuery web console.

I've used Google Cloud Datalab for about 18 months. In February 2016, I ran into an issue while configuring Datalab and opened a new issue on the Datalab Github page. I received a response on the same day from a Google employee on the core Datalab development team. After resolving my issue with the help of a Cloud Datalab development team member on Github, I became more interested in Cloud Datalab to the extent that I decided to download the source code and build Datalab locally. I learned a lot in the process.

There have been a few (rare) instances where I've encountered a stability issue during the beta pre-production stage of the product. I should mention that it was pretty easy to revert back to a previous stable build. The stability issues are even more rare with the production version and since there is a very large user base the development team is very quick to fix regressions and troubleshoot performance issues.

No, and I don't expect any issues with scalability as the Datalab Kernel is running in a Google Compute Engine (GCE) virtual machine which can be scaled according to the developer's needs. In addition, Google Cloud Platform is designed around scalability at the Petabyte-scale.

10/10. The Datalab development team is very responsive on both StackOverflow and Github. I encourage you to make use of the free support that is available from the online Datalab Community. You can even build/run Datalab from source code if you have the interest to tinker around and learn more about Datalab on your own. If you prefer Google specific support, there are three support tiers available. You can also submit feedback directly from the Datalab user interface.

Previously I used Jupyter (formerly IPython) as a tool for interactive data analysis. My primary reason for switching is that Datalab has built-in integration and high level magic commands for certain Google Cloud Platform products, such as Google Cloud Storage and Google BigQuery. In addition, Datalab has built in charting capabilities.

Yes, the initial setup is very straightforward because the Datalab kernel is installed on a virtual machine in the cloud rather than on your local machine. In addition, the quick start documentation was very easy to follow. Google Cloud Datalab is installed using Google Cloud Shell which is accessible from a web browser which means that you can have access to the full Google Cloud Datalab user interface from a light-weight laptop such as a Chromebook.

Google Cloud Datalab is an open source product (Apache 2.0 License). There are costs associated with having the Datalab Kernel running in a Compute Engine Virtual Machine, however you will only pay for the cloud resources you use. To save on costs, you can stop the GCE Virtual machine and start it when it is needed again. There may be other costs for additional resources that you decide to use, such as Google BigQuery or Google Cloud Storage.

Yes, previously I used Jupyter Notebook. Google Cloud Datalab is built on Jupyter (formerly IPython) so it was easy to transition to Google Cloud Datalab.

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.

We use it primarily for our testing. For example, if you want to deploy a laptop or something to test before deploying something on our appliance site. And we use GCP.

I'm just using it for testing and lab deployment setups.

Documentation is the best feature of Google Compute Engine. For example, the other day, I wanted to get something running with Nested Virtualization. So, I was able to do that just by going through Google's documentation. That's pretty good.

The solution could be more similar to something like ESXi. For example, in GCP, you don't get layer two connectivity between two VMs on the same subnet. So that could be great. Between two VMs on the same subnet in the same VPC.

Another area of improvement is the support. They did respond very fast, but they did not find a solution to our problem.

I have been using this solution for more than four months. 

It is a stable product. I haven't had any issues.

It is scalable. There are around ten end users using this solution. 

The response time is good, but ultimately, if they weren't able to help, it's not the best, but it's okay because at least they respond quickly.

Neutral

It's the most engineer-friendly product compared to Amazon AWS or Azure.

The initial setup is pretty straightforward. 

The pricing is comparable to the competitors. I haven't noticed any difference.

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.

This is a web application and deployment is moving to GCP for disaster recovery purposes .

It has dramatically increased our costs over running dedicated hardware.

All of the cloud solution providers are basically the same. There's a little bit around the edges that is different. Each provider has its own kind of unique products but for the most part, 90% of the coverage between all of them is the same.

The biggest problem is that it's got a very arcane and complex security environment that has to be very carefully set up and maintained. Because GCP has is such a huge attack surface and is so prominent it any security lapses can quickly become fatal.

The problem with good security is that  it more difficult to use the GCP features. Faced with a difficult to use interface people will find ingenious ways to circumvent the security.

Because GCE is tied into the overall Google services security model I do not believe that it can be improved without making the user experience more difficult.

GCP/AWS... have had more downtime in the last 5 years than the current implementation has had.

It's scalable. 

I have used 4 other cloud providers over the years and have been using one provider for some applications for over 10 years.

The switch from internally managed services to a cloud environment was a management decision.

The initial setup is reasonably straightforward.

The existing environment is fairly flat and static.

An external firm has been contracted to provide the design and implementation of a DR solution which includes moving existing components to GCP.

They have little understanding of the existing environment and that lack of understanding has caused several roll backs on requirements to keep the operational cost manageable.

At this point there is no ROI unless the business model fundamentally changes.

For the foreseeable future this will be a significant budget deficit.

It's $60,000 to $70,000 a month to replace about $10,000 a month in data center costs. 

The choice was not mine so 

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.

I used GCE as simple virtual machines to run applications such as sonar and logstash as I also used to create robust clusters to scale applications with kubernetes.

The GCE, with its versatility, allows us to create virtual machines for any situation. We can either put an application in production, or run software like Sonar to guarantee the quality of the code.

Compared to other IaaS providers, I believe that Google Compute Engine (GCE) stands out with the ability to customize the virtual machines. With other providers, these are pre-determined specifications. With this you can create machines specific to your application.

The GCE only has virtual machines with processors and video cards of the new generation in the North American region, the GCE has expanded in new areas, like Latin America, but it does not carry powerful hardware.

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.