What is most valuable?
You can perform your data queries the same way using BigQuery. That's one of the advantages. You don't need to learn anything new. Whatever SQL queries you know, you can implement there.
Any developer can study and implement what we do for cloud operations with the database side. It's not a big deal for developers. They can implement things using Google Cloud Storage.
Google Cloud Storage provides good safety measures. They use tokens for access control, which means nobody can access the data without proper authorization.
If you compare it with other cloud services like Azure, there might be some glitches in Azure. However, from my experience, Google Cloud Storage is very stable.
From a security perspective, I've never tried to hack it myself. There might be potential for brute force attacks to get into the application, but that's just speculation. It would be quite difficult to breach from a developer or normal user perspective.
What needs improvement?
The main area for improvement would be providing a local setup or working area. Currently, developers need to commit code to test changes. A lower-cost option for this would be beneficial.
A system similar to Bitbucket would be advantageous. At present, data personnel complete their work, commit the code, and then deploy it to the Kubernetes engine. A Bitbucket-like system would simplify this process for developers.
What do I think about the stability of the solution?
Google Cloud Storage is stable.
What do I think about the scalability of the solution?
The solution is scalable.
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How are customer service and support?
The solution's support team responds fast. They revert to emails within one to two hours.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
I mainly use two cloud platforms: Microsoft Azure and Google Cloud Platform (GCP). These are the only two clouds I've worked with. I know about Amazon Cloud, but I've never used it. Among these, I primarily use Google Cloud.
Regarding differences between Azure Cloud and Google Cloud, one main distinction is their database systems. With Azure Cloud, you're typically expected to use SQL Server as your backend. In contrast, with Google Cloud Platform (GCP), you're supposed to use BigQuery and Big Data.
How was the initial setup?
If you know the basics, you can deploy the product easily. One person can do the deployment but needs to know how to deploy. Developers will provide the JAR or WAR file, which must be deployed. The deployment process is challenging but manageable if you know what you're doing.
We usually update versions incrementally. For example, we might release version 1.0.1 today, 1.0.2 tomorrow, then 1.0.3 the next day. That's how we increase the version numbers.
We typically use three branches: development, testing, and production. When we modify the development branch, we need to deploy it to the production environment. You need to create a branch based on that and deploy it accordingly.
You can't simply roll back to a previous version. You have to create a new version, and the system won't accept the next build unless you do this. Once the tool is configured, maintenance is easy.
What's my experience with pricing, setup cost, and licensing?
The solution is expensive compared to other cloud platforms.
What other advice do I have?
I don't know how much storage our organization has bought, but I don't find any issues with the storage's performance.
We create a backup almost daily, but I'm unsure how long they keep the data. Data from last year is still there, and I'm not sure when they'll archive it.
You can integrate AI features with Google Cloud Storage. There's an option for that on the platform. When you're coding, you need to add annotations. Once you enable this for your services, it automatically picks up your AI endpoint, which you specify in a proper file.
At that point, you can use AI with your data. However, the developer creating the application needs to be aware of how to set this up.
If you want to integrate an AI application in the future, you need to expose the app services based on that. Specifically, you need to make that particular URL accessible. You need to provide this capability. Only then will any AI tool you're using be able to access it.
I rate the overall product a nine out of ten.
Disclosure: I am a real user, and this review is based on my own experience and opinions.