Expected and Delivered things over GCP.
Use Cases and Deployment Scope
We use Google Cloud for many things, like running our apps, keeping data, and a few neat things with Machine Learning. It lets us not think about servers and iterate on building faster. It scales well under high traffic without breaking a sweat. BigQuery is suitable for scanning through multiple terabytes of data quickly. It is also convenient for team projects, as everything is so centralized. It simplifies things and allows us to go faster without falling down tech rabbit holes.
Pros
- First, scalability is just smooth. You launch something small, and suddenly, traffic spikes? Cp. The cloud handles it without having to panic or touch anything. Auto-scaling on the compute engine or GKE is a lifesaver.
- Cloud functions is also good for just write a bit of code and it runs with.
- Even when we need it. No server setup and provisioning. Suitable for automating small backend tasks.
Cons
- The UI is so confusing. The console is good, but it is like a maze. There are too many menus and settings, and things do not work as expected. It takes time to get friendly, and it is not friendly for new users.
- Support experience: Sometimes, you get a great engineer, but other times, it's very difficult to talk with them as they are unable to respond as expected and solve issues late.
- Region and zone are issues, as not all services are available in all regions, which is lacking when deploying something in the same region or zone.
Likelihood to Recommend
We used it to build a real-time analytics dashboard with BigQuery, and it worked very well, with super-fast processing and output to the dashboard, as well as no server management. However, when we tried using cloud functions for a live chat feature, the cold start delays failed and made it feel too slow for real-time use. So, it’s lacking for this.
