In this blog post, I want to explore the uses of Google Cloud Platform (GCP) in today’s world.
The GCP was originally created by Google specifically so that they could store data on the internet, which provides a higher level of accessibility than other storage options such as iCloud or Dropbox.
Today there are many different use cases for how people are using it.
Some examples include running applications online even if they’re not fit for public consumption; storing multimedia content such as photos and the videos-the list goes on!
One common misconception about this platform might be that it only focuses on storage but you have access to so much more!
You get access via APIs directly into G Suite tools: Gmail, Google Docs, Calendar, and more.
Google Cloud Storage
With Google Cloud Storage, you can store and serve an unlimited amount of data for applications that need to be highly available or scalable without incurring service charges even if your usage exceeds the free tier levels.
You can upload any number of files, photos, videos, audio clips up to 15TB in size each, which are instantly accessible from anywhere with an Internet connection meaning storage costs are just cents per gigabyte a month.
These allow developers to create small pieces of code which can perform tasks in response to webhook events from various sources (such as Slack or Google Cloud Storage), or run periodically as a cron job. Another uses of google cloud platform.
This is the part of GCP that lets developers create, deploy and manage web applications on Google’s infrastructure with easy-to-use APIs.
It manages everything from load balancing traffic across multiple instances to automatically scaling up when there are sudden spikes in demand, so you don’t have to worry about managing your own servers!
This tool allows companies who work with huge amounts of data (think YouTube) to analyze it at scale without having engineers write custom code for each step of their process.
Instead, they can rely on a graphical interface where they specify what needs to be done – such as filtering out spam messages.
And then let the machine do the work.
This is a managed Apache Hadoop that allows you to create clusters and jobs without any specialized IT knowledge or skills.
So you can focus on your analysis instead of infrastructure management.
Cloud Datalab has fast Jupyter notebooks (a tool for data exploration) with real-time collaboration in mind.
As well as automatic scaling, powerful GPUs from NVIDIA, auto-saving to Google Drive, and an easy way to share code libraries between teammates.
It also integrates seamlessly with other GCP tools like BigQuery for SQL analytics and TensorFlow for deep learning AI modeling.
Google Compute Engine
A virtual server hosting service that you can use for powering your web apps, data processing, and storage needs.
Google Container Engine
This provides you the ability to deploy and manage software containers across a managed cluster of virtual machines or on Google Kubernetes Engine clusters.
You can create an automated lifecycle without any specialized IT knowledge or skills so that you don’t have to focus on infrastructure management.
It is used with Docker (an open-source platform that automates deployment) in order to orchestrate workloads at scale – not just managing resources but running them too!
The kubectl command-line tool lets you control all aspects of your cluster from anywhere, enabling quick changes instead of long offline periods.
Another uses of Google Cloud Platform is cloud SQL.
This is a MySQL service that automatically manages replication and sharding for you, so it’s easy to grow your database as needed.
The backups are stored in the cloud and can be restored on any instance or from Google Drive if desired.
Google Cloud Interconnect
Located at one of Google’s data centers around the world, this connection enables users to easily run workloads across continents without complex configurations, reducing latency by up to 50%!
It operates using open standards like BGP and MPLS with no proprietary hardware required.
Providing low-cost connections between virtual machines running on different networks (e.g., AWS), enabling sharing of Internet traffic routing equipment among many providers while ensuring interoperability with all major VPN standards.