Complete guide to Google Cloud Platform (GCP)
The Google Cloud Platform (GCP) is a suite of cloud services that offers server space on virtual machines, internal networks, VPN connections, disk storage, machine language SaaS (Software as a Service) applications, and even something called TPU (Tensor Processing Units).
Again, Google puts a different name on their machine learning tools, but neither Google nor Amazon invented this technology. A server in Google’s data center uses the same Intel 8086 architecture as do servers in Amazon. What makes them different is pricing, features, and customer support.
Introduction:
Google Cloud Platform (GCP) is a suite of cloud computing services by Google, offering scalable, reliable solutions for computing, storage, databases, machine learning, and more. It empowers businesses to innovate and scale efficiently, providing a global infrastructure with advanced security features. GCP enables organizations to build, deploy, and manage applications seamlessly in the cloud, transforming the way they leverage technology.
Google Cloud Pricing :
As with other companies, Google’s prices for virtual machines (VMs) vary with CPU type and memory. Monthly subscription fees can mount quickly if machines are not sized correctly or there is no mechanism to monitor prices carefully. The key is to task someone to become an expert at and use the Google cost calculator to monitor the budget.
A sample GCP account dashboard. It says at the top that Your Free Trial is Over. The free trial runs out when you have consumed $300 in credits.
GCP Product and Services offerings:
The Google Cloud Platform includes more than 100 individual products—from AI and machine learning to data analytics, to networking, storage, and security.
App Engine :
App Engine is a framework and platform for developing and hosting web apps that offers automatic scaling to answer increased demand. Instead of running apps in containers, you can create an application and run on an abstraction of it. This allows users to run an app without a VM or container.
Big Query :
BigQuery is a serverless, enterprise-level data warehouse. It is designed to help users set up their data warehouses quickly, so you can start analyzing and using the data, and it can analyze petabytes of data in minutes.
Big Query uses a standard SQL dialect that is ANSI:2011 compliant in order to reduce the need for code rewrites. It also uses federated query, which means the platform can process external data sources without duplicating data.
Cloud Run :
Cloud Run let’s you create containers without virtual machines. To see how that is useful, and can save money, consider how you would set up containers without Cloud Run or something like it. You would have to (A) spin up a virtual machine and then (B) spin up containers inside. So why do (B) when you only need (A)? This is inherently wasteful. Plus it runs counter to the whole idea of containers, which is a minimal operating system that does not need all the encryption software, file transfer software, a system log, etc., that a full-blown OS has.
But a container needs some place to run. Google provides that with Cloud Run.
Compute Engine :
Google Compute Engine delivers virtual machines. That is, it lets you pick from different operating systems and hardware sizes to create virtual machine instances. A virtual machine, of course, is a full-blown computer riding atop something called a hypervisor. It’s really an abstraction of a computer, since it emulates a computer but does not have direct access to the screen or disk drive etc. Instead, the host operating system and hypervisor do that. This is how you take one individual computer hardware and divide it into multiple virtual computers.
VMs can be started and shut down as needed, so you can surge and contrast computing resources as the load on your application rises and falls. The result is cost savings, since you are not paying for idle hardware.
Memory store for Redis :
Cloud Memory store for Redis is an open source, in-memory database, similar to SAP Hana or Apache Spark. The idea is that the database will run faster when there are no disk drives. This is because disk drivers have moving parts (e.g., the disk controller) that are not as fast as solid state storage (i.e., memory).
So Memory store stores data in memory instead of on disk. But memory is expensive compared to disk storage, so it’s not suitable for all tasks—unless their importance justifies their cost. For example, a typical medium sized VM has 8 to 32 GB of memory. That is not much compared to 1TB of data that you can add to your laptop for a mere $200 for an
Persistent Disk:
Google Persistent Disk is block storage for VMs. It allows database blocks to be easily resized, backed up, and supported across multiple readers. It is also automatically encrypted, so users don’t have to worry about security for their cloud data.
You need a persistent disk, because when you shut down a virtual machine, the storage goes away. This is because the storage is the physical storage attached to the PC on which the VM runs.
Technical support :
GCP Support is available in various tiers. There is free, self-service support, and paid support.
For free support, Google refers you to Stack Overflow, which Google engineers monitor. There are also Google Groups and Slack channels.
Paid support includes phone support and, optionally, a dedicated account manager for your account, depending on which tier you purchase.
Google Tensor Processing Unit (TPU)
Google Cloud TPU is an offering unique to Google, but not entirely since it is a proprietary form of GPUs (graphical processing units designed to handle large scale mathematics, which is important in machine learning).
TPUs (Tensor Processing Units) are based on technology invented by NVIDIA, who leads and even invented the market in graphics cards. The TPU is a graphics-card-like CPU, except it has hundreds or thousands or core instead of the normal 4 or 8 of regular CPUs.
TPUs use the same technology as the graphics card in your desktop or laptop to do very large scale mathematics.
Google Cloud Platform Certifications :
Certification courses give your team a chance to thoroughly learn the platform, and certifications let engineers demonstrate their knowledge. Of course, there are plenty of excellent engineers who do not have any certifications, but it is a type of due diligence, good for audit compliance, marketing, SEC, and meeting privacy rules.
The names of available Google Certifications describe what they cover. Those are:
- Professional Cloud Architect
- Professional Data Engineer
- Professional Cloud Developer
- Professional Cloud Network Engineer
- Professional Cloud Security Engineer
- G Suite Certification
Frequently asked questions (FAQs) for Google Cloud Platform (GCP) Training:
Google Cloud Platform (GCP) is a suite of cloud computing services provided by Google. It offers a wide range of infrastructure and platform services, including computing power, storage, databases, machine learning, and more, all delivered over the internet.
GCP training equips individuals with the skills to leverage Google Cloud services, enabling them to build, deploy, and manage applications on the cloud. As cloud computing continues to gain prominence, GCP training enhances career opportunities and enables organizations to harness the power of cloud technology.
GCP provides scalability, reliability, and flexibility for businesses. It offers a global infrastructure, robust security features, and a wide array of services for data analytics, machine learning, and application development.
GCP offers various certification paths, including Associate-level certifications like Associate Cloud Engineer, Professional-level certifications such as Professional Cloud Architect, and specialized certifications like Data Engineer and Machine Learning Engineer.
GCP provides a free tier that allows users to explore and use a limited set of services at no cost. However, it’s important to review the specific terms and conditions of the free tier to ensure compliance.
To start GCP training, you can explore the official Google Cloud training platform, which offers a variety of online courses, labs, and certification programs. Additionally, there are third-party training providers that offer GCP courses.
While there are no strict prerequisites for GCP training, having a basic understanding of cloud computing concepts and some experience with IT infrastructure can be beneficial. Familiarity with programming languages like Python can also be helpful.
Yes, GCP certifications are highly valuable for career advancement. They demonstrate proficiency in specific GCP technologies and can enhance your credibility as a cloud professional. Many employers prioritize candidates with GCP certifications.
The time required to complete GCP training and earn a certification varies based on individual learning pace and prior experience. On average, it may take several weeks to a few months of dedicated study and hands-on practice.
Yes, GCP training is designed to cater to a broad audience, including both technical and non-technical professionals. Courses are available at different skill levels, allowing individuals to choose the content that aligns with their roles and goals.
