Call Us

Home / Blog / Data Science / What Is Google Cloud Platform? : Components and Deployment steps

What Is Google Cloud Platform? : Components and Deployment steps

  • October 30, 2023
  • 3678
  • 96
Author Images

Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

Read More >

Hey! Ever wondered how your favourite apps and websites are always available, super fast, and can handle lots of people at once? It's like magic, right? Well, that magic is called cloud-based deployment, and Google Cloud Platform (GCP) is the wizard behind it all!

To get started, let's first understand what GCP is all about. Imagine it as a collection of powerful tools and services provided by Google, designed to help you build, deploy, and scale applications. It's like having a virtual playground in the cloud where you can host your projects, run complex computations, and manage data with ease.

Want to learn more about data science? Enroll in this Data Science Classes in Bangalore to do so.

What is GCP?

Google Cloud Platform (GCP) Cloud Based Deployment refers to the process of deploying applications and services on Google's cloud infrastructure. It involves configuring and setting up virtual machines, containers, or serverless environments to host and run software applications.


A group of cloud computing services is known as GCP (Google Cloud Platform), provided by Google, offering a wide range of infrastructure and platform services for building, deploying, and scaling applications. It encompasses various cloud-based services, including computing, storage, databases, machine learning, networking, and more. GCP is designed to provide a reliable, scalable, and secure infrastructure for businesses and developers. Optimizing Cloud Deployments

1. Services: GCP offers a wide array of cloud services, including computing, storage, databases, machine learning, analytics, networking, and more.

2. Scalability: With GCP, resources may be scaled up or down in response to demand, enabling flexible and effective resource usage.

3. Global Network: Google's extensive global network infrastructure ensures low-latency and reliable connectivity across regions.

4. Security and Compliance: GCP offers robust security features and is compliant with various industry standards and certifications.

5. Cost Management: GCP provides tools for monitoring and optimizing costs, allowing users to control expenses.

6. Integration with Other Google Services: GCP integrates seamlessly with other Google services like G Suite, YouTube, and Google Workspace.

What is Google Cloud Run?

Google Cloud Run, on the other hand, is a specific service within GCP that offers a serverless container execution platform. It allows developers to run stateless, containerized applications without the need to manage underlying infrastructure. Cloud Run abstracts away the complexities of server management, providing a fully managed environment for deploying and scaling applications.

1. Serverless Platform: A serverless platform called Cloud Run abstracts away the requirement to manage servers so that developers may concentrate only on creating and deploying code.

2. Containerized Applications: It is designed for running stateless, containerized applications, making it easy to deploy and scale applications in a consistent manner.

3. Pay-per-use Pricing: Users are billed based on the actual resources consumed by their applications, making it cost-effective.

4. Automated Scaling: Cloud Run automatically scales applications in response to incoming requests, ensuring optimal performance and cost efficiency.

5. Compatibility with Knative: Cloud Run is compatible with any container that follows the Knative Serving API, allowing for flexibility in the choice of container image.

6. Highly Available: Applications deployed on Cloud Run benefit from Google's infrastructure, ensuring high availability and reliability.

Both GCP and Google Cloud Run offer powerful tools and services for modern cloud-based application development and deployment. GCP offers a wide range of cloud services, while Google Cloud Run specifically addresses the need for serverless container execution.

Optimizing Cloud Deployments

Components and Description

1. Client Devices: These are end-user devices such as computers, tablets, or smartphones that interact with the application.

2. Internet: The Internet serves as the communication medium between client devices and the cloud infrastructure.

3. Load Balancer: To improve performance and availability, inbound network traffic is divided among several servers or resources.

4. Firewall and Security Groups: Ensure security by controlling access to resources, and protecting them from unauthorized access and potential cyber threats.

5. Google Cloud Run (or Compute Engine): This is where your application runs. Google Cloud Run provides a serverless platform for running containers, while Compute Engine offers virtual machines for more fine-grained control.

6. Cloud SQL (Relational Database): A managed relational database service, providing a place to store and retrieve structured data.

7. Cloud Storage: Offers scalable and highly available object storage for storing static assets like images, videos, and HTML files.

360DigiTMG also offers the Data Science Course in Hyderabad to start a better career. Enroll now!

8. Content Delivery Network (CDN): Caches content closer to end-users, reducing latency and improving the performance of the application.

9. Monitoring and Logging: Tools and services for tracking the performance, health, and behavior of the application, as well as recording events and activities for analysis.

10. Identity and Access Management (IAM): Manages user identities, permissions, and access controls, ensuring that only authorized users can interact with the application.

11. Auto-scaling and Elasticity: Automatically adjusts the resources allocated to the application based on demand, ensuring optimal performance and cost efficiency.

12. Backup and Disaster Recovery: Mechanisms to create backups of data and configurations, as well as plans for recovering from unexpected failures or disasters.

13. Compliance and Governance: Ensures that the architecture complies with industry standards, regulations, and organizational policies.

14. Networking Components (VPC, Subnets, etc.): These define the virtual network infrastructure, including subnets, routes, and gateways, to manage communication between resources.

15. Application Programming Interfaces (APIs): These facilitate communication and interaction between different components within the cloud environment.

This architecture provides a high-level overview of how the different components work together to deliver a cloud-based application on Google Cloud Platform.

Optimizing Cloud Deployments

Deployment steps on GCP: Using Google Kubernetes Engine (GKE)

  • Setting Up Your GCP Account

    Before we dive into deploying applications on GCP, you'll need to create an account. Follow these steps:

    • Go to the GCP Console.
    • Click on "Get Started for Free".
    • Comply with the instructions to create your account, including supplying the required billing data.

    Creating Project: Once you have a GCP account, you'll want to organize your resources within a project:

    • In the GCP Console, go to the project selector.
    • Click on "New Project" and follow the prompts to create a project.
    • Click "Create" after naming your project.
  • Create a Kubernetes Cluster

    Open Cloud Shell and run the following command to create a GKE cluster:

    Optimizing Cloud Deployments
  • Prepare your application

    Prepare your application code with a Dockerfile for containerization. Here's a sample Dockerfile for a Node.js application:

    Optimizing Cloud Deployments
  • Build and push Docker Image

    Build the Docker image and push it to Container.

    Optimizing Cloud Deployments
  • Deploy Your Application:

    Create a Kubernetes Deployment configuration file (deployment.yaml)

    Optimizing Cloud Deployments

    Apply the deployment to your cluster:

    Optimizing Cloud Deployments
  • Expose your application

    Create a Kubernetes Service to expose your application (yaml file).

    Optimizing Cloud Deployments

    Apply the service configuration:

    Optimizing Cloud Deployments
  • Access your Application

    You can visit your application using the external IP provided by GCP once the service has been built and load balanced:

    Optimizing Cloud Deployments

    That's it! Your application is now deployed on GCP using Google Kubernetes Engine. This process demonstrates key features such as containerization, Kubernetes deployment, and service creation for application access.

Google Cloud Testing

Testing is a critical phase in the development lifecycle, ensuring that applications meet performance, security, and functionality standards. Regarding Google Cloud Platform (GCP),testing strategies are tailored to harness the platform's capabilities effectively.

Types of Testing:

  • Unit Testing: Validates individual units or components of the application to ensure their correctness
  • Integration Testing: Verifies interactions between different components, ensuring seamless communication.
  • End-to-end Testing: Evaluate the entire workflow of an application, including UI, APIs, and integrations.
  • Load Testing: Simulates real-world user traffic to assess system performance under varying loads.
  • Security Testing: Identifies vulnerabilities and ensures compliance with security standards.

Testing Tools:

  • Google Cloud Test Lab: A cloud-based infrastructure for testing Android applications on real devices.
  • JUnit: A popular Java testing framework suitable for unit testing in GCP.
  • Selenium: A widely-used tool for automating web browsers, effective for end-to-end testing.
  • Apache JMeter: A performance testing tool ideal for analyzing application performance on GCP.

Also, check this Data Science Course Training in Hyderabad to start a career in Data Science.

Python Google Cloud

Python and Google Cloud form a formidable partnership for modern cloud-based solutions. Python's simplicity and versatility make it a favored language for developers. When integrated with Google Cloud Platform (GCP), it unlocks a world of scalable and reliable services. From deploying applications on Google Cloud Run to leveraging powerful APIs and services, Python seamlessly interacts with GCP's vast ecosystem. Whether it's data processing, machine learning, or building cloud-native applications, Python's rich libraries and GCP's robust infrastructure combine to create efficient, cutting-edge solutions. This dynamic duo empowers developers to innovate and deploy with confidence in the cloud computing landscape.

Optimizing Cloud Deployments

Constraints of GCP

While Google Cloud Platform (GCP) offers a powerful set of tools and services for cloud-based deployment, there are still certain constraints and considerations that users should be aware of. Here are some of the constraints of GCP Cloud Based Deployment:

  • Network Latency: Cloud-based applications may experience latency due to network distances between users and data centers. This can affect real-time applications or those sensitive to high latency.
  • Data Transfer Costs: GCP charges for data transfer between regions and over the internet. Heavy data traffic can lead to higher costs.
  • Resource Quotas: GCP has quotas on various resources like CPU cores, memory, and storage. Exceeding these quotas can lead to service disruptions.
  • Regional Availability: Some GCP services may not be available in all regions, limiting the choice of deployment locations.
  • Compliance and Regulatory Requirements: Certain industries or regions may have specific compliance and regulatory requirements that need to be considered during deployment.
  • Vendor Lock-in: Transitioning away from GCP or integrating with other cloud providers may involve challenges due to differences in services and architectures.
  • Scaling Limitations: While GCP allows for significant scalability, very large-scale applications may require custom solutions or additional optimizations.
  • Data Durability: While GCP provides redundancy and backup options, users should still implement proper data backup and recovery strategies.


Google Cloud Platform (GCP) Cloud Based Deployment is a transformative force in the realm of cloud computing. Its seamless deployment processes, scalability, and comprehensive suite of services have revolutionized the way applications are hosted and managed. With robust security measures and an intuitive user interface, GCP empowers businesses to innovate and thrive in the digital landscape. While there may be a learning curve, the investment in understanding GCP's capabilities pays off in the long run. As we look ahead, GCP stands as a powerful ally for achieving digital success.

Any queries or suggestions you may have are welcome! Your insights are invaluable as we navigate this dynamic landscape together. Let's continue to innovate and explore the endless possibilities that GCP offers. Feel free to share your thoughts, and together, we'll shape a future of digital excellence!"

 Looking forward to becoming a Data Scientist? Check out the Professional Course of Data Science Course in Bangalore and get certified today.

Learn the core concepts of Data Science Course video on YouTube:



Data Science Placement Success Story

Data Science Training Institutes in Other Locations

Make an Enquiry