20 Google Cloud Reference Architecture to Start your GCP Architect journey.

Biswanath Giri

Published in

Google Cloud - Community

26 min readMay 4, 2023

1. Start with a basic Google Cloud infra Architecture Diagram

To start with a basic template for a Google Cloud infrastructure architecture, you can follow these general steps:

Identify the requirements of your application: Consider the requirements of your application, such as the expected traffic, the type of data it will process, and the level of security needed. This will help you choose the appropriate components and services for your infrastructure.
Define the components and services: Based on the requirements of your application, define the components and services that you will need in your infrastructure. These could include virtual machines, databases, load balancers, firewalls, and more.
Determine the relationships between the components: Once you have identified the components and services, determine how they will be interconnected. For example, you may need a load balancer to distribute traffic across multiple virtual machines, or a firewall to protect your infrastructure from external threats.
Sketch a basic architecture diagram: Using a tool such as Google Drawings or Lucidchart, create a basic architecture diagram that shows the relationships between the components and services in your infrastructure. Start with a simple diagram and add more detail as needed.
Refine the architecture diagram: Once you have a basic diagram, refine it by adding more detail, such as the specific services or products you will use, the IP addresses of the components, and the ports they will use to communicate.
Review and optimize: Review your architecture diagram to ensure that it meets the requirements of your application, and optimize it as needed to improve performance, reliability, and security.

Remember that this is just a general guide, and the specific steps you take will depend on your unique requirements and goals.

2. Hosting domain for static website

To start hosting a website with a domain on Google Cloud using Google Cloud Storage, you can follow these steps:

Sign up for a Google Cloud account: If you don’t already have a Google Cloud account, sign up for one at https://cloud.google.com/.
Create a new project: Once you are logged in to your Google Cloud account, create a new project by clicking on the “Select a project” drop-down menu and then clicking on “New Project.” Follow the prompts to set up your project.
Enable billing: To use Google Cloud, you must enable billing. You can set up billing by navigating to the “Billing” tab in the Google Cloud console and following the prompts.
Create a Cloud Storage bucket: In the Google Cloud console, navigate to the “Storage” section and create a new Cloud Storage bucket. You can choose your region and other settings.
Set up a domain: You can buy a domain from a domain registrar and point it to your Cloud Storage bucket using a CNAME record.
Enable static website hosting: Enable static website hosting on your Cloud Storage bucket by navigating to the “Static website hosting” section in the Google Cloud console and entering your default index page and 404 page.
Upload your website files: Upload your website files to your Cloud Storage bucket using the Google Cloud Console, Google Cloud SDK, or an FTP client.
Test your website: Once your website files are uploaded, test your website to ensure it is working correctly.

3. Serverless Application on Google Cloud

To create a serverless application on Google Cloud, you can use Google Cloud Functions or Google Cloud Run. Both of these services allow you to run code without worrying about servers or infrastructure management. Here are the steps to create a serverless application using Google Cloud Functions or Google Cloud Run:

Using Google Cloud Functions:

Sign up for a Google Cloud account: If you don’t already have a Google Cloud account, sign up for one at https://cloud.google.com/.
Create a new project: Once you are logged in to your Google Cloud account, create a new project by clicking on the “Select a project” drop-down menu and then clicking on “New Project.” Follow the prompts to set up your project.
Create a Cloud Function: In the Google Cloud console, navigate to the “Cloud Functions” section and create a new function. You can choose your trigger, runtime, and other settings.
Write your code: Write the code for your serverless application in the Cloud Function editor or upload your code as a zip file.
Test your application: Once your code is uploaded, test your application to ensure it is working correctly.

Using Google Cloud Run:

Sign up for a Google Cloud account: If you don’t already have a Google Cloud account, sign up for one at https://cloud.google.com/.
Create a new project: Once you are logged in to your Google Cloud account, create a new project by clicking on the “Select a project” drop-down menu and then clicking on “New Project.” Follow the prompts to set up your project.
Build a container image: Build a container image for your application using Docker or another containerization tool.
Deploy your container image: Deploy your container image to Google Cloud Run and configure your settings such as authentication, scaling, and environment variables.
Test your application: Once your container image is deployed, test your application to ensure it is working correctly.

These are the basic steps to get started with creating a serverless application on Google Cloud using Google Cloud Functions or Google Cloud Run. You can find more detailed instructions and tutorials in the Google Cloud documentation.

4. Microservices on Google Cloud

Here are the high-level steps to set up microservices on Google Cloud using Google Kubernetes Engine (GKE) as the container orchestration platform:

Define your microservices architecture: Define the architecture of your microservices, including the services, APIs, and communication between them.
Containerize your microservices: Containerize each microservice using Docker, and push the container images to a container registry like Google Container Registry.
Create a Kubernetes cluster: Create a Kubernetes cluster on Google Kubernetes Engine (GKE) using the Google Cloud Console or the gcloud command-line tool.
Create Kubernetes deployment and service manifests: Create Kubernetes deployment and service manifests for each microservice, which specify the desired state of the application and how to access it.
Deploy your microservices: Deploy your microservices to your Kubernetes cluster using the kubectl command-line tool or the Google Cloud Console.
Configure your microservices: Configure your microservices with appropriate environment variables and other settings.
Monitor and scale your microservices: Use tools like Stackdriver and Kubernetes Horizontal Pod Autoscaler (HPA) to monitor and scale your microservices as needed.
Implement security measures: Implement security measures like authentication, authorization, and encryption to ensure the security of your microservices.

Here are some more details about each step:

Define your microservices architecture: Define the architecture of your microservices, including the services, APIs, and communication between them. You can use tools like Google Cloud’s API Gateway and Service Mesh to help manage communication between microservices.
Containerize your microservices: Containerize each microservice using Docker, and push the container images to a container registry like Google Container Registry.
Create a Kubernetes cluster: Create a Kubernetes cluster on Google Kubernetes Engine (GKE) using the Google Cloud Console or the gcloud command-line tool.
Create Kubernetes deployment and service manifests: Create Kubernetes deployment and service manifests for each microservice, which specify the desired state of the application and how to access it. You can use tools like Helm to simplify the creation and management of Kubernetes manifests.
Deploy your microservices: Deploy your microservices to your Kubernetes cluster using the kubectl command-line tool or the Google Cloud Console.
Configure your microservices: Configure your microservices with appropriate environment variables and other settings, such as database credentials and API keys.
Monitor and scale your microservices: Use tools like Stackdriver and Kubernetes Horizontal Pod Autoscaler (HPA) to monitor and scale your microservices as needed. These tools allow you to set up automatic scaling based on metrics like CPU usage or incoming requests.
Implement security measures: Implement security measures like authentication, authorization, and encryption to ensure the security of your microservices. You can use tools like Google Cloud’s Identity Platform and Key Management Service to help manage authentication and encryption.

These are the basic steps to set up microservices on Google Cloud using Google Kubernetes Engine (GKE). You can find more detailed instructions and tutorials in the Google Cloud documentation.

5. Machine Learning on Google Cloud

Here are the high-level steps to set up machine learning on Google Cloud:

Choose a machine learning framework: Choose a machine learning framework like TensorFlow or PyTorch that suits your needs.
Create a machine learning model: Create a machine learning model using your chosen framework.
Train your machine learning model: Train your machine learning model using data that you have prepared.
Store your trained machine learning model: Store your trained machine learning model in a storage service like Google Cloud Storage or Google Cloud ML Engine.
Deploy your machine learning model: Deploy your trained machine learning model to a serving service like Google Cloud ML Engine or Google Kubernetes Engine.
Test your machine learning model: Test your deployed machine learning model to ensure it is performing as expected.
Monitor your machine learning model: Monitor your machine learning model to ensure it continues to perform as expected.
Update your machine learning model: Update your machine learning model as needed to improve performance or address changing business needs.

Here are some more details about each step:

Choose a machine learning framework: Choose a machine learning framework like TensorFlow or PyTorch that suits your needs. You can also use AutoML to automatically generate machine learning models without requiring expertise in machine learning.
Create a machine learning model: Create a machine learning model using your chosen framework. This typically involves defining the model architecture and writing code to implement it.
Train your machine learning model: Train your machine learning model using data that you have prepared. You can use tools like Google Cloud AI Platform to manage and scale your training jobs.
Store your trained machine learning model: Store your trained machine learning model in a storage service like Google Cloud Storage or Google Cloud ML Engine. This allows you to easily access and deploy the model later.
Deploy your machine learning model: Deploy your trained machine learning model to a serving service like Google Cloud ML Engine or Google Kubernetes Engine. This allows you to serve predictions from the model to end-users or other systems.
Test your machine learning model: Test your deployed machine learning model to ensure it is performing as expected. You can use tools like Google Cloud’s AI Platform Prediction to test your model with simulated requests.
Monitor your machine learning model: Monitor your machine learning model to ensure it continues to perform as expected. This can involve setting up alerts for when the model’s performance deviates from expected metrics.
Update your machine learning model: Update your machine learning model as needed to improve performance or address changing business needs. This typically involves retraining the model with new data or adjusting the model architecture.

These are the basic steps to set up machine learning on Google Cloud. You can find more detailed instructions and tutorials in the Google Cloud documentation.

6. End-to-End Google Cloud DevOps Workflow

An end-to-end Google Cloud DevOps Workflow is a continuous integration/continuous deployment (CI/CD) pipeline that automates the development, testing, and deployment processes for applications running on the Google Cloud Platform (GCP). The goal of an end-to-end DevOps Workflow is to enable developers to rapidly and reliably deliver high-quality code to production by streamlining the development process and automating testing, deployment, and monitoring.

By implementing an end-to-end Google Cloud DevOps Workflow, teams can accelerate the development process, reduce the time to market, and improve the quality of the software. The key to a successful DevOps Workflow is automation and integration, using tools like Google Cloud Build, Google Cloud Test, Google Kubernetes Engine, Google Cloud Run, and Google Stackdriver to streamline the development process and improve the application’s performance.

7. Data Science on Google Cloud

Google Cloud provides a range of services and tools that can be used for data science, including data storage, processing, analysis, and machine learning. Here are the key steps for setting up a data science project on Google Cloud:

Data Storage: First, you need to store your data on Google Cloud. You can use a storage service like Google Cloud Storage or Google Bigtable to store structured or unstructured data.
Data Processing: Once you have your data stored on Google Cloud, you can use a processing service like Google Cloud Dataflow, Google Cloud Dataproc, or Google Cloud Composer to process the data and prepare it for analysis.
Data Analysis: For data analysis, you can use a range of tools like Google BigQuery, which is a serverless, highly scalable, and cost-effective data warehouse that lets you run SQL-like queries on your data. Alternatively, you can use Google Cloud Datalab, which is a Jupyter notebook environment that makes it easy to analyze and visualize data using Python, SQL, and machine learning libraries.
Machine Learning: Google Cloud provides a range of machine learning tools and services, including Google Cloud AutoML, Google Cloud AI Platform, and TensorFlow. You can use these tools to build and train custom machine learning models that can help you gain insights and make predictions from your data.
Deployment: Once you have developed your machine learning model, you can deploy it to production using Google Cloud AI Platform. You can also use Google Kubernetes Engine to deploy your model as a containerized application.
Monitoring and Optimization: To monitor and optimize your data science project on Google Cloud, you can use services like Google Cloud Monitoring and Google Cloud Trace to monitor your application’s performance, troubleshoot issues, and optimize your application’s performance.

Overall, Google Cloud provides a range of services and tools that can be used to set up and run data science projects, from data storage and processing to analysis and machine learning. By using these tools and services, you can gain valuable insights from your data, improve your decision-making, and drive business growth.

8. Data Analytics Pipeline on Google Could

A data analytics pipeline on Google Cloud typically consists of several steps that involve data processing, analysis, and visualization. Here are the key components of a typical data analytics pipeline on Google Cloud:

Data Ingestion: The first step in any data analytics pipeline is to ingest the data. Google Cloud provides several services for data ingestion, including Google Cloud Storage, Google Cloud Pub/Sub, and Google Cloud IoT Core.
Data Processing: Once the data is ingested, it needs to be processed before it can be analyzed. Google Cloud provides several services for data processing, including Google Cloud Dataflow, Google Cloud Dataproc, and Google Cloud Composer.
Data Storage: The processed data needs to be stored in a format that can be easily accessed and analyzed. Google Cloud provides several services for data storage, including Google Cloud Bigtable, Google Cloud SQL, and Google Cloud Spanner.
Data Analysis: The processed data can be analyzed using various tools, including Google BigQuery, which is a serverless, highly scalable, and cost-effective data warehouse that lets you run SQL-like queries on your data. Alternatively, you can use Google Cloud Datalab, which is a Jupyter notebook environment that makes it easy to analyze and visualize data using Python, SQL, and machine learning libraries.
Data Visualization: Once the data has been analyzed, it can be visualized using tools like Google Data Studio, which is a data visualization platform that lets you create customizable dashboards and reports.
Machine Learning: If you want to apply machine learning to your data analytics pipeline, Google Cloud provides several services for machine learning, including Google Cloud AutoML, Google Cloud AI Platform, and TensorFlow.
Deployment: Once your data analytics pipeline is complete, you can deploy it to production using Google Kubernetes Engine.

Overall, a data analytics pipeline on Google Cloud involves several components that work together to ingest, process, store, analyze, and visualize data. By using Google Cloud services and tools, you can create a powerful and scalable data analytics pipeline that can help you gain valuable insights from your data and make better business decisions.

9. Build, Deploy or Manage ML Models (AutoML)

Google Cloud offers a range of services that enable businesses and developers to build, deploy, and manage machine learning (ML) models. Here are some of the key services offered by Google Cloud for ML:

Google Cloud AI Platform: This is a fully-managed service that allows you to build, train, and deploy ML models at scale. It provides a flexible environment for building and testing models, along with pre-built algorithms and model frameworks to get started quickly.
TensorFlow: This is an open-source ML framework that is widely used by developers and data scientists to build and train ML models. Google Cloud provides a hosted version of TensorFlow, called TensorFlow Enterprise, that is optimized for enterprise use cases.
Cloud AutoML: This is a suite of services that allows you to build custom ML models without requiring deep expertise in ML. Cloud AutoML provides pre-built models for image and text classification, as well as tools for creating your own custom models.
Cloud TPU: This is a custom-built hardware accelerator that is optimized for training ML models. Google Cloud provides access to Cloud TPUs through Google Compute Engine, allowing you to train your models faster and more efficiently.
Kubeflow: This is an open-source platform for building and deploying ML workflows on Kubernetes. With Kubeflow, you can easily create, deploy, and manage ML models at scale.

Overall, Google Cloud provides a range of tools and services that enable businesses and developers to build and deploy powerful ML models quickly and easily.

10. Batch ETL Pipeline

Google Cloud provides a variety of services and tools that can be used to build a batch ETL pipeline. Here are some of the key services you can use to set up a batch ETL pipeline on Google Cloud:

Google Cloud Storage: This is a highly scalable object storage service that can be used to store raw data for processing. You can store data in Google Cloud Storage in a variety of formats, including CSV, JSON, and Avro.
Cloud Dataproc: This is a managed service that allows you to run Apache Hadoop, Apache Spark, and other big data frameworks on Google Cloud. With Cloud Dataproc, you can spin up a cluster of virtual machines to process your data using Spark or other ETL tools.
Cloud Dataflow: This is a fully managed service for building batch and streaming data pipelines. With Dataflow, you can easily build ETL pipelines using a variety of data sources and destinations, including Google Cloud Storage, BigQuery, and more.
Cloud Composer: This is a managed service for building and managing workflows on Google Cloud. With Cloud Composer, you can create complex ETL workflows that orchestrate multiple services, including Dataproc, Dataflow, and other services.
BigQuery: This is a fully managed, serverless data warehouse service that allows you to analyze data using SQL. You can load data from Cloud Storage, Cloud Dataproc, or Cloud Dataflow into BigQuery for analysis.

By combining these services and tools, you can build a scalable and efficient batch ETL pipeline on Google Cloud. You can use Cloud Storage to store raw data, Cloud Dataproc or Dataflow to process the data, and BigQuery to store and analyze the processed data. You can use Cloud Composer to orchestrate the ETL workflow and automate the process.

11. Cloud Armor on Google Cloud

Cloud Armor on Google Cloud by following these steps:

Create a Cloud Armor policy: Go to the Google Cloud Console and create a new Cloud Armor policy. A policy consists of one or more rules that define what traffic to allow or block.
Define your security policies: Define your security policies based on your application requirements. For example, you might want to block all traffic from a certain IP range or allow only HTTPS traffic.
Configure the policy: Configure the policy to specify which backend services or load balancers the policy applies to. You can also specify the action to take when a request matches a rule, such as allowing or blocking the traffic.
Attach the policy: Attach the policy to your load balancer or backend service. Once attached, the policy will be applied to all incoming traffic to your service.
Monitor and refine your policy: Monitor your Cloud Armor policy to ensure that it is effectively blocking unwanted traffic and allowing legitimate traffic. Refine the policy over time as you gather more information about your traffic patterns.

In addition to these steps, you can also use Cloud Armor to protect your Google Cloud Platform resources from DDoS attacks. You can set up rules to detect and block traffic from known malicious IP addresses or configure rate limiting to prevent overwhelming traffic. By using Cloud Armor, you can help ensure that your applications and services are secure and protected against unwanted traffic

12. DR set up Cloud SQL on Google Cloud

To set up Disaster Recovery (DR) for Cloud SQL on Google Cloud, you can follow these general steps:

Create a primary Cloud SQL instance: Create a primary Cloud SQL instance in your preferred region. This will be your primary instance that you will use for production data.
Enable automatic backups: Enable automatic backups for your primary Cloud SQL instance. This will ensure that your data is backed up on a regular basis, allowing you to restore data in case of a disaster.
Create a secondary Cloud SQL instance: Create a secondary Cloud SQL instance in a different region than your primary instance. This will be your DR instance, where you can restore your data if your primary instance becomes unavailable.
Set up replication: Set up replication between your primary and secondary instances using either synchronous or asynchronous replication. This will ensure that your DR instance always has a copy of your production data.
Test your DR plan: Test your DR plan by performing failover from your primary instance to your DR instance. This will test if your DR plan is working as expected.
Monitor your DR setup: Monitor your DR setup to ensure that replication is working properly and that your DR instance is up-to-date with your primary instance.
Update your DR plan: Update your DR plan as needed based on changes to your application or data.

These are the general steps you can follow to set up DR for Cloud SQL on Google Cloud. Keep in mind that the specific steps may vary depending on your specific requirements and use case. Additionally, Google Cloud provides tools like Cloud SQL automated failover, which can automatically failover to the DR instance in case of a disaster.

13. Application on Containers in Google Cloud

To run an application on containers in Google Cloud, you can follow these general steps:

Create a Docker image: You need to create a Docker image of your application. A Docker image is a lightweight, standalone, and executable package that includes all the dependencies and configurations required to run your application.
Store your Docker image: You can store your Docker image in a container registry like Google Container Registry (GCR). GCR provides a secure and scalable way to store, manage, and deploy your Docker images.
Create a Kubernetes cluster: Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. You can create a Kubernetes cluster on Google Cloud using Google Kubernetes Engine (GKE).
Deploy your Docker image: You can deploy your Docker image to your Kubernetes cluster by creating a Kubernetes deployment. A deployment is a Kubernetes resource that manages a set of replicas of your application.
Expose your application: You need to expose your application to the outside world by creating a Kubernetes service. A service is a Kubernetes resource that exposes your application to the network, allowing other services or users to access it.
Monitor and scale your application: You can use Kubernetes to monitor and scale your application based on your application’s resource usage and traffic patterns.

These are the general steps you can follow to run an application on containers in Google Cloud using Kubernetes. Keep in mind that the specific steps may vary depending on your specific requirements and use case. Additionally, Google Cloud provides other container orchestration tools like Cloud Run and App Engine, which allow you to run your application on containers without managing the underlying infrastructure.

14. Sample Gaming Backend Databases Architecture

A sample gaming backend database architecture typically involves multiple databases and services working together to provide a reliable and scalable platform for the game. Here’s an example of a typical architecture:

User database: This database stores user account information, such as usernames, passwords, and email addresses.
Game data database: This database stores game data, such as player scores, game progress, and achievements.
Matchmaking database: This database stores information about available game matches and player preferences.
Real-time game data database: This database stores real-time game data, such as player positions and game events.
Analytics database: This database collects and stores game usage and performance data for analysis and reporting.
Caching layer: This layer provides a caching mechanism to improve performance by reducing the number of requests to the database.
API layer: This layer exposes the game functionality to clients through an API, which can be consumed by mobile apps, websites, and other game clients.
Load balancer: This layer distributes incoming requests to multiple servers to ensure high availability and scalability.
Game servers: These servers run the game logic and communicate with the databases and other services to provide a seamless gaming experience.
Monitoring and logging: This layer monitors the system’s health, logs events and errors, and provides alerts and notifications to the operations team when needed.

The above architecture is just an example, and the specific architecture may vary depending on the specific requirements of the game. Additionally, cloud-based services like Google Cloud provide managed database services like Cloud SQL and Firestore that can simplify the database management and scalability aspects of the gaming backend

15. Sample Serverless Microservices with Cloud-run E-commerce architecture

Here’s a sample serverless microservices e-commerce architecture using Google Cloud Run:

Front-end application: This is the user-facing part of the e-commerce website, built using a modern JavaScript framework such as React, Angular, or Vue.js.
API gateway: This layer serves as a single entry point for all incoming API requests from the front-end application. This can be implemented using an API management platform like Apigee or a serverless framework like Cloud Endpoints.
Authentication and authorization: This layer handles user authentication and authorization, ensuring that only authorized users can access the protected resources. This can be implemented using Google Cloud IAM or a third-party identity provider like Okta or Auth0.
Shopping cart service: This microservice manages the user’s shopping cart and provides APIs for adding, updating, and removing items from the cart. This can be implemented using Cloud Run or Google Kubernetes Engine (GKE).
Product catalog service: This microservice manages the e-commerce product catalog and provides APIs for browsing and searching products. This can be implemented using Cloud Run or GKE.
Order management service: This microservice handles the user’s order placement, processing, and fulfillment. This can be implemented using Cloud Run or GKE.
Payment service: This microservice handles the user’s payment processing and provides APIs for accepting payments from different payment gateways. This can be implemented using a third-party payment provider like Stripe or Braintree.
Shipping service: This microservice manages the user’s shipping information and provides APIs for tracking the order delivery. This can be implemented using Cloud Run or GKE.
Data store: This layer provides a persistent storage solution for storing the e-commerce data, such as product information, user profiles, and order history. This can be implemented using Google Cloud Firestore, Cloud SQL, or a NoSQL database like MongoDB or Cassandra.
Monitoring and logging: This layer monitors the system’s health, logs events and errors, and provides alerts and notifications to the operations team when needed. This can be implemented using Google Cloud Monitoring, Logging, and Error Reporting.

The above architecture is just an example, and the specific architecture may vary depending on the specific requirements of the e-commerce website. Google Cloud provides various managed services like Cloud Run, Cloud Functions, and Firebase that can simplify the serverless microservices architecture design and deployment process

16. Storage Event Function App

17 . Application on Virtual Machines in Google Cloud

Running applications on Virtual Machines (VMs) in Google Cloud is a common way to host and manage applications in the cloud. Here are the general steps to create and run an application on Virtual Machines in Google Cloud:

Create a Virtual Machine instance: In the GCP Console, navigate to the Compute Engine page and click “Create Instance”. Choose the instance type, operating system, and other settings for your VM, and then create the instance.
Install your application: Once your VM is created, log in to the instance and install your application software. This could involve downloading and installing dependencies, configuring the application settings, and other tasks specific to your application.
Configure networking and security: Configure networking and security settings to allow external users to access your application. This might involve setting up firewall rules, configuring load balancing, and other settings depending on your application’s requirements.
Test and deploy: Test your application to ensure that it works correctly on the VM. Once you have tested the application, you can deploy it to the VM, making it available for users to access.
Monitor and manage: Use GCP’s monitoring and logging tools to monitor your application’s performance and troubleshoot any issues. You can also use GCP’s management tools to scale your application up or down as needed, or to update its code or configuration.

Note that there are several options for running applications on VMs in GCP, including using pre-configured VM images, containerizing your application with Google Kubernetes Engine (GKE), or using other tools and services provided by GCP. The specific steps you take will depend on your application’s requirements and the approach you choose.

18 . Three-tier App

A three-tier app architecture is a common way to design scalable and reliable applications in the cloud. In Google Cloud, you can implement a three-tier app using a combination of Compute Engine, Kubernetes Engine, and Cloud SQL. Here are the general steps to deploy a three-tier app on GCP:

Create a Compute Engine VM for the web tier: Create a VM instance to host your web server, which will serve as the front-end of your application. You can install your web server software and configure it to communicate with your application server.
Create a Kubernetes Engine cluster for the application tier: Create a Kubernetes cluster to host your application servers, which will handle the business logic and data processing of your application. You can containerize your application using Docker and deploy it on the Kubernetes cluster.
Create a Cloud SQL instance for the data tier: Create a Cloud SQL instance to host your database, which will store the data for your application. You can configure the database to be highly available and scalable as needed.
Configure networking and security: Configure networking and security settings to allow external users to access your application. This might involve setting up firewall rules, configuring load balancing, and other settings depending on your application’s requirements.
Test and deploy: Test your application to ensure that it works correctly on the three-tier architecture. Once you have tested the application, you can deploy it to the three tiers, making it available for users to access.
Monitor and manage: Use GCP’s monitoring and logging tools to monitor your application’s performance and troubleshoot any issues. You can also use GCP’s management tools to scale your application up or down as needed, or to update its code or configuration.

Overall, a three-tier app architecture on GCP provides a scalable and reliable solution for hosting and managing your applications in the cloud. By leveraging GCP’s infrastructure and services, you can focus on developing and deploying your application without worrying about the underlying infrastructure.

19. Computing resources with Load Balancer

In Google Cloud, you can use a Load Balancer to distribute traffic across multiple computing resources, providing high availability and scalability for your application. Here are the general steps to use a Load Balancer to manage your computing resources:

Create your computing resources: Create one or more instances of your computing resources, such as Compute Engine VMs or Kubernetes Engine clusters. Ensure that your computing resources are configured to run your application and are accessible on your network.
Configure your Load Balancer: Create a Load Balancer that will distribute traffic across your computing resources. Choose the appropriate type of Load Balancer for your application, such as HTTP(S), TCP/UDP, or Internal Load Balancing. Configure the Load Balancer’s settings, including the target pool, health checks, and session affinity.
Test your Load Balancer: Test your Load Balancer to ensure that it’s distributing traffic correctly to your computing resources. You can use GCP’s monitoring tools to track the Load Balancer’s performance and detect any issues.
Scale your computing resources: As traffic to your application increases, you may need to scale your computing resources to handle the load. You can use GCP’s autoscaling features to automatically add or remove instances of your computing resources based on demand.
Monitor and manage: Use GCP’s monitoring and logging tools to monitor your application’s performance and troubleshoot any issues. You can also use GCP’s management tools to update your application’s code or configuration, or to perform other management tasks.

Overall, using a Load Balancer to manage your computing resources in Google Cloud provides a highly available and scalable solution for hosting and managing your application. By distributing traffic across multiple instances of your computing resources, you can ensure that your application remains available and responsive to users, even during peak usage periods.

20. Billing management GCP Architecture

Labeling resources in Google Cloud Platform (GCP) can help you manage costs more effectively. Here’s how you can use labels to manage costs and set up cost alerts in GCP architecture:

Label your resources: Assign labels to your GCP resources, such as instances, disks, and buckets, to categorize them by project, department, environment, or any other relevant attribute. You can apply labels manually or programmatically using the GCP APIs.
Create labels-based budgets: In the GCP Billing dashboard, you can create budgets based on labels. This allows you to track spending for specific categories of resources, such as all instances labeled “prod”. You can also set alerts based on these budgets to receive notifications when spending exceeds a certain threshold.
Use the GCP Pricing Calculator: The GCP Pricing Calculator allows you to estimate the cost of your resources based on their usage and pricing model. You can also factor in labels to get a more accurate estimate of your costs.
Monitor and optimize your costs: Use GCP’s cost management tools, such as Cost Explorer and Billing Reports, to monitor your spending and identify areas where you can optimize costs. Use labels to drill down into specific categories of resources and identify areas where you can reduce costs.
Use third-party cost management tools: Consider using third-party tools that integrate with GCP to help you manage your costs and set up alerts based on labels. Tools such as CloudHealth and Cloudability can help you monitor your spending and optimize your costs.

By using labels and cost management tools in combination, you can gain greater visibility into your costs and proactively manage your budget. With labels-based budgets and alerts, you can track spending by specific categories of resources and receive notifications when spending exceeds a certain threshold. By continuously monitoring and optimizing your costs, you can ensure that you are getting the most value out of your GCP investment.

About Me:

I am having experienced IT professional with a passion for helping businesses embark on their journey to the cloud. With over 14+ years of industry experience, I currently serve as a Google Cloud Principal architect, specializing in assisting customers in building highly scalable and efficient solutions on the Google Cloud Platform. My expertise lies in infrastructure and zero trust security, google cloud networking, and cloud infrastructure building using Terraform. I hold several prestigious certifications, including Google Cloud Certified, HashiCorp Certified, Microsoft Azure Certified, and Amazon AWS Certified.

Certificated :

Google Cloud Certified — Cloud Digital Leader.
2. Google Cloud Certified — Associate Cloud Engineer.
3. Google Cloud Certified — Professional Cloud Architect.
4. Google Cloud Certified — Professional Data Engineer.
5. Google Cloud Certified — Professional Cloud Network Engineer.
6. Google Cloud Certified — Professional Cloud Developer Engineer.
7. Google Cloud Certified — Professional Cloud DevOps Engineer.
8. Google Cloud Certified — Professional Security Engineer.
9. Google Cloud Certified — Professional Database Engineer.
10. Google Cloud Certified — Professional Workspace Administrator.
11. Google Cloud Certified — Professional Machine Learning.
12. HashiCorp Certified — Terraform Associate
13. Microsoft Azure AZ-900 Certified
14. Amazon AWS-Practitioner Certified

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and DM me,:) I am happy to help!!