Google Cloud Platform Essentials

Explore the four key GCP compute services—Compute Engine, Kubernetes Engine, App Engine, and Cloud Functions—and compare their roles from highly customizable to highly managed, including serverless options.

Explore Google Compute Engine, an infrastructure as a service that lets you configure and deploy virtual machines with flexible resources, persistent storage, and scalable instance groups.

Configure a google cloud platform compute engine instance, install node.js and git, deploy a hello world node.js web server, and access it via the external ip.

Explore Google Kubernetes Engine for deploying and managing containerized applications with clusters, nodes, pods, and container registry on Google Cloud, and learn container versus VM differences.

Create a GKE cluster of three nodes with auto-scaling, containerize and publish hello world Node.js app to Container Registry, then expose it with a load balancer and verify with kubectl.

Explore Google App Engine, a fully managed serverless platform, and compare the Standard Environment with the Flexible Environment to determine which suits your app's scaling and runtime needs.

Learn to deploy and run a Node.js web app on App Engine's standard environment, using setup, three deployment methods, cloud shell, and gcloud deploy with app.yaml and dependencies.

Explore Google Cloud Functions, a serverless, event-driven platform that runs code on demand and scales automatically. Create and test functions, configure http and background triggers, and connect to cloud services.

Explore the key cloud storage and database services, including Google Cloud Storage, persistent disk, Filestore, Cloud SQL, and Cloud Bigtable, and learn object, block, and file storage types.

Learn how Google Cloud Storage provides a unified object storage API via rest to store and serve assets, with buckets, objects, access control, encryption, versioning, and storage classes.

Create and interact with a Google Cloud Storage bucket, manage object ACLs and bucket IAM permissions, upload files, and share access using the web console.

Explore Google Persistent Disk, a durable block storage for Compute Engine and Kubernetes Engine with up to 64 terabytes, snapshots, and Google Filestore, managed network file storage for shared data.

Create and attach a persistent disk to a GCE VM and mount it. Then create a Filestore instance and mount it via NFS on the VM.

Explore why moving databases to the cloud benefits businesses and how Google Cloud SQL provides a managed, scalable, and secure relational database service for MySQL, Postgre, and Microsoft SQL Server.

Demonstrates how to create a MySQL Cloud SQL instance, secure it, and connect to load and query sample data using Cloud Shell and MySQL commands.

Explore Google Cloud Bigtable, a petabyte-scale, low-latency storage system with a sparse, distributed, persistent multidimensional sorted map of rows, column families, and timestamps, aligned with cluster and tablet architecture.

Learn to work with Google Cloud Bigtable by creating an instance and clusters, selecting storage type, configuring replication and application profiles, and deploying data with the Google Cloud SDK.

Explore the key GCP networking services, including Google Cloud Load Balancer, Google Cloud DNS, Google Cloud CDN, Google Cloud NAT, and Traffic Director, with tier options and cross-VM connectivity.

Explore how load balancers distribute client requests across servers, ensuring high availability. Learn about Google Cloud Load Balancer categories—external vs internal, global vs regional—and its key features.

Learn how to implement an HTTP(S) load balancer across two virtual machines, compare it with a network load balancer, and configure backend, health checks, and frontend settings.

Learn Google Cloud DNS, a high-performance global DNS service that publishes domain names, manages zones and records, and explains authoritative and recursive servers and public and private zones.

Expose a web page on Google Cloud DNS by registering a free domain, deploying a web app on a VM with a static IP, and mapping it to the domain.

Discover how Google Cloud CDN accelerates content delivery by caching at the edge, using external HTTP(S) load balancing, and managing cache hits and misses.

Configure a Cloud Storage bucket as the backend, enable Cloud CDN on an HTTP load balancer, and verify the caching mechanism by timing consecutive requests to observe edge caching.

Explore Google Cloud NAT and Cloud Traffic Director to enable secure outbound internet access for private VMs without external IP addresses, using software-defined networking and service mesh concepts.

Explore how GCP secures cloud applications across six-layer infrastructure and learn services such as Google Cloud identity and access management, ssl policies, Google Cloud Armor, and Google Cloud Security Scanner.

Learn Google Cloud IAM, covering identities, resources, permissions, and roles; understand policies, least privilege, and hierarchy for managing access across projects.

Learn how Google Cloud implements SSL policies for load balancers, manages certificates, configures TLS 1.2 and the compatible profile, and secures HTTPS endpoints.

Discover the key GCP data integration and analytics services, from Pub/Sub ingestion and Dataflow processing to BigQuery storage and Data Studio visualization, enabling real-time, serverless data pipelines.

Explore Google Cloud Pub/Sub, a fully managed real-time messaging service that uses the publish-subscribe model with topics and subscriptions, enabling asynchronous, scalable communication between apps.

Discover google cloud dataflow, a fully managed, serverless service for real-time and batch data processing. Build pipelines with Apache Beam, scale automatically, and integrate with Cloud Pub/Sub and BigQuery.

Explore how Google Cloud BigQuery acts as a fully managed, serverless data warehouse for storage, ingestion, and querying, enabling secure collaboration via IAM on public datasets like covid19_ecdc using SQL.

Discover how Google Data Studio visualizes data on Google's data stack, building customizable reports and dashboards with datasets, data sources, reports, and connectors that enable sharing without importing data.

Integrate Pub/Sub, Dataflow, BigQuery, and Data Studio in one project, enabling APIs, creating storage buckets and datasets, and running a Pub/Sub to BigQuery data pipeline.

Explore the key GCP management tools and monitoring services, including Google Cloud Deployment Manager, Google Cloud Operations, Cloud Logging, Cloud Monitoring, Cloud Debugger, Cloud Trace, and Cloud Profiler.

Learn how Google Cloud Deployment Manager enables infrastructure as code by using YAML configurations and templates (Python or Jinja) to deploy a Compute Engine instance and monitor deployment status.

Learn how Google Cloud Monitoring collects metrics in workspaces with access control to dashboards and alerts, and Cloud Logging stores, queries, and analyzes log entries across Google Cloud and AWS.

Explore monitoring and logging services for a cloud functions project, deploy a simple app with cloud functions, run load testing with Vegeta, and review execution logs and metrics.

Explore Google Cloud Trace and Google Cloud Debugger to measure latency with traces and spans, view the web UI, analyze traces, and inspect app state without slowing the running app.

Explore Google Cloud Error Reporting and Google Cloud Profiler to identify production issues, group errors by stack traces and frameworks, and profile performance with CPU time, wall time, and heap metrics.

Explore Google Cloud AI building blocks, including AutoML services and pre-trained models, and use AI Hub to train custom models or deploy APIs for vision, language, video, and structured data.

Explore what IoT is and how Google Cloud IoT Core connects devices via the device manager and protocol bridges, enabling real-time telemetry and cloud-based analytics.

Plan and execute cloud migration with Google Cloud tools. Learn the four-phase process—assess, plan, deploy, optimize—and use Transfer Appliance, Migrate for Compute Engine, and BigQuery Data Transfer Service.

Explore building a technical support web portal on Google Compute Engine, integrating persistence, Cloud DNS, load balancing, security services, and BigQuery analytics visualized in Data Studio.

Deploy a Node.js-based technical support web portal on a Compute Engine instance, capture employee id, laptop model, and issue type, storing data in Cloud SQL via Google Cloud SDK.

Learn to integrate persistence and cloud dns with a gce vm instance by configuring credentials, creating a datastore entity, registering a domain, and mapping dns records to the vm.

Configure an external load balancer across two virtual machine instances, set up the backend service and health checks, and integrate Cloud Armor and Cloud Security Scanner to secure the project.

Import datastore data into BigQuery via the managed import-export service and cloud storage, then visualize the results in Data Studio with configurable dashboards.