Explore cloud fundamentals, understand why startups and enterprises move away from data centers, and discover what cloud is and its advantages through a step-by-step visual approach with real enterprise scenarios.

Enterprises need thousands of servers to scale beyond single-server limits and prevent a single point of failure by using data centers for large, reliable operations.

Understand what a data center is and why managing it is costly and complex, including peak load provisioning, latency, and geographic expansion. See how cloud offers a scalable alternative.

Explore how cloud computing transforms infrastructure from building power plants to renting servers on demand, with elasticity and pay-as-you-go pricing from providers like AWS, Azure, and Google Cloud.

Discover the key advantages of the cloud, including pay-for-use economics, economies of scale, and elastic capacity. Learn how these benefits enable faster development, global deployment, and reduced undifferentiated heavy lifting.

Learn essential cloud terminology such as elasticity, agility, geo distribution, and availability. Understand latency, cost concepts like CapEx to OpEx, pay-as-you-go, economies of scale, and avoiding undifferentiated heavy lifting.

Analyze cloud concepts through scenarios, illustrating elasticity, pay-as-you-go costs, agility in provisioning, high availability, geo-distribution, low latency, economies of scale, and avoiding undifferentiated heavy lifting.

Explore how Google Cloud uses its private fiber network to deliver fast, secure data and leverage a service catalog across compute, storage, databases, analytics, AI, and ML.

Learn a structured, step-by-step path to mastering cloud services across AWS, Azure, and Google Cloud, and discover how a cloud account provides global access, a gateway, and free credits.

Create a Google Cloud account and start a free trial with $300 credit by providing your personal details, verifying your mobile number, and entering card and tax information.

Use playback speed to maximize learning efficiency in your GCP for beginners course, saving time while maintaining comprehension by starting at 1x and gradually increasing to 1.25x, 1.5x, or 2x.

Learn how regions and zones define cloud infrastructure, and how multi-region, multi-data-center deployments deliver low latency and high availability across the globe.

Choose cloud deployment regions by evaluating compliance, latency, service availability, and pricing. Ensure data stays local where required and services are available near your users.

Deploy your applications to multiple regions to reduce latency, expand your global footprint, meet data residency requirements, and improve availability and disaster recovery.

Learn how availability zones within a region, London, offer Zone A, B, and C, isolated yet connected by independent power and low latency links, to achieve high availability.

Explore how regions and zones enable disaster recovery, low latency, and high availability through multi-zone and multi-region deployments, and address common misconceptions.

Explore Google Cloud regions and zones with examples such as us-west-1 and us-west-1a, and learn the naming convention, plus deploying across multiple zones or regions for high availability.

Explore how to create virtual machines with Compute Engine, group them with instance groups, and balance traffic with Cloud Load Balancing while setting up a simple web app.

Explore Google Compute Engine to provision, manage, and scale virtual machines, attach storage, configure networking, and use load balancing and auto scaling for cloud deployments.

Create and manage virtual machine instances in Google Compute Engine, exploring the lifecycle and SSH access. Configure region, zone, HTTP firewall, and external IP, and review cost estimates.

Set up an Apache http server on a Google Compute Engine virtual machine by updating packages, installing Apache, and serving an index.html that echoes the host name and IP.

Explore internal versus external IP addresses for Google Compute Engine VMs. Understand how internal IPs stay fixed, external IPs are ephemeral, and how to reserve a static external IP.

Use a startup script to bootstrap a compute engine VM, automatically install Apache 2, enable http traffic, and run a simple page while applying the latest OS patches at boot.

Create an instance template to preset machine type, image, startup script, and firewall, then reuse it to deploy multiple vms; copy to create a version, since templates cannot be updated.

learn to speed up vm launch by creating a hardened custom image with preinstalled patches and software, then use instance templates instead of startup scripts.

Discover sustained use discounts, a reduction for running vm instances for a portion of month, starting after 25% usage, increasing with usage, applying to compute and kubernetes engine with exceptions.

Discover committed use discounts for Google Cloud virtual machines, offering up to 70% off for one- or three-year commitments on Compute Engine and GKE, which cannot be canceled.

Learn about preemptible vms, short-lived cheaper compute that can terminate within 24 hours, ideal for non-immediate batch workloads, with no sla, migration, restart, or free trial credits.

Discover spot vms as the latest version of preemptible vms, with no maximum run time, 30-second termination notices, dynamic pricing, and significant discounts on Google Cloud resources.

Learn how sole-tenant nodes provide dedicated hardware for your VMs, create node groups and templates, apply affinity labels, and manage licensing and costs with the pricing calculator.

Create custom machine types in Compute Engine by selecting the machine family and configuring vCPUs, memory, and optional GPUs when predefined VM options don’t fit your workload.

Discover the cost structure of Google Compute Engine, separating infrastructure costs from licensing costs for premium images, and learn pay-as-you-go licensing and bring your own license options.

Review how to choose VM images, including custom hardened images, configure machine types and static IPs, and apply discounts via instance templates and preemptible or committed use.

Explore Google Compute Engine scenarios to optimize virtual machines with custom images, custom machine types, static IPs, and preemptible VMs or spot VMs.

Explore instance groups that manage identical VMs via a template, with MIGs offering auto-scaling, auto-healing, and rolling updates. Unmanaged groups lack these features and are for different VM configurations only.

Configure a managed instance group using an instance template, set auto-scaling with min and max instances, metrics like CPU utilization or load balancer utilization, and health checks for auto-healing.

Manage a Google Cloud managed instance group with auto healing and health checks, keeping two healthy instances across zones while tuning minimum and maximum sizes for auto scaling.

Explore how cloud load balancing distributes traffic across VM instances in regions globally, with auto scaling, public or private options, and types like external HTTPS and internal HTTPS SSL proxy.

Create a Google Cloud load balancer to distribute traffic across a managed instance group. Configure a backend service, host and path rules, and a frontend for internet-facing http traffic.

Configure a Google Cloud load balancer to distribute traffic across a managed instance group using a backend service, monitor health, and verify via external IP checks.

Troubleshoot on your own to build resilience. Research with Google, consult ChatGPT, post details in Q&A when needed, give back, and take a break when stuck.

Explore how Google Cloud automates deployment with managed services, detailing IaaS, PaaS, and SaaS, and covering containers, container orchestration, and serverless for app deployment.

Explore managed services and how they replace manual setups, with overview of infrastructure as a service, platform as a service, function as a service, container as a service, and serverless.

Learn the differences between IaaS and PaaS, including who handles the operating system, runtime, deployment, and autoscaling, with examples like EC2, App Engine, and Google Cloud SQL.

Understand how containers enable microservices and portable deployments across cloud environments. Explore docker images, container runtimes, and orchestration features like auto scaling, service discovery, and zero downtime deployments.

Discover serverless computing, where you focus on code while cloud services handle infrastructure, scaling, and pay-per-invocation pricing, with examples like AWS Lambda, Azure Functions, and Google Cloud Functions.

Learn SaaS: cloud-based software on subscription, such as Gmail, Google Docs, and Outlook 365, where users configure the app while providers handle hardware, OS, and scaling.

Explore the shared responsibility model in Google Cloud and how duties split between Google and customers across IaaS, PaaS, and SaaS. Identify responsibilities for deployment, access control, and data security.

Get a 10,000-foot overview of GCP compute services: Compute Engine, Kubernetes Engine, App Engine, Cloud Functions, and Cloud Run, with insights on IaaS, PaaS, container as a service, and serverless.

Ranga Karanam introduces himself as the instructor for this course, founder of in28minutes, with Java, Python, JavaScript and cloud certifications on AWS, Azure, and Google Cloud.

Explore Google Cloud compute services, from App Engine for deploying Java and Python apps to container orchestration with Google Kubernetes Engine, and serverless options with Cloud Functions and Cloud Run.

Discover Google App Engine, the simplest way to deploy and scale apps, with automatic load balancing, versioning, and traffic splitting across languages.

Understand app engine environments: standard and flexible. Standard uses language sandboxes with v1/v2 and can scale to zero; flexible uses docker on compute engine vms and keeps one instance.

Create a new Google Cloud project and enable App Engine APIs. Configure a Python app with app.yaml and requirements.txt, then deploy via gcloud app deploy to App Engine Standard.

Learn how to navigate App Engine's UI, manage services and versions, and deploy new versions with traffic split using gcloud commands, while monitoring instances and URLs.

Explore IaaS, PaaS, and SaaS through real scenarios, from compute engines and virtual machines to App Engine and Gmail, clarifying OS updates, auto scaling, and shared availability responsibility.

Explore Google Kubernetes Engine (GKE) as a managed Kubernetes service for cluster management, upgrades, auto repair, and pod and cluster auto scaling with logs and monitoring.

Create a Kubernetes cluster with a default node pool, explore standard vs autopilot modes, and deploy a simple microservice to Kubernetes.

Connect to a Google Cloud Kubernetes cluster, get credentials, deploy a microservice with cube cutter, expose a deployment as a load balancer service, and verify the endpoint with curl.

Scale deployments with kubectl to set replicas, observe load balancing across pods, and resize node pools with gcloud container clusters resize, comparing manual pod scaling to later auto scaling.

Learn how to autoscale both your Kubernetes microservice and cluster using kubectl autoscale deployment and gcloud container clusters update, setting cpu targets and min/max pods and nodes.

Delete the service HelloWorldRest.api, then delete the deployment, and finally remove the cluster. List and set the project with gcloud, then delete the project to finish cleanup.

Learn how Google cloud functions enable event-driven code without managing servers, scale automatically, and pay only for invocations, with multi-language support and isolated, time-bound executions.

Create your first Google Cloud function in the Google Cloud console, choose first-gen vs second-gen, enable the HTTP trigger, deploy with Cloud Build, and monitor logs and performance.

Learn how to deploy containerized apps with Cloud Run, compare it to Kubernetes, configure autoscaling, traffic, and revisions, and extend to Anthos for multi-cloud and on-premise clusters.

Explore Google Cloud compute services, including Compute Engine, managed instance groups, load balancing, App Engine, Cloud Run, and Kubernetes Engine for container orchestration.

Set ambitious long-term goals to guide your learning path and short-term milestones toward becoming an expert across AWS, Azure, and Google Cloud.

Explore block storage, file storage, and object storage in google cloud, including persistent disks for virtual machines, file store for shared file storage, and cloud storage for scalable object storage.

Explore block storage, file storage, and cloud file store options in Google Cloud Platform. Determine how persistent disks, regional versus zonal replication, local SSDs, and boot disks attach to VMs.

Create Google Cloud storage bucket for object storage, choose region option (regional, dual region, or multi region), assign a unique name, select a storage class, and upload files as objects.

Explore cloud storage in GCP as serverless, auto-scaling object storage with infinite scale. Access via REST API and gsutil; manage whole objects and transfer on-prem data for backups.

Explore Google Cloud storage classes—standard, nearline, coldline, and archive—and learn how bucket defaults and per-object overrides optimize durability, availability, and cost for different access patterns.

Automate cost savings with object lifecycle management by configuring rules that move aged objects to colder storage or delete them, based on age, storage class, and newer versions.

Compare transfers using gsutil or cloud storage APIs with Storage Transfer Service for large or scheduled transfers from on-premises or AWS or Azure, and use Transfer Appliance for data transfers.

Explore storage scenarios in Google Cloud, including Filestore for shared media projects and cloud storage for global unstructured data, with object lifecycle management and transfer appliance for migrations.

Explore relational and noSQL databases in google cloud, including oltp and olap workloads, and learn how to choose among cloud sql, cloud spanner, bigquery, firestore, datastore, bigtable, and memorystore.

Explore database categories, including relational, document, key-value, graph, and in-memory. Learn OLTP and OLAP needs, schema choices, and transaction properties to select the right database.

Explore OLTP relational databases in Google Cloud, comparing Cloud SQL and Cloud Spanner for regional databases up to a few terabytes versus unlimited global scaling and 99.999% availability.

Learn to create a MySQL Cloud SQL instance, enable the Cloud SQL Admin API, connect from Cloud Shell, and build a to-dos database with a simple user table.

Explore Cloud SQL with a MySQL 5.7 instance, review monitoring metrics, backups and point in time recovery, and configure connectivity, storage and maintenance for high availability.

Enable Cloud Spanner API, create an instance with 100 to 1000 processing units, build a users table (user ID int64 not null, username string(1024)) and set primary key, run inserts.

Explore Google Cloud OLAP relational databases with BigQuery, a petabyte-scale data warehouse, and compare columnar storage to row storage for fast, distributed analytics.

Explore noSQL options in Google Cloud: Cloud Data Store and Cloud Fire Store for transactional mobile or web apps, and Cloud Big Table for petabyte-scale analytics with not serverless tradeoffs.

Explore Cloud Firestore in native mode, create collections and documents, and build a hierarchical data structure with to-dos and activities while learning indexing, querying, and import-export workflows.

Explore in-memory databases on Google Cloud, focusing on Memorystore with Redis and Memcached, to accelerate caching and session management by storing persistent data in memory for microsecond latency.

Explore relational OLTP databases like Cloud SQL and Cloud Spanner (with MySQL, PostgreSQL, SQL Server DBs), compare BigQuery, NoSQL Cloud Firestore and Cloud Bigtable, and Memory Store for microsecond responses.

Explore database scenarios in Google Cloud, including Cloud Datastore/Firestore for evolving schemas, Cloud Bigtable for fast growth and time series, Cloud Spanner for global transactions, and BigQuery for analytics.

Explore the modern security landscape, including ransomware, phishing, insider threats, malware and viruses, DDoS, data breaches, and cloud misconfigurations, with real-world attack scenarios and prevention insights.

Learn the fundamentals of Google Cloud IAM to authenticate users and authorize access for both people and applications across Google Cloud services.

Learn how Cloud IAM handles authentication and authorization, manage identities from users to groups and applications, and apply granular access to Google Cloud resources.

Discover how to grant a colleague access to manage a cloud storage bucket by identifying the member, resource, and action, then binding a suitable role via a policy.

Explore cloud IAM roles in Google Cloud, including basic roles (owner, editor, viewer), predefined roles, and custom roles, and learn how to assign permissions to different members.

Learn to manage Google Cloud identities with IAM v2, add members, assign roles like compute instance admin, and use policy troubleshooter to verify access within a project.

Understand service accounts as secure VM identities in GCP. Create and assign user-managed accounts to compute engines, grant roles like compute admin and storage object admin.

Create and configure a Compute Engine service account, grant Storage Admin, attach it to a VM, and use gsutil to create a Cloud Storage bucket.