
Explore Google Cloud Platform concepts, including infrastructure as a service and platform as a service, with compute engine and virtual machines. Learn provisioning, regions, zones, and security basics.
Explore Google Cloud Platform overview, including projects, billing accounts, resource scopes (global, regional, zonal), interfaces like console, cloud shell, gcloud, and APIs, plus budgeting and IAM.
Explore cloud IAM to control who can access what resources in Google Cloud, using users, service accounts, and roles across an organization hierarchy with organization policies and quotas.
Explore Google Cloud Compute Engine and Kubernetes Engine to Cloud Run and App Engine, with API delivery via API Gateway, Cloud Endpoints, and Apigee.
Explore Google Cloud Compute Engine as a pay-as-you-go virtual machine service, featuring scalable vCPU, memory, GPU options, encryption, networking, and autoscaling to fit workloads.
Explore how Kubernetes Engine on Google Cloud Platform orchestrates containerized workloads with pods, deployments, services, and namespaces, using master and worker architecture, API server, scheduler, and RBAC-based security.
Explore Google Cloud Platform storage and database services, including Cloud SQL, Cloud Spanner, Cloud Bigtable, Firestore, and Cloud Storage, with comparisons and use cases.
Cloud SQL provides managed MySQL, PostgreSQL, and SQL Server for OLTP workloads on Google Cloud, with automatic backups, encryption, replication, 99.95 availability, and read replicas.
discover cloud spanner, a globally scalable, strongly consistent relational database with sql and acid transactions, five nines availability, and multi-region replication.
Describe Google Cloud Platform networking, including VPCs and subnets, firewall rules, routes, VPC peering and shared VPC, interconnects, Cloud VPN, load balancers, and IPsec tunnels.
Master Google Cloud operations by configuring cloud logging and monitoring, creating dashboards and alerts, and using debugger, profiler, trace, and error reporting to monitor latency and errors.
Survey Google Cloud big data and machine learning services, including BigQuery analytics, Dataflow and Dataproc pipelines, Pub/Sub messaging, Data Fusion, Looker, Data Studio, and Vertex AI with AutoML.
Dear Cloud Professional
Our idea is to give you an overview of the Google Cloud Platform.
Updates: New Services content, We had to reduce the content to 2 hours because of Udemys FREE content restrictions and we have optimized the content to give you a glance at Google Cloud.
Google is developer friendly and has provided almost all code examples to Githut link -> github Slash GoogleCloudPlatform/
Google provides $300 credit to try/Learn the GCP platform besides the free tier, So you can even learn paid services that do not have free tier.
Note: This course has been created from Cloud Architect Certifications Course (- Paid version) to give you an overall idea of the Google Cloud Platform. So may find a context mismatch.
Google has committed to many more data centers to extend its reach.
GCP is already prominent in Data Analytics, and Machine Learning offerings proven so many years and has added Storage, Compute platform, Database, Security, and many more public cloud services for enterprises around the world.
Google has private fiber optic cable around the world. They have innovative data centers built to scale enterprise infrastructure and platform services requirements
Hope this course will get you started on Google Cloud Platform
Thank You
GCP Gurus