Google Cloud Services Fundamental Crash Course For Beginners

Create your first Compute Engine instance on Google Cloud Platform by selecting region, zone, and a machine family, then SSH into it and manage or delete as needed.

Create a docker image of a simple Flask app, prepare a Dockerfile and requirements.txt, then push to the artifact registry for deployment to Cloud Run or Google Kubernetes Engine.

Learn to deploy a containerized app to Google Cloud Run using images from Docker Hub or artifact registry, configure auto scaling, region, port 8080, and public access.

Deploy a dockerized flask app to Google Cloud using Google Kubernetes Engine. Create a standard cluster, deploy from artifact registry, and expose the service with Kubernetes services and ingress gateway.

Learn to create a Google Cloud Run function using an inline editor, configure HTTP triggers, choose region and Python 3.11 runtime, deploy and test, and observe function execution.

Deploy a hello world web app to Google App Engine using the standard Python 3 runtime, via gcloud app deploy with app.yaml and Flask, noting per-project region restrictions.

Review the core GCP compute services and when to use them, including Compute Engine, Spot VMs, Sole Tenant, VMware Engine, Cloud Run, App Engine, GKE, Cloud Functions, and Anthos.

Explore Google Cloud storage options—object, block, and file storage—and learn how buckets, regions, storage classes, lifecycle rules, and backups protect and move data.

Create a Google Cloud storage bucket to store data, recognizing buckets as containers for objects with unique names, configurable regions and storage classes, plus access controls and public access prevention.

Create a persistent disk in Google Compute Engine, compare standard, balanced, and SSD types, then attach the disk to a VM and configure regional or zonal storage and snapshots.

Learn how identity and access management gates Google Cloud Platform access by defining identities—Google accounts, Google Groups, Cloud Identity, Google Workspace, and service accounts—and assigning permissions and roles.

Explore Google Cloud IAM by examining predefined and custom roles. Learn how roles group permissions like Spanner databases list and instances get, and how to assign access to users.

Learn how to grant access in google cloud iam by assigning predefined roles or creating custom roles, understanding permissions, conditions, and the importance of minimal privileges.

Create a new service account in gcp, grant it access to resources like artifact registry or google spanner, and download a json key for authentication.

Create a Cloud SQL MySQL instance on Google Cloud, using a sandbox configuration (two vCPU, eight GB RAM, ten GB SSD), and configure backups, IP access, and basic management.

Learn to create a Google Cloud Spanner instance, choosing standard edition and regional configuration (Mumbai). Spin up a database and a table using Google Standard SQL and insert sample data.

Explore Google Cloud pub/sub for asynchronous communication by creating a topic and subscription, publishing messages, and choosing pull or push delivery with acknowledgment and retention concepts.

Create a Firestore database in native mode, build collections and documents, and run basic queries with where and order by, while understanding security rules and indexing options.

Create a BigQuery dataset and table in a Google Cloud project, set region, and define an ID and name schema, and explore queries and public data in BigQuery studio.

Provision a memory store Redis instance, connect via a private network, and perform basic Redis operations (PING, SET, GET, DEL) through the Redis endpoint on port 6379.

Learn how virtual private cloud in Google Cloud enables compute engine through default and custom VPC options. Explore subnets by region and manual subnet creation with IP ranges.

Understand the difference between ephemeral and static IP addresses in Google Cloud. Reserve a static IP in VPC, release unused addresses, and use gcloud compute address create to provision resources.

Explore how to use Google Cloud BigQuery, a data warehousing service for petabyte-scale data, to create datasets and tables, run SQL queries, and explore public datasets with dry-run cost awareness.

Explore Google Cloud Pub/Sub for asynchronous communication, creating topics and subscriptions, publishing and pulling messages, and managing delivery, retention, and acknowledgement at scale.

Explore Google Cloud Dataproc to lift and shift Spark and Hadoop jobs into the cloud, create a cluster, and quickly submit a Spark job using a jar.