Google cloud complete learning course

Module 1: Introduction to Google Cloud Platform (GCP)

1.1 Overview of GCP Services

Okay, here’s an overview of Google Cloud Platform (GCP) services, covering their main categories and some common examples:

GCP services are a collection of computing tools and infrastructure offered by Google over the internet. They allow you to build, deploy, and manage applications and services on Google’s global network. The services are generally organized into the following categories:

1. Compute: This category provides virtual machines and ways to run your code.

• Compute Engine: Think of this as renting a computer in Google’s data center. You choose the operating system, amount of RAM, CPU, and storage. Example: Running a web server or database on a virtual machine.
• Google Kubernetes Engine (GKE): Manages containerized applications using the Kubernetes orchestration system. Example: Deploying a microservices application where each service runs in a container.
• Cloud Functions: Serverless execution environment for event-driven code. You only pay when your function is running. Example: Resizing images when they are uploaded to cloud storage.
• Cloud Run: Another serverless option that runs containers. It’s good for stateless applications or those that can handle bursty traffic. Example: Building a web API that scales automatically.

2. Storage: This category provides different ways to store your data.

• Cloud Storage: Object storage for unstructured data like images, videos, and documents. Think of it like a giant online hard drive. Example: Storing website assets or backups.
• Cloud SQL: Managed relational databases like MySQL, PostgreSQL, and SQL Server. Example: Running a database for an e-commerce application.
• Cloud Spanner: A globally distributed, scalable, and consistent relational database. Example: Managing financial transactions requiring high reliability and scale.
• Cloud Datastore: A NoSQL document database. Example: Storing user profiles or product catalogs.
• Filestore: Network file system to share storage between Compute Engine instances. Example: Mounting a shared drive for a team working on video editing.

3. Networking: This category allows you to control how your applications connect to each other and the outside world.

• Virtual Private Cloud (VPC): A private network within Google Cloud that allows you to isolate your resources. Example: Creating a private network for your web server and database.
• Cloud Load Balancing: Distributes traffic across multiple instances of your application to improve performance and availability. Example: Ensuring your website stays up during a traffic spike.
• Cloud DNS: A highly scalable and reliable Domain Name System (DNS) service. Example: Mapping your domain name (e.g., example.com) to the IP address of your application.
• Cloud CDN: Content Delivery Network that caches your content at edge locations around the world to improve performance for users. Example: Speeding up delivery of images and videos to website visitors globally.

4. Data Analytics: This category provides tools to analyze and gain insights from your data.

• BigQuery: A fully managed, serverless data warehouse for large-scale data analytics. Example: Analyzing website traffic patterns to identify trends.
• Cloud Dataflow: A data processing service for batch and stream data processing. Example: Building a real-time data pipeline for social media sentiment analysis.
• Cloud Dataproc: A managed Hadoop and Spark service for big data processing. Example: Running machine learning algorithms on large datasets.
• Cloud Pub/Sub: A messaging service for real-time data streaming and event ingestion. Example: Building a system for tracking user activity in a mobile app.

5. Machine Learning: This category provides tools and services for building and deploying machine learning models.

• Vertex AI: A unified platform for building, deploying, and managing machine learning models. Example: Training a model to predict customer churn.
• Cloud Vision API: A pre-trained machine learning model for image recognition. Example: Identifying objects in images uploaded by users.
• Cloud Natural Language API: A pre-trained machine learning model for natural language processing. Example: Analyzing the sentiment of customer reviews.
• Cloud Speech-to-Text API: A pre-trained machine learning model for converting audio to text. Example: Transcribing customer service calls.
• Cloud Translation API: A pre-trained machine learning model for translating text between languages. Example: Automatically translating website content.

6. Identity and Security: This category helps you manage access to your resources and secure your data.

• Cloud Identity and Access Management (IAM): Controls who has access to your resources and what they can do. Example: Granting a developer access to deploy code but not to manage billing.
• Cloud Key Management Service (KMS): Manages cryptographic keys used to protect your data. Example: Encrypting data stored in Cloud Storage.
• Cloud Armor: Web Application Firewall (WAF) to protect your application from common web exploits. Example: Preventing SQL injection attacks.
• Secret Manager: Securely stores sensitive data like API keys, passwords, and certificates. Example: Storing database passwords instead of hardcoding them in your application.

This is a high-level overview. Each service has many more features and configuration options. The specific services you use will depend on the requirements of your application or project.

1.2 GCP Regions and Zones

1.3 Setting up a GCP Account

1.4 Navigating the GCP Console

Module 2: Compute Engine: Virtual Machines

2.1 Creating and Managing VMs

2.2 VM Instance Types

2.3 Networking for VMs

2.4 Storage Options for VMs

2.5 Deploying Applications on VMs

Module 3: Cloud Storage: Object Storage

3.1 Understanding Cloud Storage Buckets

3.2 Uploading and Downloading Objects

3.3 Object Versioning

3.4 Access Control and Permissions

3.5 Lifecycle Management

Module 4: Networking in GCP: Virtual Private Cloud (VPC)

4.1 Creating and Configuring VPC Networks

4.2 Subnets and IP Addressing

4.3 Firewall Rules

4.4 Cloud DNS

4.5 Load Balancing

Module 5: Databases in GCP: Cloud SQL and Cloud Spanner

5.1 Overview of Cloud SQL

5.2 Setting up Cloud SQL Instances

5.3 Managing Databases and Users

5.4 Introduction to Cloud Spanner

5.5 Choosing the Right Database

Module 6: Big Data and Analytics: BigQuery

6.1 Introduction to BigQuery

6.2 Loading Data into BigQuery

6.3 Querying Data with SQL

6.4 Analyzing Data with BigQuery

6.5 Data Visualization with BigQuery

Module 7: Identity and Access Management (IAM)

7.1 Understanding IAM Roles and Permissions

7.2 Creating and Managing Service Accounts

7.3 Granting Access to Resources

7.4 Best Practices for IAM

Module 8: Define, Track and Measure Your Success with Google Cloud Guided Learning

8.1 Defining Key Performance Indicators (KPIs)

8.2 Setting SMART Goals for Google Cloud Projects

8.3 Utilizing Google Cloud Monitoring Tools

8.4 Tracking Progress with Google Cloud Skills Boost

8.5 Measuring Return on Investment (ROI) of Cloud Investments

8.6 Leveraging Google Cloud Learning Paths