This document provides an overview of a workshop on Google Cloud Platform presented by Javed Habib, GDSC Lead at IIT Bhilai. The workshop covers introduction to cloud computing and Google Cloud architecture, hands-on labs for Google Cloud storage options, APIs, Pub/Sub, security, big data analysis using Dataflow and BigQuery, machine learning with Vertex AI and AutoML, and networking and security on Google Cloud including VPCs, load balancing, and firewalls.

1. Indian Institute of Technology, Bhilai
Google Cloud Platform
Javed Habib
GDSC Lead, IIT Bhilai

2. What is the
GOAL of
Today!
Indian Institute of Technology,
Bhilai

3. Swags, what “you” are here for!!!
Tier Based System
Tier 1 Tier 2 Tier 3
First
80 completions
GCCF + GenAI
Pathway
First
60 completions
GCCF + GenAI
Pathway
First
40 completions
GCCF + GenAI
Pathway

4. Disclaimer!

5. Google Cloud Platform
1. Availing the codes
2. Intro to Cloud Computing
3. Google Cloud Architecture
4. Tour on hands-on labs
Owning a cloud resource for computation, storage, etc.
What is Cloud Computing?

6. Infrastructure
● Iaas – get pc
● Paas – get hosting
platform
● Saas – get services
● Faas – run function on demand

7. How do you talk with these resources?
SSH - gets connection via a secure pathway

8. Virtual Machine
A Virtual Machine (VM) is a compute
resource that uses software instead of a
physical computer to run programs and
deploy apps. One or more virtual “guest”
machines run on a physical “host” machine.

9. Path 2: Google Cloud Computing
Foundations: Infrastructure in Google
Cloud
➢ How do we STORE databases over Google Cloud?
➢ What’s an API? How to manage them over cloud?
➢ Can all of this be SECURED?

10. Part 1
Introduction to Google Cloud
Storage Options

11. Exploring Google Cloud Storage
1. Google Cloud offers a range of storage solutions to meet
diverse application needs.
2. Choices depend on data types and business requirements.
3. Managed storage and database services are scalable, reliable,
and user-friendly.
4. Notable services include Cloud Storage, Cloud SQL, Cloud
Spanner, Firestore, and Cloud Bigtable.
5. Cloud storage use cases: Content delivery, data analytics,
backup/archival storage.
6. Google's support for database migration and growth planning.
Section 1: Introduction to Google Cloud Storage Options

12. Section 2: Structured vs. Unstructured Data
1. Unstructured data: Non-tabular, e.g., documents, images, audio
files.
2. Cloud Storage is ideal for unstructured data storage.
3. Structured data: Organized in tables, rows, columns; easier to
work with.
4. Used for transactional and analytical workloads.
5. Options for structured data: Cloud SQL, Cloud Spanner,
Firestore, BigQuery.

13. 1. Cloud Storage: It's a flexible digital storage solution.
2. Key Features: You can keep different versions of files, control
when to delete them, and access them globally.
3. Organization: Your files are grouped into "buckets," and once
in a bucket, they can't be changed.
4. Rules: You can set rules for how long files stay and what
happens to them.
5. Hands-on Practice: You'll get to use and learn about cloud
storage in practical exercises.
Section 3: Google Cloud Storage Services
Buckets are the basic
containers that hold your
data. Everything that you
store in Cloud Storage must
be contained in a bucket.
You can use buckets to
organize your data and
control access to your data,
but unlike directories and
folders, you cannot nest
buckets.

14. Part 2
API- Application programming
interface

15. 1. API Basics: APIs (Application Programming Interfaces) are
like connectors that help different software talk to each
other.
2. Daily Life Examples: Think of APIs as the way apps on your
phone share information or log in using social media.
3. Why APIs Matter: They're vital for faster app development
and making digital services work together seamlessly.
4. Using APIs: In programming, you'll use APIs to access data,
perform tasks, and make apps interact with various online
services.
Section 1: Introduction to APIs and Cloud Endpoints

16. Section 2: Cloud Endpoints and Apigee API Management
● Cloud Endpoints: Manages APIs, ensures fast
performance, offers features like API Console,
Hosting, Logging, and Monitoring.
● Apigee API Management:
1. Apigee API Management ensures smooth operation
of computer programs, acting like a traffic cop for
APIs.
2. It's compatible with old and new systems,
simplifying upgrades and technology use.
3. Useful for reorganizing programs, making
technology run efficiently for businesses.

17. Pub/Sub Basics:
● Messaging service for data exchange.
● Handles asynchronous data.
Architecture and Components:
● Involves publishers, subscribers, and data processing.
● Data sources and Dataflow for processing.
Use Cases and Benefits:
● Handles IoT data.
● Provides reliability and global availability.
Challenges and Tools:
● Deals with data ingestion challenges.
● Uses visualization and analysis tools.
Section 3: Pub/Sub: Handling Distributed Messaging

18. Part 3
Security

19. Infrastructure Security Design

20. GC Encryption Options

21. What is Identity-Aware Proxy?
Identity-Aware Proxy (IAP) is a Google Cloud service that intercepts web requests sent to your application,
authenticates the user making the request using the Google Identity Service, and only lets the requests through if
they come from a user you authorize. In addition, it can modify the request headers to include information about
the authenticated user.

22. Time for a lab!

23. Identity Access Management

24. Time for a lab!

26. • What is Big Data?
Big data primarily refers to data sets that are too large or complex to
be dealt with by traditional data-processing application software.
• What is Big Data Analysis?
This field deals with collecting and analyzing large data sets to
find valuable information that can be leveraged to make better
business decisions.
• Cluster
A cluster refers to a group of computers (nodes) used to store
and process large volumes of data
• DataProc
Dataproc automation helps you create clusters which are
quick to start scale , manage and shut down.
Dataproc helps users process, transform and understand
vast quantities of data.

27. • Two Methods of Big Data Processing-
1. Batch processing-
Batch processing is the method computers use to *periodically* complete high-volume, repetitive data jobs.
Its a way to process large amounts of data that is collected over a period of time.(‘batches’ of data)
2. Stream Processing-
Stream processing is the process of being able to almost instantaneously analyze data that is streaming
(flowing) from one device to another.
The data output rate must be as fast as the data input rate
Dataflow is a google service optimized for
large-scale batch processing or long-running
stream processing of structured and
unstructured data.

28. • Data warehouse –
Terabytes and Petabytes of data gathered from a wide range of sources
• Big Query is a fully-managed data warehouse. Fully Managed implies that google takes care of all the underlying
infrastructure
BigQuery is like a
common staging
area for data
analytics workloads
BigQuery outputs
usually feed into two
buckets: business
intelligence tools and
AI/ML tools.

29. Let Machines do the work!

30. Objectives
1. Understanding basic ML concepts
2. Cloud solutions : Vertex AI
3. No code solution : Auto ML
4. More flexibility for Users : Custom Training
5. Using Pre-trained models : Prebuilt APIs

31. Thinking like a human

32. Sample Machine Learning Tasks
1. Sentiment Analysis
2. Spam Classification
3. Recommendation Systems
4. Object Detection
And many more ….

33. Artificial Intelligence vs Machine Learning

34. Unsupervised Learning vs Supervised Learning

35. Deep Learning

37. Vertex AI

39. Custom Training

43. Hands on!

44. Google Cloud Computing Foundations: Networking &
Security in Google Cloud
GDSC IIT Bhilai

45. IMP
VPC _ Auto Mode , Custom Mode
Ip Address - Public Private
Google Cloud Zone Region
Google CLoud Networking (VPC , Load Balancing , CDN , Interconnect , DNS)
Routes and Firewall (need not configure)
Multiple VPC Network (VPC Peering , Shared VPC )
Multiple Hybrid (IPSec VPN , Direct Peering , Carrier Peering , Dedicated Interconnect)
Load Balancing (HTTPs , SSL Proxy , TCP Proxy , Regional External , Internal HTTPs)

46. Cloud Region Zone

47. Lab Multiple VPC
1. Create 2 Networks
2. Create 2 VM
3. Ping and Check

48. Lab HTTP Load Balancer with Cloud Armor

49. Automation Using Google Cloud
● Infrastructure as Code (IaC)
● Terraform
● Google Cloud’s Operations
Suite

50. ● Web-based automation solutions
like Dashboards and Control
Panels.

51. ● Monitoring
● Logging
● Error Reporting
● Profiling