Big Data & Cloud Computing

Big Data & Cloud Computing In the subject Technology, the topic Big Data & Cloud Computing focuses on managing and analyzing massive volumes of data. Big data refers to extremely large and complex datasets that cannot be processed effectively using traditional methods, requiring advanced tools and cloud-based solutions for storage and analysis.

Big Data

• Definition : Big data refers to extremely large and complex datasets that cannot be processed or analyzed effectively using traditional data-processing methods. These datasets are often too complex, voluminous, or fast-changing for conventional software tools to handle.
• While raw big data contains a wealth of potential insights, it requires processing, transformation, and analysis to unlock its value. Once processed, the data can be analyzed using advanced analytics, machine learning, and statistical methods to uncover insights, patterns, and trends.

Characteristics of Big Data (The 6 Vs)

V	Definition	Example	Significance
Volume	The amount of data generated and collected.	Social media platforms generating terabytes of data daily.	Requires scalable storage and processing systems like Hadoop.
Velocity	The speed at which data is generated, processed, and analyzed.	Real-time data from IoT devices or stock market transactions.	Demands real-time processing technologies such as Apache Kafka.
Variety	Different types and formats of data.	Structured (databases), unstructured (images, videos), and semi-structured data (JSON).	Requires tools like NoSQL databases for diverse formats.
Veracity	The accuracy, reliability, and trustworthiness of data.	Misinformation in social media or incomplete survey data.	Emphasizes the need for data cleansing and validation processes.
Value	The usefulness of data in deriving actionable insights.	Retail businesses analyzing customer behavior for personalized offers.	Ensures ROI by focusing on meaningful data for business decisions.
Variability	The inconsistency and dynamic nature of data.	Changes in the context of words in sentiment analysis over time.	Requires adaptive algorithms and contextual understanding.

Popular Metaphors:

• Data is the New Oil: Data is valuable, just like oil, but it needs to be processed and analyzed to be useful.
• Data is the New Gold: Data is as valuable as gold, driving growth and innovation in businesses.
• Data is the New Currency: Data can be traded and used to create value, like money in the digital world.
• Data as the New “Crown Jewel”: Data is seen as a precious asset that gives businesses a competitive edge.
• Data is the New ‘Electricity’: Just like electricity powered the industrial revolution, data will power today’s technology and growth.
• Data is the New “Intellectual Property”: Data is valuable property that can be owned, shared, and used for profit.Big Data is the ‘Goldmine’ of the Digital Age: Big data is like a goldmine full of valuable insights waiting to be discovered.

Key Components of the Big Data Ecosystem

Data Sources

• Social Media, IoT Devices, Government Records, Mobile Devices, Online Transactions.

Data Storage

• Distributed StorageSystem → Big Data is often stored across multiple servers to ensure scalability and redundancy.
  • HDFS (Hadoop Distributed File System): Splits data into blocks and stores across machines.
  • Cloud Storage:  Cloud platforms like Amazon S3, Google Cloud Storage, or Microsoft Azure 
• NoSQL Databases: Databases like MongoDB, Cassandra, and CouchDB are optimized for storing unstructured and semi-structured data, unlike traditional relational databases.
• Data Lakes: A storage repository that can hold vast amounts of raw data in its native format until it’s needed for analysis.

Data Processing

• Batch Processing → Large batches of historical data (e.g., Hadoop MapReduce).
• Stream Processing → Real-time data processing (e.g., Apache Kafka).
• Data Warehousing → Data is extracted from different sources, cleaned, and loaded into a central repository for analytics. This system allows users to run complex queries on large datasets.

Data Analytics and Insights

• Statistical Analysis → Summarizing data patterns (e.g., healthcare trends).
• Machine Learning & AI → Predictive modeling (e.g., crop yield forecasts).
• Data Visualization → Graphical representation of insights (e.g., Power BI, Tableau).

Data Security and Privacy

• Encryption → Protecting data during storage and transmission.
• Access Control → Role-based access to secure data.
• Data Anonymization → Protecting privacy by removing personal identifiers.

Benefits of Big Data

Enhanced Decision Making

• Data-driven insights help organizations make better decisions.
• Examples: AI-powered tools assist doctors in diagnosing.

Operational Efficiency

• Analyzing data helps streamline processes and optimize resources.
• Examples: Predictive maintenance reduces factory downtime.

Cost Reduction

• Big data identifies inefficiencies and cost-saving areas.
• Examples: Optimized inventory management cuts warehousing costs.

Innovation and New Product Development

• Data uncovers market needs, leading to new products.
• Examples: E-commerce – Analyzing behavior enables personalized recommendations.

Personalization

• Big data tailors experiences to individual preferences.
• Examples: Netflix – Recommends content based on user history.

Predictive Analytics

• Explanation: Historical data helps forecast future outcomes for proactive actions.
• Examples: Predicts stock market trends.

Competitive Advantage

• Data insights provide an edge over competitors.
• Examples: Sports – Performance data enhances team training and strategies.

Improved Customer Experience

• Analyzing customer data improves satisfaction and loyalty.
• Examples: Airlines – Big data personalizes travel and loyalty services.

Applications of Big data

Sector	Application	Examples
Healthcare	Personalized Medicine, Predictive Analytics, Medical Imaging	IBM Watson Health analyzes cancer data for personalized treatments; 3Nethra helps in screening for common eye problems; COVID-19 → Genomic sequences, Variant Detection, Drug Discovery.Tackling antibiotic resistance → Big data analytics identifying patterns in antibiotic usage to combat superbugs
Agriculture	Precision Farming, Smart Irrigation Systems	Automatic weather stations, soil sensors, and crop images → Precision sowing, irrigation management, optimizing crop yieldsMicrosoft + KPAC Project for agricultural commodity price forecasting; e-NAM → Uses big data to forecast commodity pricesIMD (INSAT-3D, INSAT-3DR) → Provides weather data for agriculture; NOAA → Provides global weather data used for forecasting.
Retail and E-commerce	Customer Segmentation, Personalization, Inventory Management	Amazon and Netflix → recommend products/movies using Big Data; Walmart → Uses Big Data for demand forecasting and inventory management.
Finance and Banking	Fraud Detection, Risk Management	ICICI Bank → Uses machine learning and big data to detect fraudulent transactions and predict financial risks; Credit scoring models → Big data-driven analysis of customer credit history to predict loan repayment probabilities.
Transportation and Logistics	Fleet Management, Predictive Maintenance	Uber → Big data algorithms optimize driver-rider matchingAmazon → Uses big data to manage its vast fleet, optimizing delivery routes and times; Airlines → Predictive maintenance uses real-time data from aircraft to predict potential failures and reduce downtime, saving costs.
Manufacturing and Industry 4.0	Smart Manufacturing,Supply Chain Optimization	Companies like Amazon and Flipkart optimize their supply chain using Big Data analytics for real-time adjustments.
Smart Cities and Urban Planning	Traffic Management, Energy Management	Smart grids optimize energy distribution and reduce wastage.Smart cities like Bengaluru → Use data analytics for urban planning, traffic management, and to monitor environmental parameters like air quality.
Education	Student Performance Analytics, Personalized Learning	Coursera and Khan Academy use Big Data to personalize courses; AI-driven platforms provide real-time feedback and recommend study resources.
Government and Public Sector	E-Governance, Disaster Management	CoWIN app used for vaccination scheduling in India;Google uses Big Data for flood prediction in 80 countries, providing advance warnings.MGNREGA → Analyzes data on enrollment to predict unemployment trends; Geo-tagging assets in MGNREGA → Helps track physical assets and their usage in rural areas; Project ADVAIT → Big data-driven analytics to detect fraud in tax systems; Operation Clean Money (During Demonetisation) → Monitors large transactions to detect black money; Project insight → by income tax departmentHydrological data sharing between India and China → Collaborative flood forecasting; India-South Korea → Collaboration on big data for disaster management prediction.
Environmental Sustainability	Climate Change Monitoring, Wildlife Conservation	Climate models using satellite data and Big Data to forecast climate change effects; Big Data and GPS tracking help monitor endangered species.

Challenges of Big Data

Data Privacy and Security

• Privacy Risks: Vast personal data collection leads to potential breaches.
  • Example: India’s Aadhaar system faces criticism over privacy concerns.
• Data Breaches and Cybersecurity: Increased data collection attracts cyberattacks.
  • Example: Financial institutions face security breaches due to weak protection.
• Regulatory Compliance: Ensuring data use complies with regulations like GDPR.

Data Breaches in India (2016-2021)

1. 2016 Debit Card Data Breach
   • Malware injected into Hitachi Payment Services
   • 3.2 million debit cards compromised
2. Aadhar Data Breach (2018)
   • 1.1 billion citizens’ data exposed
3. SBI Data Breach (2019): Due to unprotected server in Mumbai
4. Justdial Data Breach (2019)
5. Kudankulam Nuclear Plant Data Breach (2019)
6. 2019 Credit & Debit Card Data Breach
   • 1.3 million card records sold on dark web
7. BigBasket Data Breach (2020)
8. Unacademy Data Breach (2020)
   • Data sold on dark web for ₹1.5 lakh INR ($2,000 USD)
9. Air India Data Breach (2021)
10. Dominos India Data Breach (2021)

Data Quality and Integrity

• Inconsistent Data: Data from diverse sources may be conflicting or incomplete.
• Noise and Redundancy: Irrelevant data (noise) distorts analysis. Example: Social media data may include irrelevant or spam content.
• Missing Data: Gaps in data affect the quality of insights.

Data Storage and Management

• Complex Data Integration: Combining structured, semi-structured, and unstructured data is challenging.
• Cost of Storage: Storing large datasets can be expensive, especially for smaller entities.

Data Processing and Analysis

• Computational Power: Large data sets require significant computing resources.
• Complex Algorithms: Analyzing unstructured data demands specialized algorithms.
• Skill Gap: Lack of skilled data scientists limits Big Data applications.

Ethical and Legal Issues

• Data Ownership and Consent: Who owns the data and who can use it?
• Bias in Algorithms: Algorithms may reflect or amplify biases, leading to unfair outcomes.
• Surveillance: Big Data raises privacy concerns due to potential mass surveillance.

Big Data Initiatives in India

National Data and Analytics Platform (NDAP):

• Launched by: NITI Aayog
• Objective: Democratize access to public government data.
• Provides clean, accessible, and interoperable datasets.

Central Board of Direct Taxes (CBDT) Project Insight:

• Purpose: Improve tax compliance and detect fraud using big data analytics.
• Features: Analyzes vast volumes of financial data to track tax evasion.

Digital India Programme:

• Focus on Big Data: Encourages the digitization of services, creating vast datasets.
• Enhances decision-making in areas like e-governance, healthcare, and education.
• Filtering of Ghost Beneficiaries using JAM trinity

Open Government Data (OGD) Platform

• Launched in October 2012. 
• The platform was created by the National Informatics Centre (NIC) of the Ministry of Electronics and Information Technology (MeitY). 
• It was launched to make government data available to the public in compliance with the National Data Sharing and Accessibility Policy (NDSAP)

National Data Warehouse

• By Ministry of Statistics and Programme Implementation (MoSPI):
• A centralized repository where data collected by various government agencies is stored, making it easily accessible for analysis and public use.

Big Data Management Policy: By CAG

Indian Railways and Big Data:

• Project: One ICT (Information and Communication Technology) Platform.
• Purpose: Improve operational efficiency and passenger services.

Agriculture: Big Data and AI Integration:

• The Ministry of Agriculture uses big data to predict crop yields, monitor weather patterns, and analyze soil health.
• Tools like Krishi Vigyan Kendras and National Agriculture Market (e-NAM) use big data for real-time analysis and price discovery.

National e-Governance Plan (NeGP):

• Objective: Use big data to improve service delivery and transparency in governance.

National Digital Health Mission (NDHM):

• Integrates big data analytics to create electronic health records (ABHA IDs).

Technology Innovation Hubs (TIHs) Mission

• Key TIHs Focusing on Big Data:
  • Indian Statistical Institute (ISI), Kolkata
  • IIT Indore
  • IIT (BHU), Varanasi

Recent

• In January 2025, India joined the UN Committee of Experts on Big Data and Data Science for Official Statistics (UN-CEBD). This membership allows India to contribute to global standards in utilizing big data for official statistics.

Cloud Computing

Definition: Cloud computing refers to the delivery of computing services (like servers, storage, databases, networking, software) over the internet (“the cloud”).

• These services are provided on-demand and allow users to access resources without needing on-premises infrastructure. 
• This approach offers faster innovation, flexible resources, and economies of scale. 
• Example: Using Google Drive to store files. 

Why is it Called “Cloud”?

• The term “cloud” symbolizes the internet. Computing resources are hosted remotely and accessed online, much like information stored “in the cloud.”

Everyday Examples of Cloud Computing

• Email Services: Gmail, Yahoo Mail.
• Cloud Storage: Google Drive, Dropbox.
• Streaming Services: Netflix, Spotify.
• Online Collaboration: Google Docs, Microsoft Teams.

Key Features and Benefits of Cloud Computing

1. On-Demand Access: Access resources as needed. Example: Accessing additional storage when required.
2. Broad Network Access: Accessible through various devices (PCs, smartphones, etc.) via the internet.
3. Resource Pooling: Multiple users share the same resources.
4. Pay-as-You-Go: Only pay for what you use. Example: Paying for cloud storage based on the amount of data stored.
5. Reliability: Automatic backups and disaster recovery ensure data safety.
6. Scalability and Elasticity: Resources can be scaled up or down based on demand.

Benefits of Cloud Computing:

• Cost Efficiency → Pay only for what you use, no upfront costs.
• Scalability & Flexibility → Scale resources up or down based on demand. Ex: BigBasket scales up during festive seasons for peak traffic.
• Global Accessibility → Access services from anywhere, on any device. Example: Google Drive.
• Enhanced Security → Data encryption, secure access, Disaster recovery and automatic backups. 
• Collaboration & Productivity → Real-time teamwork and easy file sharing. Ex: Zoho enables global team collaboration via cloud tools.
• Automatic Updates & Maintenance → Seamless updates without downtime.
• High Performance → Access to powerful computing without hardware investment.
• Environmental Benefits → Reduced energy consumption and e-waste.
• Innovation & Agility → Faster development and access to advanced tools.
• Business Continuity → High reliability, redundancy, and minimal disruption.

Types of Cloud Computing

A. Deployment Models of Cloud Computing

1. Public Cloud:
   • Definition: Owned and operated by third-party cloud providers. Resources are delivered over the internet and shared among multiple customers.
   • Examples: Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure.
   • Advantages:
     • Cost-effective as resources are shared.
     • Scalable to accommodate fluctuating demands.
     • No maintenance required by the user.
   • Use Cases: Web hosting, testing and development, and small-to-medium enterprises (SMEs).
   • Indian Context: Startups like Zomato and Paytm use public clouds for cost-efficient scaling.
2. Private Cloud:
   • Definition: Exclusive to a single organization, providing more control and security.
   • Examples: On-premises private clouds used by enterprises like Infosys or Tata Consultancy Services (TCS).
   • Advantages:
     • Enhanced data security and control.
     • Tailored to organizational needs.
   • Use Cases: Financial institutions, healthcare organizations, and government agencies.
   • Indian Context: Used by banks like SBI for secure customer data management.
3. Hybrid Cloud:
   • Definition: Combines public and private clouds, allowing data and applications to be shared between them.
   • Examples: AWS Outposts, Microsoft Azure Stack
   • Advantages:
     • Balances cost efficiency with data security.
     • Suitable for dynamic workloads.
   • Indian Context: Indian Railways employs a hybrid cloud model for managing its vast network of data.
4. Community Cloud:
   • Definition: A shared cloud infrastructure that is used by several organizations with common concerns (e.g., security, compliance, or regulatory requirements). The infrastructure may be managed by one of the organizations or a third-party provider.
   • Examples: HealthCloud for healthcare organizations
   • Advantages:
     • Collaboration among organizations.
     • Cost-sharing among members.
   • Indian Context: Universities and research institutions sharing resources for collaborative projects.

B. Service Models of Cloud Computing

1. Infrastructure as a Service (IaaS):
   • Definition: Provides virtualized computing resources over the internet. IaaS offers the most basic services, where the provider manages the infrastructure (servers, storage, networking) while the user controls the operating system, applications, and middleware.
   • Examples: AWS EC2 (Elastic Compute Cloud), Google Compute Engine, Microsoft Azure Virtual Machines, MeghRaj Initiative.
   • Advantages:
     • Highly scalable and cost-efficient.
     • Users have control over infrastructure.
   • Indian Context: Zomato uses Microsoft Azure for managing its online platform
2. Platform as a Service (PaaS):
   • Definition: Provides a platform allowing customers to develop, run, and manage applications without dealing with infrastructure.
   • Examples: Microsoft Azure App Service, Google App Engine, AWS Elastic Beanstalk, Aadhaar authentication services, etc..
   • Advantages:
     • Simplifies development.
     • Accelerates deployment cycles.
   • Indian Context: Used by Indian startups for rapid development of apps and solutions.
3. Software as a Service (SaaS):
   • Definition: Delivers software applications over the internet on a subscription basis.
   • Examples: Google Workspace (Docs, Sheets, Gmail), Microsoft Office 365, Salesforce, GSTN (Goods and Services Tax Network), DigiLocker, AEPS, BHIM.
   • Advantages:
     • Ready-to-use applications accessible over the internet.
     • Subscription-based pricing model.
     • Accessible from any device with an internet connection.
   • Indian Context: DigiLocker, an Indian government initiative, is a SaaS-based application.

Aspect	IaaS	PaaS	SaaS
Target Audience	IT admins, developers needing control	Developers focusing on app development	End users needing ready-to-use tools
Control Level	High (infrastructure control)	Medium (app control, no infrastructure)	Low (provider manages everything)
Scalability	Highly scalable	Scalable, but within platform limits	Limited to the software’s features
Setup Time	Medium	Low	Immediate
Customization	High	Medium	Low
Cost	Pay-per-use (based on resources used)	Pay-per-use (platform usage)	Subscription-based
Maintenance	User-managed	Shared between user and provider	Provider-managed
Examples	Amazon EC2, Microsoft Azure VMs, Google Compute Engine	AWS Elastic Beanstalk, Google App Engine, Microsoft Azure App Service	Google Workspace, Zoho CRM, Microsoft Office 365

How Cloud Computing Works

A. Components of Cloud Architecture

• Front-End Platform (Client Side) : Application. Examples: Google Drive, e-commerce website.
• Back-End Platform (Server Side) → Infrastructure: Servers, storage, databases etc.
  Examples: AWS EC2, Google Cloud Storage.
• Cloud-Based Delivery Model
  • Service Layer: Delivers IaaS, PaaS, or SaaS services.
  • Management Layer: Handles resource monitoring and provisioning.
  • Security Layer: Protects resources from unauthorized access and threats.
• Network → Internet/intranet connects front-end and back-end, enabling smooth service access.

B. Virtualization in Cloud Computing

• The process of creating virtual versions of physical components like servers, storage, and networks

C. Cloud Computing Process Flow

1. User Request → User sends a request (e.g., accessing a website or downloading a file).
2. Data Transmission → Request is sent via the internet to the cloud’s back-end servers.
3. Cloud Service Processing → Back-end processes the request using data or computing resources.
4. Response to User → Cloud server sends processed data back to the user’s device.

Key Challenges of Cloud Computing

• Security & Privacy Concerns: Storing sensitive data on the cloud makes it vulnerable to breaches or cyberattacks.
• Downtime & Service Reliability: Cloud services may experience outages, disrupting operations.
• Vendor Lock-In: Switching cloud providers can be complex and costly due to lack of compatibility.
• Data Transfer & Bandwidth Costs: Transferring large data volumes can lead to high costs.
• Compliance & Legal Issues: Data storage across borders may raise compliance concerns. Example: GDPR compliance for European businesses
• Limited Control & Flexibility: Businesses may lose control over infrastructure, limiting customization.
• Environmental Impact: Data centers consume significant energy, raising environmental concerns.
• Cost Management: High scaling cost.
• Internet DependencyL Cloud services rely on stable and high-speed internet connections. In areas with poor connectivity, accessing cloud services can be problematic.

Applications of Cloud Computing

Cloud Computing in Business and Enterprises

• Software as a Service (SaaS) → Access software online without installation. Example: Google Workspace, Salesforce for Customer Relationship Management (CRM)
• Cloud-Based Collaboration → Real-time teamwork regardless of location.
  Example: Microsoft Teams, Slack for file sharing and communication.
• Big Data & Analytics → Analyze large datasets for insights. Example: AWS, Google Cloud for real-time data processing.
• Disaster Recovery → Backup data and ensure business continuity. Example: DRaaS minimizes downtime during disruptions.

Cloud in Education

• Learning Management Systems (LMS): Supports online courses, virtual classrooms, and personalized learning. Example: Google Classroom, Coursera for eLearning.

Cloud in Healthcare

• Telemedicine & Remote Monitoring: Practo leverages cloud technologies for teleconsultation across India.
• Electronic Health Records (EHR) : Enables secure storage and remote access to patient data for better care. Example → Health Cloud: ABHA ID to securely store and manage health records online under Ayushman Bharat Digital Mission.
• AI & Data-Driven Diagnostics: AI-powered cloud systems enhance diagnostics and treatment strategies.

Cloud in Government

• E-Governance and Public Services
  • National e-Government Services Portal (NeGP):: The NeGP relies on cloud computing for hosting online services and applications, enabling access to government services from anywhere, anytime.
  • Online portals for tax payments, registrations, and pension management (e.g., GST portal, e-District).
• Smart Cities
  • Bhubaneswar and Ahmedabad implement cloud to optimize urban services.

Cloud in Media and Entertainment

• Content Streaming and Distribution: Example: Disney+ Hotstar uses cloud for live sports streaming and on-demand content.

Cloud in Big Data and Analytics

• Data Analysis and Insights – Example: AWS and Google Cloud offer tools for big data analytics and visualization.

Cloud in IoT and Smart Devices

• Integration and Automation : Connects IoT devices for data exchange and intelligent automation. Example: Google Nest and Amazon Alexa use cloud to manage smart home systems.

Cloud in Financial Services

• Secure Transactions and Digital Banking Example: ICICI Bank, SBI using AI-driven cloud solutions.

Cloud in Supply Chain Management

• Cloud-based ERP solutions for supply chain efficiency. Example: SAP S/4HANA for logistics and production.

Cloud Gaming

• Play games without high-end hardware. Example: NVIDIA GeForce NOW, Google Stadia for game streaming.

E-Commerce & Retail

• Manage online stores and inventory in real-time. Example: Amazon, Shopify for e-commerce platforms.

Data Storage & Backup

• Store and access vast data without physical devices. Example: Google Drive, iCloud for storage and automatic backups.

Artificial Intelligence (AI) & Machine Learning (ML)

• Run AI models without in-house infrastructure.
• Example: Google AI, AWS AI Services for ML tools.

Cloud Computing initiatives in India

MeghRaj (GI Cloud):

• Launched by: Ministry of Electronics and Information Technology (MeitY).
• Launch Year: 2013 
• Objective:
  • Optimize ICT spending
  • Accelerate e-service delivery
  • Enhance interoperability among government departments
• Infrastructure:
  • Utilizes existing National and State Data Centers 
  • Offers cloud service models: IaaS (Infrastructure as a Service), PaaS (Platform as a Service), SaaS (Software as a Service)
• Implementation: Managed by the National Informatics Centre (NIC)
• Security: Operates on a private cloud infrastructure for enhanced security
• Impact:
  • Improves transparency, accountability, and efficiency in government operations
  • Over 300 government departments leverage cloud services, streamlining application deployment and data management

National Data Centres

• Expanded capacity to 100 PB; 
• Enhancements in NIC’s National Cloud Services with key data centers established by NIC in Delhi, Pune, Bhubaneswar, Hyderabad.

Banking

• Cloud Adoption by the Reserve Bank of India (RBI): The Reserve Bank of India plans to launch a cloud storage pilot program in 2025 for financial institutions.
• Indian Banking Community Cloud (IBCC) is the banking industry’s first community cloud.

National Knowledge Network (NKN):

• Launched by: Department of Telecommunications.
• Objective: To connect academic, research, and government institutions across the country.
• NKN facilitates collaboration among institutions via cloud platforms, enabling the sharing of data and resources.

PARAM Supercomputers:

India has developed the PARAM series of supercomputers to enhance computational capabilities. 

Microsoft’s Investment in India:

• Microsoft announced a $3 billion investment in India to expand its Azure cloud and AI infrastructure.
  • →setting up new data centers and training 10 million people in AI by 2030.

Digilocker

• Public cloud storage for citizens, enabling instant digital verification and signing by the government.

Railways 

• RailCloud: First application developed is Nivaran grievance redressal platform.

Project AMBER under the SANKALP Programme:

• Objective: Train 1,500 individuals in cloud computing skills.
• Collaboration: Partnership between Ministry of Skill Development and Entrepreneurship (MSDE), Generation India Foundation (GIF), and Amazon Web Services India (AWS India).
• Special Focus: Improve gender diversity in the tech industry.
• Scale and Impact: Project AMBER aims to train 30,000 youth, with 50% participation from women.
  • Supports demographic transitions and technological advancements in areas like Industry 4.0 and Web 3.0.

India as a Cloud Computing and Data Centre Hub

India’s Digital Growth:

• Mobile data consumption: Highest globally.
• Digital economy is projected to grow from $200 billion (2017-18) to $1 trillion by 2028. 

Cloud Computing & Data Centres

• What is a Data Centre?
  • A secure space for computing, storing, processing, and accessing data.
  • Hosts Cloud Service Providers (e.g., AWS, Microsoft Azure, Google Cloud).
• India’s Vision:
  • PM Narendra Modi’s commitment: Make India a Global Data Centre Hub.
  • Budget 2022-23: Data Centres included in infrastructure list.

Global Context

• Global Data Centre Stats: ~8,000 globally; top 6 countries: US (33%), UK (5.7%), Germany (5.5%), China (5.2%), Canada (3.3%), Netherlands (3.4%).

India’s Data-Centric Future

• Why India Needs Data Centres:
  • Proposed Data Protection Act mandates data localization.
  • Protection of digital sovereignty.
• Draft Data Centre Policy (2020):
  • Objectives:
    • Make India a Global Hub.
    • Promote investments & boost the digital economy.
    • Enable trusted hosting infrastructure.

Enabling Ecosystem

• Key Policy Areas:
  • Clean, cost-effective electricity.
  • Robust connectivity (MeitY + DoT collaboration).
  • Recognize Data Centres as Essential Services.
  • Establish Data Centre Economic Zones (DCEZs).
• TRAI Recommendations (2022):
  • Incentives (e.g., Data Centre Incentivization Scheme).
  • Launch Data Centre Readiness Index (DCRI).
  • Promote green certification & data-related courses.

NIC & National Data Centres

• National Informatics Centre (NIC):
  • Operates National Data Centres at Delhi, Pune, Hyderabad, Bhubaneswar, and regional centres.
  • Supports e-Governance projects.
• Milestones:
  • Launched first Data Centre (Hyderabad, 2008).
  • Initiated National Cloud Services (2014) under MeghRaj Initiative.

Edge Computing

Edge computing is a distributed computing model that processes data closer to its source, such as IoT devices or local data centers, rather than relying solely on centralized cloud servers. 

How It Works:

• Data → Processed locally → Only relevant info sent to the cloud.

Benefits of Edge Computing

• Low Latency: Instant responses for real-time needs (e.g., autonomous vehicles).
• Cost-Efficient: Less data sent to cloud saves bandwidth and expenses.
• Reliable: Works even with poor internet connectivity.
• Privacy: Local processing minimizes data exposure risks.

Applications

• IoT: Smart thermostats optimize settings locally.
• Healthcare: Wearables alert users instantly on abnormal vitals.
• Smart Cities: Traffic cameras detect and manage congestion in real-time.
• Retail: Smart shelves track stock levels locally.

Challenges

• Higher Setup Costs: Requires advanced edge devices.
• Security Risks: Vulnerable to physical or cyber tampering.
• Resource Limits: Edge devices have less processing power than cloud servers.

Edge + Cloud:

• Edge handles real-time processing locally.
• Cloud stores and analyzes large-scale data for long-term use.

Example: Autonomous vehicles process navigation in real-time (edge) and upload data for fleet analysis (cloud).