How Big Data is Revolutionizing Smart Cities? 12 Roles Making an Impact in 2025

India’s urban population is projected to reach 600 million by the end of 2025, increasing the demand for smarter infrastructure and efficient city management. Big Data in Smart Cities plays a crucial role in enhancing efficiency by enabling data-driven decision-making, real-time monitoring, and automation. 

Governments and enterprises leverage these insights to optimize energy usage, waste management, and public safety. This blog explores How Big Data is Revolutionizing Smart Cities, the Development of Smart Cities, and 12 key roles that will make an impact in 2025.

How Big Data is Revolutionizing Smart Cities? 12 Key Roles and Their Impact

Smart cities are evolving rapidly with Big Data, AI, IoT, and 5G in 2025. These technologies help manage traffic, energy, public safety, and sustainability. The global Big Data in smart cities market is projected to reach $2.5 trillion by 2026. Additionally, urban population growth is expected to rise to 68% by 2050, increasing the need for data-driven solutions.

Big Data in Smart Cities enables governments and organizations to collect, analyze, and use vast amounts of data for real-time decision-making. From intelligent traffic systems to predictive maintenance in infrastructure, it plays a transformative role in urban development and the development of smart cities.

Below are 12 key roles in How Big Data is Revolutionizing Smart Cities and shaping modern cities.

Also Read: Big Data Technology: Transforming Data into Actionable Insights

Let's now explore these roles in detail and understand their impact.

1. Smart Traffic Management

Efficient traffic management is crucial for reducing congestion and travel time. Big Data optimizes transportation networks using real-time analytics, enhancing urban mobility, reducing emissions, and development of smart cities.

Key Features

Big Data in smart cities enables intelligent traffic solutions by analyzing vast amounts of data from multiple sources. Below are some key features:

• Real-time Traffic Monitoring – AI-powered platforms like Google Maps and TomTom analyze live GPS and sensor data to suggest the fastest routes.
• Predictive Analytics – Machine learning models forecast congestion patterns, helping city planners design efficient road networks.
• Adaptive Traffic Signals – Smart signals, powered by IoT platforms like Siemens Mobility, adjust light timing based on real-time vehicle density.
• Connected Vehicles – Automotive companies like Tesla and Mahindra integrate vehicle-to-infrastructure (V2I) communication to enhance road safety and efficiency.
• Smart Parking Systems – Platforms like Park+ and Bosch use data-driven insights to guide drivers toward available parking spaces.

Applications:

Big Data in cities are leveraging Big Data to combat traffic congestion and enhance urban mobility. Below are some impactful applications:

• AI-powered Traffic Control – Bengaluru and Mumbai use AI-based surveillance and data-driven traffic light adjustments to optimize vehicle movement.
• Intelligent Route Planning – Apps like Ola and Uber integrate real-time traffic data to provide optimized ride routes.
• EV Charging Infrastructure Optimization – Companies like Tata Power and Statiq use predictive analytics to place charging stations where demand is highest.
• Public Transport Efficiency – Delhi Metro and BMTC leverage Big Data to analyze commuter patterns, optimizing schedules and routes.
• Smart Parking Solutions – Cities like Hyderabad and Pune are deploying automated parking systems powered by IoT and data analytics to reduce congestion.

Also Read: Top Data Analytics Tools Every Data Scientist Should Know About

2. Smart Waste Management

Big Data ensures efficient waste collection and disposal by automating and optimizing waste management Big Data plays a crucial role in streamlining waste collection and disposal processes. AI and IoT technologies enable automation, reducing inefficiencies in urban waste management. 

Key Features

• Sensor-enabled Bins – Smart bins, like those used by Bigbelly, are equipped with sensors to detect fill levels, ensuring timely pickups and preventing overflow.
• Predictive Waste Collection – Machine learning models analyze waste generation patterns to predict when bins will be full, reducing unnecessary collection trips.
• Optimized Routing for Waste Trucks – GPS and data analytics help optimize waste truck routes, saving time, fuel, and reducing emissions.
• Recycling Insights – Big Data tools like WasteLogics help track recycling materials, improving the efficiency of recycling plants and ensuring better resource recovery.
• Waste Segregation Systems – Automated systems use AI to sort waste efficiently, increasing the percentage of recyclable materials.

Applications

In India, Big Data is helping cities tackle waste management issues and move toward a more sustainable future. Here are some impactful applications:

• Smart Bins in Public Spaces – Cities like Delhi and Bengaluru are installing sensor-equipped smart bins to manage waste more effectively and ensure timely collection.
• Optimized Waste Collection Routes – Municipalities in cities like Pune and Ahmedabad use data-driven insights to optimize waste collection truck routes, minimizing fuel consumption and reducing traffic congestion.
• Recycling Initiatives – Companies like Recykal are leveraging data platforms to manage waste streams more effectively, supporting recycling efforts across various sectors.
• Waste-to-Energy Projects – Smart systems in cities like Surat and Hyderabad use Big Data to monitor waste-to-energy plants, optimizing energy production from solid waste.
• Citizen Participation – Platforms like 'Swachh Bharat' encourage citizens to engage in waste segregation by providing data-driven insights into local waste patterns, fostering community involvement.

3. Energy Optimization

Rising energy demands necessitate efficient power distribution and consumption management. Big Data-driven solutions help minimize energy wastage and enhance sustainability. Smart grids enable real-time monitoring and adaptive energy distribution, reducing operational inefficiencies. 

Key Features

• Smart Grids – IoT-enabled smart grids, powered by platforms like GE Digital, provide real-time data on energy usage, allowing utilities to balance supply and demand dynamically.
• Demand Response Systems – Data analytics models predict energy demand peaks and adjust energy supply accordingly, reducing pressure on the grid and preventing blackouts.
• Energy Consumption Analytics – Platforms like EnergyHub analyze energy usage data, identifying patterns that help businesses and households optimize consumption.
• Renewable Energy Integration – Big Data helps integrate renewable energy sources, such as solar and wind, into grids by forecasting weather patterns and energy generation capacity.
• Automated Building Management – Building management systems use sensor data to control heating, cooling, and lighting in real-time, reducing energy consumption in commercial and residential buildings.

Applications

• Demand Response Programs – Energy companies in Chennai and Bengaluru use predictive analytics to manage peak demand periods, encouraging consumers to reduce consumption during high-demand hours.
• Solar Energy Forecasting – Companies like ReNew Power leverage Big Data to predict solar energy generation based on weather forecasts, improving the integration of renewable energy into the grid.
• Smart Buildings and Campuses – Commercial properties and university campuses in cities like Hyderabad use data-driven systems to optimize lighting, HVAC, and energy consumption for greater efficiency.
• Electric Vehicle (EV) Charging Infrastructure – Data platforms like ChargeGrid optimize the placement and operation of EV charging stations. It ensures availability and efficient energy usage across cities like Pune and Bengaluru.

Also Read: 27 Big Data Projects to Try in 2025 For all Levels

4. Public Safety and Surveillance

Big Data strengthens urban security by enabling predictive crime prevention and enhancing emergency response. AI-driven surveillance systems and data analytics improve law enforcement strategies. Cities utilize predictive policing models to identify and address potential threats proactively.

Key Features

• Real-time Surveillance – AI-driven cameras, like those from Hikvision, analyze live video feeds to detect suspicious behavior and identify potential security threats.
• Predictive Policing – Machine learning algorithms analyze crime data to predict and prevent future criminal activities, improving resource allocation and crime prevention strategies.
• Crowd Monitoring – Data analytics tools like C-CURE and ShotSpotter help monitor large gatherings or public events, ensuring crowd safety and detecting potential threats early.
• Social Media Monitoring – Tools like Dataminr scan social media platforms for potential threats, alerting authorities to public disturbances or emergencies.
• Integrated Emergency Response Systems – Platforms such as Hexagon Safety & Infrastructure integrate multiple data sources to optimize emergency response, reducing time to action during crises.

Applications

• Crime Prediction Models – Police forces in cities like Mumbai use data analytics to predict crime hotspots, allowing law enforcement agencies to allocate resources more efficiently.
• Crowd Control at Major Events – In cities like Hyderabad, data tools are used to monitor crowd movements during religious or public events, ensuring safety and reducing the risk of stampedes.
• Emergency Response Optimization – Platforms like the one used in Chennai enhance coordination between police, fire, and medical teams by providing a real-time dashboard during emergencies.
• Disaster Management – Smart systems in cities like Kochi integrate real-time data from weather stations and sensors to optimize disaster response, such as during floods or earthquakes.

Also Read: Top 50 IoT Projects For all Levels in 2025

5. Smart Healthcare

Big Data revolutionizes healthcare by improving accessibility, optimizing resource allocation, and enhancing patient care. AI-enabled analytics enable early disease detection and effective treatment planning. Predictive healthcare models facilitate better decision-making for medical professionals.

Key Features

• Wearable Health Devices – Wearables like Fitbit and Apple Watch collect real-time health data such as heart rate and activity levels, providing doctors with continuous monitoring for more accurate diagnoses.
• Telemedicine and Virtual Care – Platforms like Practo and mfine allow patients to consult healthcare professionals remotely, making healthcare accessible to people in remote areas.
• AI-assisted Diagnosis – AI tools such as IBM Watson analyze medical imaging and patient data to assist doctors in diagnosing diseases like cancer, enhancing accuracy and speed.
• Predictive Analytics for Disease Management – Big Data tools like Health Catalyst track patient health trends to predict disease progression, allowing timely intervention.
• Electronic Health Records (EHRs) – Cloud-based EHR platforms, like Epic Systems, allow healthcare providers to store and access patient histories securely, enabling more efficient care.

Applications

• Telemedicine for Rural Areas – Platforms like Apollo 24/7 and Practo bring quality healthcare to remote regions of India, enabling virtual consultations and reducing the burden on local healthcare infrastructure.
• AI-driven Diagnostic Tools – Hospitals in cities like Bengaluru and Mumbai are using AI-driven diagnostic tools to detect diseases early, improving survival rates and reducing healthcare costs.
• Personalized Healthcare – Big Data platforms like Portea use patient data to deliver personalized healthcare services, including in-home visits and medical support, tailored to individual needs.
• Disease Surveillance Systems – The Indian government uses Big Data tools to monitor public health trends and manage disease outbreaks such as dengue and COVID-19, implementing early intervention strategies.

Also Read: Complete Guide to Resource Management Projects: Key Steps, Tools, and Strategies

6. Water Resource Management

Big Data-driven water management systems enhance distribution efficiency and minimize wastage. AI-enabled solutions facilitate real-time monitoring and predictive maintenance. Sustainable water resource management ensures a steady supply for urban populations.

Key Features

• Real-time Water Quality Monitoring – IoT-enabled sensors monitor parameters like pH, temperature, and contaminants in water, helping utilities maintain water quality.
• Water Consumption Analytics – Data tools like AquaDemand analyze water usage patterns to optimize water distribution and minimize waste.
• Leak Detection and Prevention – Sensors placed in pipelines detect leaks early, reducing water loss and improving system efficiency.
• Predictive Water Demand – Machine learning models use weather and historical data to forecast water demand, enabling utilities to manage supply more effectively.
• Smart Irrigation Systems – Data-driven irrigation platforms like CropX use soil moisture levels and weather data to optimize irrigation, reducing water wastage in agriculture.

Applications

• Smart Water Metering – Cities like Pune and Delhi have implemented smart water meters that provide real-time data on water usage, helping consumers and municipalities monitor and manage water consumption.
• Water Quality Monitoring Systems – In cities like Chennai, Big Data is used to monitor river and groundwater quality, ensuring safe drinking water and early detection of pollution.
• Leak Detection in Water Networks – Cities such as Hyderabad use data from IoT sensors to identify leaks in water pipelines, reducing water loss and improving network efficiency.
• Agricultural Water Management – In regions like Rajasthan and Punjab, Big Data platforms are used to optimize irrigation schedules based on weather forecasts and soil moisture data, ensuring water is used efficiently.

Also Read: Data Science in Agriculture: How Does it Transform Modern-Day Farming?

7. Smart Housing and Infrastructure

Big Data supports the design and maintenance of efficient, sustainable urban infrastructure. AI and IoT-driven innovations enable smarter city planning and building management. These technologies enhance resource utilization and sustainability in urban environments.

Key Features

• Energy-efficient Smart Buildings – IoT-enabled building management systems monitor and optimize energy consumption for lighting, heating, and cooling, reducing operational costs.
• Automated Home Systems – Platforms like Amazon Alexa and Google Home allow residents to control lighting, appliances, and security systems remotely, improving convenience and energy efficiency.
• Predictive Maintenance – Sensors installed in infrastructure components predict wear and tear, enabling timely maintenance and reducing downtime.
• Urban Mobility Integration – Smart housing solutions integrate with transportation systems, such as bike-sharing and electric vehicles, to ensure seamless mobility and reduce traffic congestion.
• Waste and Water Management Systems – Data platforms track waste production and water usage in buildings, providing insights for better resource management and sustainability.

Applications

• Energy-efficient Buildings – Commercial buildings in Mumbai and Delhi use automated systems to optimize energy consumption, cutting down on electricity bills and reducing carbon footprints.
• Predictive Maintenance in Infrastructure – In cities like Pune, infrastructure management tools analyze data from sensors in bridges and roads, predicting and preventing maintenance issues before they arise.
• Waste and Water Management in Residential Areas – Platforms like Smart Cities Mission enable efficient waste management and water distribution systems in housing complexes in cities like Chennai and Ahmedabad.
• Sustainable Construction – Builders in cities like Noida are using Big Data to streamline construction projects by optimizing resource usage, minimizing waste, and incorporating green technologies.

Also Read: Infrastructure as Code – What is, What is not, Principles

8. Sustainable Mobility Solutions

Big Data optimizes urban mobility systems by reducing congestion and enhancing transport efficiency. AI-enabled analytics support eco-friendly transportation solutions, reducing carbon footprints. Predictive modeling improves transportation planning and implementation.

Key Features

• Electric Vehicle Integration – Data platforms like ChargePoint optimize EV charging networks, ensuring that infrastructure supports electric vehicle growth and reducing emissions.
• Shared Mobility Services – Platforms like Uber and Lyft use data to connect riders with shared mobility options like electric bikes and carpooling, reducing the number of vehicles on the road.
• Public Transport Optimization – Big Data helps cities track public transportation performance, optimizing bus and train schedules and improving commuter experiences.
• Pedestrian and Bicycle Infrastructure – Data analysis helps cities design safer and more efficient pedestrian and cycling networks, promoting sustainable mobility modes.

Applications

• Smart Traffic Management in Cities – Bengaluru and Mumbai are adopting AI-powered traffic management systems that use real-time data to reduce congestion, improve traffic flow, and minimize emissions.
• Shared Mobility Services – Ride-sharing services in cities like Hyderabad and Chennai leverage Big Data to offer electric bike rentals and carpooling options, helping reduce the number of vehicles on the road.
• Public Transportation Improvements – In cities like Kolkata and Ahmedabad, data analytics are used to optimize bus and metro schedules, reducing waiting times and improving service reliability for commuters.

Also Read: Predictive Modelling in Business Analytics: Detailed Analysis

9. Citizen Engagement and E-Governance

Big Data fosters transparent governance and increased citizen participation in policy-making. AI-driven solutions improve public service accessibility and engagement. Open data initiatives enhance accountability and decision-making processes.

Key Features

• Real-time Feedback and Communication – Platforms like Citizen Feedback Systems allow governments to collect and analyze citizen input, enhancing transparency and improving service delivery.
• Digital Platforms for Public Services – Tools such as the Digital India portal provide citizens with easy access to essential services, reducing bureaucracy and making government services more accessible.
• Data-driven Decision Making – Big Data helps governments analyze trends and public needs, allowing for more informed and effective decision-making.
• Public Awareness and Education – E-governance platforms use Big Data to tailor information campaigns and increase public awareness on crucial issues such as health, safety, and legal rights.
• Citizen Participation and Voting – Digital platforms enable citizens to engage in decision-making processes, participate in e-voting, and provide feedback on policies.

Applications

• Digital India Initiative – The Digital India program uses Big Data to provide online services such as government benefits, education, and health records to citizens in urban and rural areas.
• Swachh Bharat Mission – Platforms like Swachhata App use real-time data from citizens to report cleanliness issues, improving waste management and public sanitation in cities like Delhi and Pune.
• E-Voting Systems – In states like Maharashtra and Andhra Pradesh, e-voting platforms use Big Data to ensure more accessible, transparent, and secure voting systems.
• GovTech Solutions for Public Services – In cities like Bengaluru and Chennai, Big Data is used to streamline government services such as license renewals, permits, and tax collections.

Also Read: Big Data Career Opportunities: What to Expect in 2025?

10. Disaster Management and Resilience

Big Data enhances disaster preparedness, enabling faster emergency response and infrastructure resilience. Big Data strengthens disaster preparedness by enhancing predictive analytics and emergency response strategies. AI-driven systems improve resilience against natural disasters and crises.

Key Features

• Real-time Monitoring and Alerts – Big Data platforms collect and analyze data from weather satellites and IoT sensors, providing early warning systems for events like floods, hurricanes, and earthquakes.
• Predictive Analytics for Disaster Response – Using historical data, predictive models can anticipate disaster events, helping authorities prepare and allocate resources more efficiently.
• Geospatial Data Integration – GIS tools integrate real-time geospatial data with disaster management platforms, helping responders navigate and prioritize areas in need of assistance.
• Resource and Relief Distribution – Big Data helps manage the distribution of relief supplies, tracking inventory and ensuring timely delivery to affected regions.
• Post-disaster Recovery Insights – Analytics platforms provide insights into the impact of disasters, aiding governments in designing recovery plans and improving future resilience.

Applications

• Flood Forecasting Systems – The Central Water Commission uses Big Data from meteorological data and river monitoring systems to predict flood events and warn affected communities.
• Cyclone Warning Systems – The Indian Meteorological Department (IMD) leverages Big Data to provide early warning alerts for cyclones, helping governments evacuate vulnerable populations in coastal areas.
• Earthquake Monitoring and Preparedness – India’s National Center for Seismology uses Big Data from seismic sensors to track earthquakes, offering real-time data to inform disaster response teams.
• Disaster Relief Distribution Networks – Platforms like the National Disaster Management Authority use Big Data to monitor the availability and distribution of relief materials such as food, water, and medical supplies during major disaster events.

Also Read: What is Disaster Management? Importance, Stages, Guidelines, and More

11. Financial Technology (FinTech) Integration

Big Data improves digital payment ecosystems, ensuring secure transactions and fraud detection in smart cities. Big Data enhances financial systems by improving security, fraud detection, and transaction efficiency. AI-driven analytics ensure seamless digital payments and risk assessment.

Key Features

• Real-time Fraud Detection – Data platforms continuously monitor financial transactions to detect and prevent fraudulent activities in real-time.
• Predictive Analytics for Investment – By analyzing historical financial data, predictive models provide investors with insights to make more informed decisions on stock trading and wealth management.
• Blockchain and Cryptocurrency – Big Data enhances the transparency, security, and scalability of blockchain systems, contributing to the growth of cryptocurrency markets.
• Automated Financial Advisory – Robo-advisors powered by Big Data help individuals manage their investments automatically, using algorithms to optimize asset allocation based on data-driven insights.

Applications

• Digital Payment Solutions – Platforms like Paytm and PhonePe leverage Big Data to offer secure, fast, and convenient digital payment options for both consumers and businesses across India.
• Personal Finance Management – Apps like Moneycontrol and ET Money use Big Data to provide users with personalized budgeting tools, investment advice, and portfolio management services.
• Online Lending Platforms – Startups like Lendingkart and Capital Float use Big Data to assess the creditworthiness of borrowers and provide microloans to small businesses and individuals in need.

12. Smart Education and Workforce Development

Big Data enhances personalized learning and workforce planning, ensuring adaptive education solutions and future-ready skills. Big Data facilitates adaptive learning and workforce planning by providing personalized education experiences. AI-driven insights improve curriculum design and future skill development.

Key Features

• Personalized Learning Paths – Data platforms analyze students' learning behaviors and preferences to tailor educational content, creating individualized learning experiences.
• Real-time Performance Analytics – Teachers and trainers can monitor student or employee progress in real-time, adjusting teaching methods to address specific learning gaps.
• Skills Gap Analysis – Big Data helps organizations identify workforce skill gaps, allowing them to design targeted training programs for employees to remain competitive.
• E-Learning and Remote Education – Online learning platforms such as Coursera and Udemy use Big Data to recommend courses based on past learning patterns, ensuring continuous skill development.
• Workforce Analytics for HR – HR departments use Big Data to analyze employee performance, optimize recruitment processes, and design personalized development plans.

Applications

• Online Learning Platforms – Platforms like Byju's and Vedantu use Big Data to personalize learning experiences, offering adaptive learning paths that cater to individual student needs across diverse subjects.
• Skill Development Initiatives – The Government of India’s Skill India program uses Big Data to monitor skill gaps and offer targeted vocational training to improve employment opportunities in various sectors.
• Corporate Training and Development – Companies like Tata Consultancy Services (TCS) and Infosys use data analytics to offer customized training programs that help employees enhance their skills in emerging technologies.

Also Read: Future Scope in Education: Current Scenario, Expectations & Technology

Before diving into the challenges and best practices, let’s explore how Big Data is shaping key sectors in smart cities.

Implementation of Big Data in Smart Cities: Challenges and Best Practices

Data privacy concerns, high infrastructure costs, and lack of technical expertise often hinder adoption. Additionally, fragmented data sources and interoperability between different systems create further complications in urban management.

To overcome these barriers, cities need structured approaches and well-defined strategies. Below are key challenges and best practices for successfully implementing Big Data solutions in Smart Cities.

Challenges in Implementing Big Data in Smart Cities

• Data Privacy and Security – Protecting citizens' data from cyber threats and unauthorized access remains a major concern.
• Infrastructure Costs – Developing smart city infrastructure requires heavy investments in sensors, cloud computing, and advanced analytics.
• Data Integration Issues – Fragmented data from various sources, such as IoT devices and city departments, create difficulties in analysis.
• Lack of Skilled Workforce – Managing Big Data requires professionals in data science, AI, and cloud computing.
• Regulatory and Compliance Challenges – Cities must adhere to data protection laws and maintain transparency in data usage.

Best Practices for Overcoming Big Data Challenges

• Implement Strong Data Governance Policies – Establish clear guidelines for data collection, storage, and sharing to ensure security.
• Adopt Scalable Cloud Solutions – Cloud-based platforms provide cost-effective storage and processing power for large data sets.
• Use AI and Machine Learning for Insights – AI-powered analytics enhance decision-making in traffic, energy, and emergency management.
• Encourage Public-Private Partnerships – Collaborating with tech companies and startups can accelerate smart city initiatives.
• Develop Citizen Engagement Platforms – Open data platforms allow residents to access and contribute to city planning.

Let's now explore how Big Data shapes the overall development of Smart Cities in various sectors.

How Does Big Data Shape the Development of Smart Cities?

Big Data plays a fundamental role in urban planning, governance, and infrastructure, ensuring it operates more efficiently. By integrating data from multiple sources, including IoT sensors, GPS tracking, and AI analytics, municipalities can optimize waste management, healthcare services, and public safety. 

Let's now explore how Big Data enhances smart governance and citizen engagement.

How Does Big Data Enhance Smart Governance and Citizen Engagement?

Smart governance utilizes Big Data to improve policy-making and public service delivery. Governments use AI and analytics to make data-driven decisions that enhance urban management and citizen engagement.

Below are key ways in which Big Data is transforming governance:

• Data-Driven Policymaking – Authorities use real-time data analysis to implement evidence-based policies for better urban planning.
• AI-Powered Chatbots – Automated platforms provide real-time solutions for citizen queries, reducing response time in public services.
• Citizen Feedback Analysis – Data analytics assess public sentiment and improve service delivery through community engagement platforms.

Let's now examine how AI and IoT improve Smart City data analytics.

How Do AI and IoT Improve Smart City Data Analytics?

AI and IoT enhance urban management by providing real-time insights and predictive analytics for various smart city applications.

Below are the key contributions of AI and IoT in smart city data analytics:

• AI-Driven Insights – Machine learning models analyze traffic, pollution, and resource consumption to optimize urban management.
• IoT in Data Collection – Smart sensors collect real-time data on energy usage, water supply, and air quality for efficient resource distribution.
• Integrated AI-IoT Systems – AI processes IoT data to predict and prevent issues, such as traffic congestion and infrastructure failures.

How Can upGrad Help You Build a Career in Big Data?

Big Data is transforming finance, healthcare, and e-commerce, creating demand for skilled professionals. To enter this field, upGrad’s Big Data programs offer real-world projects and hands-on experience with Hadoop, Spark, and Python.

Here are some upGrad courses that can help you stand out.

• Master of Science in Data Science 
• AI and Data Science
• Professional Certificate Program in Digital Marketing
• Data Structures & Algorithms
• Data Science in E-commerce

Still unsure about the right path in big data? Speak to upGrad’s expert counselors for personalized career guidance. For more details, visit the nearest upGrad offline center.

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Reference Links:
https://www.business-standard.com/content/press-releases-ani/india-s-urban-population-to-reach-600-million-by-2036-primus-partners-report-125021100644_1.html
https://www.sganalytics.com/blog/iot-smart-cities/ 
https://krct.ac.in/blog/2024/09/03/the-role-of-big-data-analytics-in-shaping-smart-cities/
https://www.globenewswire.com/news-release/2021/07/12/2260896/0/en/Global-Smart-Cities-Market-to-Reach-2-5-Trillion-by-2026.html