Data-Driven Innovations at Maha Kumbh 2025

The Maha Kumbh 2025 in Prayagraj is a significant spiritual event, as I mentioned in my earlier blog - "Maha Kumbh 2025: Tradition Meets Technology". Over 500 million devotees from across the globe participated till date, marking it as the largest religious assembly in history. This year is especially notable due to a rare alignment of celestial bodies that happens only once every 144 years, where the positions of Jupiter, the Sun, and the Moon generate strong spiritual energy, creating an exceptionally favorable time for worshippers. To accommodate the enormous number of pilgrims, authorities utilized cutting-edge IT technologies (DIP - Digital Public Infrastructure) and thorough planning, including advanced surveillance, a centralized command control center, AI-powered management, and the involvement of Teerth Purohits throughout the temporary city that covers 4,000 hectares and houses 150,000 tents. This truly showcases India's capability to organize large-scale events and reflects the country’s rich cultural legacy.

Data played a crucial role in ensuring that Maha Kumbh 2025 is safe, efficient, and sustainable. From managing crowds to addressing healthcare needs, infrastructure, and environmental practices, the strategic use of data illustrated how large events can be organized and conducted effectively. As the event is nearing its conclusion, I wanted to share insights into the tools, technologies, and software platforms that powered this remarkable spiritual gathering.

The Maha Kumbh incorporated various technologies to improve the experience for millions of pilgrims. Together, these advancements transformed the Maha Kumbh into a "Digital Kumbh," merging tradition with innovation to create a more engaging atmosphere for all participants. The data regarding visitor attendance at Maha Kumbh 2025 is incredibly accurate and dependable, thanks to the use of advanced tools and technologies (DIP- Digital Public Infrastructure). These tools enable real-time monitoring and analysis, minimizing potential human error. The data has been cross-verified through multiple sources, including manual counts and transportation records, to ensure a thorough and trustworthy estimate. Although no method can guarantee complete accuracy, the integration of these technologies significantly boosts the precision of visitor counts.

• They implemented AI-Driven Surveillance Systems featuring 2,700 AI-enhanced cameras linked with IoT devices that monitor crowd density and movement in real-time. These cameras utilize computer vision technologies such as YOLO (You Only Look Once) and Faster R-CNN for tasks like object detection, facial recognition, and activity identification.
•  They utilized drone technology that included high-definition aerial drones with AI capabilities for real-time surveillance, underwater drones for continuous monitoring of water bodies, tethered drones to extend flight durations, anti-drone systems to incapacitate unauthorized drones, and remote-controlled life buoys for quick rescue efforts.
•  To monitor the movement and distribution of individuals within the defined boundaries of the event Mela, they employed GPS, geo-fencing, and geospatial technology. Geographic Information Systems (GIS) were combined with Google Maps for real-time navigation and crowd oversight, while geo-fencing allowed tracking of individuals entering and exiting through virtual boundaries. Satellite imaging facilitated the estimation of crowd density and movement patterns, while infrared and ultrasonic sensors measured crowd size and flow. Additionally, wearable GPS devices and GPS-enabled smartphones of attendees provided crucial data on their location and movement trends. Geospatial analytics software processed this information to produce heat maps and flow patterns, indicating areas of crowd concentration and movement throughout the event. 
• Mobile network providers partner with event coordinators to monitor the number of active mobile devices within the Kumbh Mela venue. This is achieved by analyzing data from mobile towers, which detect the number of connected devices and calling patterns in the vicinity. By compiling this information, officials can estimate the crowd size at the event. Given that most attendees carry mobile phones, this approach offers a dependable measure of attendance. Furthermore, mobile applications created for the event can gather anonymized location data from users who consent, enhancing the accuracy of the estimates.
• Transportation authorities keep comprehensive records of pilgrim arrivals through various transportation methods, such as trains, buses, and personal vehicles. Information from ticket sales, passenger totals, and vehicle entries is collected to estimate the number of visitors attending the Mela. To handle the high volume of pilgrims, special provisions like extra train services and shuttle buses are organized. By examining transportation data, event planners can assess overall attendance and prepare logistical support as needed.
• On-site surveys and manual counts are carried out at crucial entry and exit points. Volunteers and officials are positioned at these locations to tally the individuals coming into and leaving the Mela grounds. This information is verified against other data sources to ensure precision. Manual counts are especially beneficial for corroborating the estimates generated by artificial intelligence and geospatial technologies, resulting in a thorough and accurate assessment of visitor numbers.

The Unified Data Platform at the event served as the central hub for collecting, integrating, and analyzing data from various sources in real-time. It employed a range of software and tools to ensure seamless data integration and analysis. Key tools included Apache Kafka for real-time data ingestion and processing, Apache Spark for big data processing and analytics, and the Elastic Stack (Elasticsearch, Logstash, Kibana) for real-time search, log management, and visualization. The Hadoop ecosystem, including HDFS and MapReduce, provided distributed storage and processing capabilities. Tableau is used for data visualization, creating interactive dashboards and reports. AWS Glue, a fully managed ETL service, facilitated data preparation and integration.

 

For Data Fusion and Integration, several advanced software and tools were employed to ensure seamless and efficient handling of data from multiple sources. Key tools included Apache NiFi, an open-source data integration tool for real-time data movement and transformation, and Talend Data Integration, a robust ETL platform supporting both batch and real-time data integration. Informatica PowerCenter provided advanced ETL functionality and automation, while Microsoft Azure Data Factory enabled building, orchestrating, and monitoring data pipelines across multiple cloud and on-premises sources. IBM InfoSphere DataStage offered high-speed parallel processing for large data workloads, and Google Cloud Data Fusion facilitated the creation and management of data pipelines with minimal coding. These tools collectively ensured efficient data fusion and integration by providing comprehensive data movement, cleaning, and visualization capabilities.

 

For decision making and response management they deployed few software’s like IBM Watson provided predictive insights and natural language processing capabilities, helping authorities make data-driven decisions by analyzing vast amounts of data in real-time. Microsoft Power BI offered interactive visualizations and real-time dashboards, allowing authorities to monitor key metrics and track crowd movements. Tableau enabled real-time data visualization through interactive and shareable dashboards, while Splunk helped identify patterns, diagnose issues, and provide actionable insights. ArcGIS provided real-time geospatial data visualization, aiding in understanding crowd density and movement patterns, and AWS Lambda allowed real-time data processing and automated response actions.

 

Lastly, to ensure that data collection sources, the Unified Data platform, Data Fusion & Integration, and decision-making and response management function smoothly, they implemented SAS Viya, a contemporary and scalable platform designed for AI, analytics, and data management. They extensively utilized its Data Connectors and Data Connectors Accelerators for data access and loading, while the centralized platform facilitates the complete data and AI lifecycle with automated ETL processes, data quality management, and robust data handling. This integration converts raw data into valuable insights.

 

In wrapping up this blog, I can assert that the Maha Kumbh 2025, taking place in Prayagraj and referred to as the "Digital Maha Kumbh," exhibited impressive technological progress. This included AI-driven crowd control, drones monitoring the event, and the BHASHINI initiative aimed at facilitating multilingual communication, all of which contributed to the safety and experience of millions of attendees. However, a tragic stampede underscored the necessity for better safety protocols and greater transparency. This incident revealed the transformative potential of data-driven innovations for large gatherings, establishing a new benchmark by effectively merging tradition with advanced technology.

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