Weather data map reduce programming

1. WEATHER CONDITION ANALYSIS
USING MAPREDUCE
Presented by:
Anagha R 1DS22IS017
Ananya B A 1DS22IS019
Ananya Olekar 1DS22IS020
Anusha R 1DS22IS025

2. HADOOP
What is Hadoop?
• An open-source framework for storing and processing large-scale data
• Developed by the Apache Software Foundation
• Designed to run on clusters of commodity hardware
Why Hadoop:
Manages the 3Vs of Big Data:
• Volume – Handles petabytes of data
• Velocity – Processes data quickly using parallel computing
• Variety – Supports diverse data types (text, images, videos, logs)

3. OBJECTIVE & APPLICATIONS
Objective:
To develop a MapReduce program that processes large-scale weather data and displays weather conditions of
the day, such as "Hot Day," "Cold Day," or "Rainy Day," based on temperature, humidity, or precipitation
records.
Applications:
• Real-time weather monitoring
• Historical weather trend analysis
• Disaster preparedness and alerts
• Integration into agricultural or logistic planning systems

4. CODE

5. OUTPUTS
Expected output:
2025-04-01 Hot Day
2025-04-03 Cold Day
Actual output:

6. CONCLUSION
• Implemented a MapReduce program to analyze and classify daily weather data
• Efficiently processed large datasets using Hadoop’s distributed computing model
• Accurately identified weather conditions like Hot Day and Cold Day
• Showcased the power of parallel processing with Mapper and Reducer functions
• Demonstrated a practical use-case of Big Data in weather analysis
• Project is scalable and can be extended with real-time data and predictive analytics

7. THANKYOU