EarthScape Climate Agency — eProject (Hadoop Climate Analytics)

eProject: Hadoop-Powered Climate Analytics Platform

A step-by-step, laddered learning & implementation environment that mirrors classroom + lab workflows — now live on your machine. Process, analyze, and visualize climate data at scale with Hadoop, HDFS, MapReduce, and real-time streaming.

Goal

Actionable climate insights

Tenet

Learn by building

Uptime

≥ 99%

1. Introduction

Laddered approach • Live implementation • Classroom ↔ Lab simulation

The industry expects IT professionals to learn fast, upgrade skills, and apply concepts in real environments. Busy schedules, location constraints, and time slots often hinder practice, especially for students in dynamic domains like IT where technology shifts rapidly.

To overcome these barriers, this eProject provides a live, synchronous learning environment that simulates classroom and lab experience in a practical, phased (laddered) manner. You will practice step-by-step, build toward a unified application, and receive mentoring via email support. Submit complete documentation and source code upon completion.

What you will do (high-level)

Practice step-by-step, laddering tasks into a complete app.
Build a robust climate analytics platform with Hadoop.
Use utilities and unify modules into cohesive source code.
Implement concepts in phases, enhancing skills and value.
Work on a real-life climate scenario with meaningful outputs.

2. Objectives

Build a real-life project • Not a concept course • Implementation-first

Provide a realistic climate analytics problem to solve using the tools.
Encourage revisiting concepts independently, then applying them in implementation.
Ensure clarity of subject matter and outcomes before development.

Having doubts? Reach out to the eProjects Team for clarifications on application goals or scope.

3. Problem Statement

Background: Climate change requires deep analysis and proactive mitigation. Rising GHG emissions, deforestation, and land-use changes have shifted climate patterns, increasing extreme weather events. To address this, the EarthScape Climate Agency must process vast data from satellites, weather stations, and sensors to derive insights.

Need: A robust solution for ingesting, storing, processing (batch + real-time), modeling, and visualizing climate data at scale. Hadoop and its ecosystem (HDFS, MapReduce, streaming) are central to meeting these requirements.

Functional Requirements

Track progress as you implement. (Saved locally.)

User Authentication & Authorization
Secure auth; roles (admins, analysts); role-based access control.

Data Ingestion
Support satellites, stations, sensors; handle historical + real-time; accept common climate formats.

Data Storage
Use HDFS for scalable, fault-tolerant storage; partition & organize for efficient retrieval.

Batch Data Processing
Hadoop MapReduce for parallel processing; detect patterns, anomalies, correlations; handle missing data gracefully.

Real-time Processing
Integrate stream ingestion/processing and harmonize with batch pipelines.

Machine Learning Models
Predictive trends & impacts; anomaly detection; correlation analysis; periodic model refresh.

Data Visualization
Interactive dashboards; visual patterns/anomalies/predictions; user-friendly exploration.

Notifications & Alerts
Automated alerts on thresholds and climate events; configurable channels.

Feedback & Support
User support to report issues, request help, and submit feedback.

Non-Functional Requirements

Performance
Monitor system performance, resource use, and processing time; optimize pipelines.

Data Security
Encryption at rest & in transit; compliance with data protection standards.

Reliability
≥ 99% uptime; scheduled maintenance; automated backups.

Scalability
Horizontal scaling; load balancing for optimal utilization.

Compliance & Standards
Follow environmental data standards; big-data best practices.

Documentation
User guides, FAQs, tutorials; developer docs for architecture, pipelines, models.

System Architecture (Hadoop-Centric)

HDFS • MapReduce • Streaming • Impala • Dashboards • Alerts

High-level components

Ingestion: Batch loads (historical archives), streaming (Kafka/Flume) from sensors/stations.
Storage: HDFS with partitioning (e.g., /climate/<year>/<month>/<day>/<source>).
Batch Processing: MapReduce jobs for ETL, quality checks, anomaly detection.
Real-time: Stream processing to update anomaly flags & short-term aggregates.
Serving: Impala/Hive for ad-hoc queries; curated datasets for dashboards.
ML: Model training pipelines; scheduled retraining; model registry & metrics.
Viz & Alerts: Tableau/custom dashboards; alert engine with thresholds & subscribers.

Data quality & missing data handling

Schematize inputs; reject/quarantine malformed records.
Impute missing values via last-observation, interpolation, or model-based where appropriate.
Attach quality flags (per sensor, per variable) for downstream consumers.

Security & governance

Encryption at rest (HDFS KMS) and in transit (TLS).
Role-based access; audit logs; dataset classification (public, internal, restricted).
Backups & disaster recovery (DR) with periodic restore drills.

Hardware / Software Requirements

Hardware

Component	Spec
CPU	Min i5 (4 cores); i7 recommended
Memory	16 GB RAM
Storage	500 GB SSD
GPU	Optional graphics card
OS	64-bit Windows 10 or higher

Software

Tool	Purpose
Jupyter (Anaconda) 3	Notebooks & exploration
RStudio	Stat analysis / plots
VS Code / PyCharm	Development IDE
MongoDB Compass & Shell	Auxiliary NoSQL storage
Hadoop, HDFS, Apache	Core big-data stack
Tableau	Dashboards
Impala server	Fast SQL on Hadoop

Installation & Setup (Quickstart)

Install prerequisites: JDK 8+, Python 3.x (Anaconda), Hadoop (HDFS + MapReduce), Impala, Kafka (optional for streaming).
Configure HDFS: Initialize NameNode; create base dirs like /data/raw, /data/curated, /models.
Ingestion: Batch loaders (e.g., distcp / custom ETL) and Kafka topics for sensors: climate.readings.
Processing: Deploy MapReduce jobs for cleansing, anomaly tagging, and feature building.
Serving: Expose curated tables in Hive/Impala for dashboards and ad-hoc analytics.
ML: Schedule training (cron/Airflow); store artifacts in /models with version tags.
Dashboards: Connect Tableau to Impala/Hive; publish views for anomalies, trends, and alerts.
Alerts: Implement threshold rules and notification channels (email/webhook).

# Example: create HDFS layout
hdfs dfs -mkdir -p /data/raw/sensors
hdfs dfs -mkdir -p /data/raw/satellites
hdfs dfs -mkdir -p /data/curated
hdfs dfs -mkdir -p /models

# Example: register external Hive table (CSV)
CREATE EXTERNAL TABLE climate_raw (
  station_id string, ts timestamp, temp_c double, rh double, wind_ms double
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.OpenCSVSerde'
LOCATION '/data/raw/sensors';

Project Deliverables

Complete Project + Source Code
Project Report including:
- Problem Definition
- Design Specifications
- Flowcharts, DFDs
- Test Data used
- Installation Instructions
ReadMe.doc / README.md (use the “README.md” button to export from this page)
Video demonstrating full application working (see placeholder below)
Optional: Live hosted URL

Over and above the specs, apply your creativity and logic to enhance the portal. Documentation quality matters.

Demo Video (Placeholder)

Replace the source with your recorded demo.

Tip: Record a guided tour: ingestion → processing → ML → dashboards → alerts.

Feedback & Support

Use the built-in feedback mechanism of your platform or email the eProjects Team. Capture issues with steps, logs, and dataset sample (if allowed).

📧 Mentoring via email

🐞 Issue reports with logs

💡 Feature suggestions welcome