In as we speak’s data-driven world, real-time climate insights are crucial for decision-making particularly in agriculture, aviation, and catastrophe preparedness. On this weblog, I share my journey of constructing a Climate Danger Intelligence Dashboard.
This undertaking showcases methods to design and implement a full end-to-end information pipeline structure utilizing open climate APIs, Apache Airflow, Apache Spark, Elasticsearch, and Kibana.
The purpose was to create a Large Information pipeline that:
- Ingests information from two completely different sources through REST APIs
- Cleans and transforms the info for evaluation
- Combines historic and real-time climate information
- Exposes the leads to a user-friendly Kibana dashboard
What made my undertaking distinctive?
I created a customized climate danger degree metric that classifies temperature readings into NORMAL or HIGH danger, mixing historic accuracy with dwell updates.
Ingestion (Python Scripts + REST API), Workflow Orchestration (Apache Airflow), Information Transformation (Apache Spark), Information Storage (Native Information Lake -Parquet recordsdata), Indexing (Elasticsearch), Dashboarding (Kibana).
Information Pipeline Overview
The pipeline is managed through a single Airflow DAG with each day scheduled runs:
- Ingestion:
- Transformation:
- Mixture:
- Indexing & Visualization
A number of the superior KPIs and visualizations I created embrace:
- Each day Danger Stage Monitor: Classifies areas as “NORMAL” or “HIGH” danger based mostly on temp > 35°C
- Max & Min Temperature Traits: Time collection line charts utilizing NOAA information
- Actual-time Snapshot of Lagos Climate: Fused OpenWeather dwell information
- How one can combine real-time + historic datasets
- Palms-on mastery of Airflow DAG design and scheduling
- How one can index and discover semi-structured information in Elasticsearch
- Information transformation utilizing Spark is scalable and clear
- Deploy the undertaking on AWS/GCP utilizing S3 for a real Information Lake
- Use Kafka for real-time ingestion as an alternative of Airflow
- Implement anomaly detection ML fashions on climate patterns
- Add a frontend for public entry to the dashboard
This undertaking was a sensible software of some issues I’ve realized about Large Information: combining ingestion, transformation, and visualization to create real-world worth. It gave me deep publicity to production-grade information engineering and helped me recognize the facility of becoming a member of a number of datasets to make choices.
