Real-Time Taxi Fare Calculation
This project, developed as part of a learning initiative, showcases a back-end platform that calculates taxi fares in real time. It leverages advanced technologies such as Apache Kafka for data streaming, ElasticSearch for indexing and monitoring, and Google BigQuery for clustering and revenue analysis. The platform provides dynamic fare estimation based on the distance between drivers and customers, factoring in selected comfort levels and geographic clusters. Designed with scalability and efficiency in mind, this project demonstrates innovative solutions for real-time data processing and insightful revenue visualization.
BIGQUERYELASTICSEARCHKIBANAKAFKAGCPAPACHE-NIFI
1 min read
Main Features
Technology Stack
Programming Language: Python
Streaming & Messaging: Apache Kafka
Indexing & Monitoring: ElasticSearch, Kibana
Data Warehouse & Analytics: Google BigQuery, BigQuery ML
Data Transformation: Apache Nifi, Java Custom Processor
Data Format: Parquet
Visualization: Looker Studio
Real-Time Fare Calculation: Calculates taxi fares dynamically based on distance, comfort level, and geographic clustering.
Streaming Data Processing: Utilizes Kafka for real-time data ingestion and processing of driver and customer location data.
Clustering and Revenue Analysis: Employs BigQuery ML for K-Means clustering to analyze revenue distribution by geographic cluster and comfort level.
ElasticSearch Integration: Enables efficient data indexing, performance monitoring, and real-time visualization via Kibana.
Scalable Data Warehousing: Stores over 10,000 records in .parquet format using Google BigQuery for further analysis and reporting.
Comprehensive Data Visualization: Insights are presented in Looker Studio, offering clarity on clustering results and revenue trends.