Build a Real-Time Data Pipeline 🚀 | Kafka + Spark Streaming + ELK Stack Project
🚀 In this video, I present my mini project:
“Real-Time Analytics Dashboard using Apache Kafka, Spark Streaming, Elasticsearch, and Kibana.”
This project demonstrates how modern big data technologies can be integrated to build a powerful real-time data processing pipeline for monitoring web application logs.
🔧 Technologies Used
Apache Kafka (Data Ingestion)
Apache Spark Structured Streaming (Real-Time Processing)
Elasticsearch (Data Storage & Indexing)
Kibana (Data Visualization)
Python (Log Generator)
Docker (Containerization)
📊 Project Overview
A Python-based producer generates simulated web log data, which is streamed through Kafka.
Spark Structured Streaming processes the data in real-time and stores it in Elasticsearch.
Finally, Kibana dashboards visualize key insights such as:
📌 Endpoint Traffic Analysis
📌 Status Code Distribution
📌 Response Time Monitoring
📌 Error Heatmaps
⚡ Key Features
✔ Real-time data processing
✔ Scalable and fault-tolerant architecture
✔ Interactive dashboards
✔ Near real-time updates (low latency)
📈 Results
Successfully processed streaming data with minimal delay
Achieved dynamic dashboard updates
Identified traffic patterns, errors, and performance metrics
🔮 Future Enhancements
Machine Learning for anomaly detection
Cloud deployment (AWS/Azure/GCP)
Real-world data integration
Alerting system for errors
🎓 About This Project
This project was developed as part of my B.Tech Computer Science Engineering mini project.
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