The Streaming ETL Pipeline with Flink is a Big Data project that combines Stream processing engine and Live dashboard, built with Spark Streaming. The project follows a clean, modular pipeline where data ingestion, processing, and presentation stay separated, making it easy to test, extend, and present. It showcases practical Big Data techniques while producing a working, demo-ready application.
Traditional tools struggle to handle the volume, velocity, and variety of this data, making analysis slow and expensive. Without a Big Data approach built on Live dashboard and Spark Streaming, users cannot process and analyze large datasets efficiently, and there is no scalable way to derive timely insights.
This project applies Big Data techniques through Stream processing engine, orchestrated with Spark Streaming and Live dashboard. The pipeline is designed for scale and reliability, with ingestion, processing, and clear evaluation. It produces consistent, reusable results and can be adapted to related large-scale tasks with minimal changes.
Spark Streaming
Zookeeper
Python / Java
Distributed storage and processing
SQL and NoSQL stores
Logging and monitoring
Apache Kafka
Kafka Connect
Modular data pipeline around Stream processing engine and Live dashboard
Configurable processing and storage settings
Clear logging, metrics, and error handling
Clean interface for viewing results
Reusable components for related Big Data tasks
Scalable to larger datasets
The project is layered: the ingestion layer loads and validates data through Stream processing engine; the processing layer applies Big Data tools with Spark Streaming and Live dashboard; and the output layer formats and presents results via Kafka producer. Shared configuration, logging, and monitoring modules support all layers, keeping the system robust and easy to extend.
Set up the environment, cluster, and configuration files.
Build the data ingestion and preprocessing layer with Stream processing engine.
Implement the core Big Data pipeline using Spark Streaming and Live dashboard.
Add the output and presentation layer via Kafka producer.
Wire up end-to-end flows and add error handling and logging.
Run on realistic data, tune parameters, and evaluate results.
Package the project, document it, and prepare the demo and viva report.
Build production-style Big Data applications
Apply Setting up Kafka clusters and Producing and consuming streams
Process and analyze real large-scale datasets
Work with popular Big Data tools and frameworks
Present and defend a complete Big Data project in viva
Move the pipeline to cloud infrastructure
Add more data sources and streaming support
Add advanced analytics and machine learning models
Deploy with auto-scaling for larger workloads
The Streaming ETL Pipeline with Flink delivers a complete Big Data workflow — from data ingestion and processing to analysis and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand Big Data skills.
