The Social Media Hashtag Analytics is a Big Data project that combines Trend detection and Data export, built with Elasticsearch. 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 Data export and Elasticsearch, 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 Trend detection, orchestrated with Elasticsearch and Data export. 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.
Elasticsearch
Streamlit
Python / Java
Distributed storage and processing
SQL and NoSQL stores
Logging and monitoring
Python
Tweepy
Pandas
Modular data pipeline around Trend detection and Data export
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 Trend detection; the processing layer applies Big Data tools with Elasticsearch and Data export; and the output layer formats and presents results via API rate limiting. 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 Trend detection.
Implement the core Big Data pipeline using Elasticsearch and Data export.
Add the output and presentation layer via API rate limiting.
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 Building insight dashboards and Collecting social data via APIs
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 Social Media Hashtag Analytics 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.
