Real-Time Social Media Sentiment Analytics Dashboard Using Databricks Lakehouse Architecture

Sumitra Sahoo
Department of Master of Computer Applications

GIFT Autonomous, Bhubaneswar, Odisha, India, debasmitab2024@gift.edu.in

Smruti Ranjan Swain
Head of Department Master of Computer Applications

GIFT Autonomous, Bhubaneswar, Odisha, India, hodmca@gift.edu.in

Abstract-Telecommunication networks generate a massive volume of data every day in the form of call records, network traffic, signal strength, data usage, latency, and service quality metrics. Analyzing this large amount of telecom data is essential for monitoring network performance, identifying service issues, improving customer experience, and optimizing resource utilization. This paper presents a Telecom Network Performance Dashboard developed using the Databricks Lakehouse Platform and Medallion Architecture for scalable telecom data processing and analysis.

The proposed system processes telecom datasets through Bronze, Silver, and Gold layers to improve data quality, perform transformation, and generate meaningful analytical insights. Interactive dashboards are used to visualize network traffic distribution, call drop analysis, signal strength trends, regional performance comparison, data usage patterns, and overall network efficiency using various charts and analytical reports.

The developed system helps telecom operators, network analysts, and decision-makers identify performance bottlenecks, monitor network stability, detect service disruptions, and support data-driven operational decisions. Furthermore, this work demonstrates how modern big data technologies and visualization tools can be effectively utilized for intelligent telecom network monitoring and performance management.