Telecom Big Data Analytics Platform

### Telecom Big Data Analytics Platform

Developed a complete **Telecom Big Data Analytics Platform** designed to collect, process, analyze, and visualize large-scale telecommunications data such as **Call Detail Records (CDR), internet usage, SMS activity, customer usage, recharge transactions, network KPIs, and complaints**.

The project demonstrates an end-to-end Big Data pipeline, starting from data ingestion and storage through processing, analysis, and interactive visualization.

**Key Features:**

* Collected and processed telecom datasets containing customer and network activity.

* Used **Apache Kafka** for real-time data ingestion and streaming.

* Stored large datasets using **HDFS** as the distributed storage layer.

* Used **Apache Spark / PySpark** for data cleaning, transformation, and analytics.

* Used **Spark Streaming** for processing real-time telecom data.

* Used **Apache Hive** for structured data storage and SQL-based analysis.

* Used **HBase** to store and access real-time or abnormal usage records.

* Built analytics for **customer usage, peak hours, data consumption, active cells, revenue, and customer segmentation**.

* Identified potentially **suspicious or abnormal usage patterns** from telecom data.

* Created an interactive **Power BI dashboard** to visualize important business and operational KPIs.

* Designed the system as a distributed Big Data architecture using multiple components working together.

**Technologies Used:**

Python, PySpark, Apache Spark, Apache Kafka, Hadoop, HDFS, YARN, Apache Hive, HBase, SQL, Power BI, CSV, Big Data Analytics.

This project demonstrates practical experience in **Big Data engineering, data processing, distributed systems, real-time analytics, data visualization, and building end-to-end data pipelines**.