Real-time Data Pipeline Optimization for Enhanced Telecommunications Analytics

In the fast-paced telecommunications industry, real-time data analytics is crucial for staying competitive. This project focuses on developing a state-of-the-art data pipeline that integrates event streaming, real-time analytics, and data observability to provide actionable insights quickly and efficiently. Utilizing technologies such as Apache Kafka for event streaming, Spark for real-time processing, and Snowflake for scalable data storage, the project aims to transform raw data into valuable insights. The pipeline will incorporate data mesh principles to ensure flexibility and scalability, enabling seamless data sharing across various departments. By establishing a MLOps framework, the project will facilitate the incorporation of machine learning models into data workflows, enhancing predictive capabilities. Additionally, the implementation of dbt and Airflow will ensure robust data transformations and orchestrations, respectively. This holistic approach will revolutionize how the telecommunications company leverages data, ultimately improving customer satisfaction and driving business growth.