Optimizing Real-Time Data Pipelines for Enhanced Extraction Efficiency

The mission is to enhance the operational efficiency of our mining processes through a state-of-the-art data engineering solution. We aim to build a real-time data processing pipeline that integrates data from various sources, including sensors and operational systems, to provide actionable insights. This project will utilize Apache Kafka for event streaming, Apache Spark for large-scale data processing, and Airflow for orchestrating complex workflows. Data will be stored and processed in platforms like Snowflake or BigQuery, ensuring scalability and performance. The integration of dbt and Databricks will facilitate data transformation and machine learning model deployment, creating a seamless MLOps environment. The successful implementation of this solution will lead to improved resource allocation, predictive maintenance, and enhanced safety protocols. By aligning with industry trends such as data mesh and data observability, the company will maintain a competitive edge and meet the increasing demands for efficient and sustainable mining operations.