Real-Time Data Pipeline Optimization for Genomic Analysis

The biotechnology industry is evolving rapidly, with an increasing demand for precision medicine and personalized treatment plans driven by genomic data. Our enterprise is dedicated to pioneering advancements in genomics, but our current data processing systems are unable to keep pace with the vast influx of real-time data. This project seeks to optimize our existing data pipeline, transforming it into a robust, real-time analytical platform. By implementing a data mesh architecture, we can achieve decentralized data ownership, enhancing collaboration and innovation across research teams. Leveraging technologies such as Apache Kafka for event streaming, Spark for real-time analytics, and Airflow for orchestrating complex workflows, we aim to create a seamless data experience. This transformation will enable us to utilize genomic data more effectively, accelerating research timelines and improving patient outcomes. The project will also incorporate data observability to ensure data reliability and integrity. The final solution will be integrated with platforms like Snowflake and Databricks to provide scalable storage and compute resources, ultimately supporting our ambitious goals in personalized medicine.