Real-time Data Pipeline Optimization for Genomic Analysis

We are a biotechnology scale-up focused on advancing genomic research. As we scale, the volume and complexity of our genomic data have increased significantly. To maintain our competitive edge and support our research teams effectively, we need to transition from batch processing to real-time data analysis. This project involves designing and implementing a data pipeline that leverages state-of-the-art technologies such as Apache Kafka for event streaming, Apache Spark for processing, and Snowflake or BigQuery for data warehousing. The pipeline should enable us to process vast datasets in real-time, allowing our researchers to gain insights into genetic sequences faster and more efficiently. The ideal solution will incorporate data mesh principles, ensuring data is easily accessible and managed across different teams. Furthermore, implementing MLOps and data observability will ensure our models are continuously optimized, and data flows are monitored effectively. This project is critical to support our growing data demands and enhance our research outcomes.