Real-Time Data Pipeline Optimization for Enhanced Customer Experience

As an enterprise leader in the e-commerce sector, our company is experiencing exponential growth in data volume and complexity. To maintain our competitive edge and deliver exceptional customer experiences, we need to transition to a more dynamic data architecture. This project involves the design and implementation of a real-time data pipeline using a data mesh approach. Key components include Apache Kafka for event streaming, Spark for in-memory data processing, Airflow for workflow orchestration, dbt for data transformation, and Snowflake or BigQuery for cloud-based data warehousing. We also seek to integrate MLOps practices to streamline machine learning model deployment, ensuring rapid adaptability to changing market trends. The successful completion of this project will provide our teams with real-time analytics, enhancing decision-making and customer engagement strategies. The focus will be on establishing data observability for tracking and resolving data quality issues promptly.