Real-Time Data Pipeline Enhancement for Predictive Maintenance in Utility Operations

As an enterprise leader in the Utilities (Electric, Water, Gas) industry, we recognize the increasing importance of data-driven operations in maintaining service reliability and operational efficiency. This project focuses on enhancing our existing data infrastructure to support real-time analytics and predictive maintenance strategies. By implementing a comprehensive data pipeline utilizing Apache Kafka for event streaming, Spark for real-time data processing, and Airflow for workflow automation, we aim to create a self-sustaining data ecosystem that supports data mesh principles. Additionally, tools like dbt and Snowflake/BigQuery will be employed to ensure scalable data transformations and storage solutions. By leveraging these technologies, we anticipate significant reductions in unexpected downtime and maintenance costs, all while enhancing our ability to forecast and address potential issues proactively. The project also aligns with MLOps practices to facilitate continuous improvement and data observability, fostering a culture of data-driven decision-making across the organization.