Real-time Data Pipeline Implementation for Enhanced Infrastructure Monitoring

As a leading enterprise in the DevOps & Infrastructure industry, we require a sophisticated data engineering solution to optimize our infrastructure monitoring and management processes. The project's goal is to develop a real-time data pipeline capable of ingesting, processing, and analyzing data from multiple sources. This data pipeline will leverage technologies such as Apache Kafka for event streaming, Spark for data processing, and Airflow for workflow orchestration. We aim to implement a data mesh architecture to ensure scalability and flexibility across different teams. Additionally, integrating MLOps capabilities will allow us to develop and deploy predictive maintenance models, enhancing our decision-making processes. By utilizing Snowflake or BigQuery for data warehousing and dbt for transformation, this project will enable efficient data aggregation and reporting. The expected outcome is a robust, scalable infrastructure monitoring system that provides actionable insights in real-time, reducing downtime and improving operational efficiency.