Defense & Aerospace — Data Quality Normalization for Predictive Maintenance

Overview

The primary purpose of this DAG is to ensure the quality and normalization of data ingested for predictive maintenance in the Defense and Aerospace sector. It ingests various data sources, including sensor readings, maintenance logs, and operational metrics. The ingestion pipeline initiates with data collection, followed by a series of processing steps designed to validate and normalize the data. These steps include applying quality tests, checking compliance with predefined standards, and perfo

The primary purpose of this DAG is to ensure the quality and normalization of data ingested for predictive maintenance in the Defense and Aerospace sector. It ingests various data sources, including sensor readings, maintenance logs, and operational metrics. The ingestion pipeline initiates with data collection, followed by a series of processing steps designed to validate and normalize the data. These steps include applying quality tests, checking compliance with predefined standards, and performing historical data archiving for lineage tracking. The processing logic incorporates automated checks to ensure data integrity, and any failures trigger alerts for immediate intervention. Key performance indicators (KPIs) monitored throughout the process include the compliance rate of the data and the processing time for each batch. The outputs of this DAG consist of validated and normalized datasets, compliance reports, and alert logs. By implementing this DAG, organizations can significantly improve the reliability of their predictive maintenance analyses, leading to enhanced decision-making and operational efficiencies in the Defense and Aerospace industry.

Part of the Fraud & Anomaly Analytics solution for the Defense & Aerospace industry.

Use cases

• Enhanced reliability of predictive maintenance analyses
• Reduced operational risks through timely data interventions
• Improved compliance with industry standards
• Increased efficiency in data processing workflows
• Better decision-making supported by high-quality data