Telecom — Predictive Maintenance Feature Engineering Pipeline

Overview

The purpose of this DAG is to facilitate predictive maintenance in the telecom industry by extracting and engineering relevant features from ingested data. The data sources include network performance metrics, equipment logs, and historical maintenance records. The ingestion pipeline processes these data sources, ensuring they are accurately captured and prepared for analysis. The processing steps involve data transformation techniques such as aggregation and normalization to compute health feat

The purpose of this DAG is to facilitate predictive maintenance in the telecom industry by extracting and engineering relevant features from ingested data. The data sources include network performance metrics, equipment logs, and historical maintenance records. The ingestion pipeline processes these data sources, ensuring they are accurately captured and prepared for analysis. The processing steps involve data transformation techniques such as aggregation and normalization to compute health features of telecom equipment. Quality controls are implemented throughout the pipeline to validate the integrity and accuracy of the data, ensuring that only high-quality features are stored in the data warehouse. The outputs of this DAG include engineered features that feed into predictive models, with key performance indicators (KPIs) such as precision and recall being monitored to assess model effectiveness. The business value of this DAG lies in its ability to enhance operational efficiency, reduce equipment downtime, and optimize maintenance schedules, ultimately leading to cost savings and improved service reliability.

Part of the Predictive Maintenance solution for the Telecom industry.

Use cases

• Reduces unexpected equipment failures and downtime
• Enhances decision-making through data-driven insights
• Optimizes maintenance schedules for cost efficiency
• Improves customer satisfaction with reliable service
• Facilitates compliance with industry regulations