High Tech — Data Normalization and Historical Tracking for Predictive Maintenance

Overview

The primary purpose of this DAG is to ensure the quality and historical tracking of ingested data, which is crucial for effective predictive maintenance in the high-tech industry. The pipeline begins with the ingestion of various data sources, including sensor data, maintenance logs, and ERP transaction logs. Once ingested, the data undergoes a series of processing steps designed to normalize its format and validate its quality. This includes implementing data quality tests and checks to ensure

The primary purpose of this DAG is to ensure the quality and historical tracking of ingested data, which is crucial for effective predictive maintenance in the high-tech industry. The pipeline begins with the ingestion of various data sources, including sensor data, maintenance logs, and ERP transaction logs. Once ingested, the data undergoes a series of processing steps designed to normalize its format and validate its quality. This includes implementing data quality tests and checks to ensure compliance with predefined standards. The DAG also incorporates a lineage tracking system that catalogs data transformations over time, enabling traceability and accountability. Governance controls are a key feature, including role-based access control (RBAC) and data masking for sensitive information, which help to maintain data security and integrity. In the event of any errors detected during processing, a correction process is triggered to rectify the issues promptly. The outputs of this DAG include standardized datasets, historical records, and compliance reports that are essential for analytics and decision-making. Monitoring key performance indicators (KPIs) such as data quality scores and processing times allows for continuous improvement and optimization of the pipeline. The business value derived from this DAG is significant, as it enhances data governance, supports regulatory compliance, and ultimately leads to more reliable predictive maintenance strategies.

Part of the Predictive Maintenance solution for the High Tech industry.

Use cases

• Improved data governance enhances decision-making capabilities.
• Increased compliance with industry regulations and standards.
• Enhanced predictive maintenance through reliable data insights.
• Reduced operational risks associated with data errors.
• Streamlined data management processes increase efficiency.