Energy — Data Lineage and Cataloging System for Energy Sector

Overview

The primary purpose of this DAG is to implement a robust data lineage and cataloging system tailored for the energy industry. It tracks the origin and transformations of data throughout the ingestion pipeline, integrating essential metadata for effective cataloging. The data sources include ERP transaction logs, sensor data from energy production facilities, and regulatory compliance reports. The ingestion pipeline begins with data extraction from these sources, followed by data validation and c

The primary purpose of this DAG is to implement a robust data lineage and cataloging system tailored for the energy industry. It tracks the origin and transformations of data throughout the ingestion pipeline, integrating essential metadata for effective cataloging. The data sources include ERP transaction logs, sensor data from energy production facilities, and regulatory compliance reports. The ingestion pipeline begins with data extraction from these sources, followed by data validation and cleansing processes to ensure quality. Next, the DAG performs transformation steps, including metadata enrichment and lineage tracking, which document the data's journey through various processing stages. The final outputs consist of a comprehensive data catalog, lineage reports, and compliance documentation, which are crucial for regulatory audits and internal governance. Monitoring key performance indicators (KPIs) such as data accuracy, lineage completeness, and processing times enables continuous improvement of the system. The business value of this DAG lies in its ability to enhance data transparency, ensure compliance with industry regulations, and facilitate informed decision-making across the organization.

Part of the Governance & Compliance solution for the Energy industry.

Use cases

• Enhances transparency of data flows in energy operations
• Facilitates compliance with stringent regulatory requirements
• Improves data quality and trustworthiness for decision-making
• Reduces risks associated with data mismanagement
• Streamlines audit processes and reduces operational overhead