Energy — Energy Industry Taxonomy and Knowledge Graph Development

Overview

The purpose of this DAG is to construct a robust taxonomy and knowledge graph tailored for the energy industry, leveraging both internal and external data sources. The ingestion pipeline begins with the collection of data from various sources, including energy consumption reports, regulatory documents, and market analysis datasets. Triggered by content updates and the introduction of new data sources, the pipeline initiates data analysis to identify relationships and hierarchies within the data.

The purpose of this DAG is to construct a robust taxonomy and knowledge graph tailored for the energy industry, leveraging both internal and external data sources. The ingestion pipeline begins with the collection of data from various sources, including energy consumption reports, regulatory documents, and market analysis datasets. Triggered by content updates and the introduction of new data sources, the pipeline initiates data analysis to identify relationships and hierarchies within the data. This is followed by a processing step that involves creating connections between entities, which are then stored in a graph database designed for efficient querying and retrieval. Quality control measures are embedded throughout the process to ensure data consistency and accuracy, including validation checks and anomaly detection. The final outputs are made available through a knowledge portal, allowing stakeholders to access and utilize the information effectively. Key performance indicators (KPIs) for monitoring the success of this DAG include the number of relationships created and the time taken for updates. In the event of a failure, an error report is generated to facilitate troubleshooting and ensure continuous improvement. This DAG not only streamlines data management but also significantly enhances decision-making capabilities within the energy sector by providing a structured knowledge framework.

Part of the Data & Model Catalog solution for the Energy industry.

Use cases

• Improves data accessibility for energy stakeholders
• Enhances decision-making through structured knowledge
• Facilitates compliance with regulatory requirements
• Supports innovation through data-driven insights
• Increases operational efficiency in data management