Energy — Energy Data Normalization and Quality Assurance Pipeline

Overview

The primary purpose of this DAG is to normalize ingested energy data while applying predefined quality tests to ensure reliability and compliance. The data sources include energy consumption logs, sensor data from smart meters, and historical energy usage records. The ingestion pipeline begins with extracting these data sources, followed by a series of processing steps that include data validation, normalization, and quality checks. During the transformation phase, the DAG applies business rules

The primary purpose of this DAG is to normalize ingested energy data while applying predefined quality tests to ensure reliability and compliance. The data sources include energy consumption logs, sensor data from smart meters, and historical energy usage records. The ingestion pipeline begins with extracting these data sources, followed by a series of processing steps that include data validation, normalization, and quality checks. During the transformation phase, the DAG applies business rules to standardize data formats and values, ensuring consistency across datasets. Quality controls are integrated at each step to verify adherence to safety and privacy standards, with specific Key Performance Indicators (KPIs) monitored, such as compliance rates and processing times. The processed data is then cataloged and historical records are maintained for traceability. Outputs include normalized datasets ready for analysis, compliance reports, and quality assurance documentation. This DAG not only enhances data quality but also provides significant business value by enabling accurate analytics and reporting, which supports informed decision-making in the energy sector.

Part of the Document Automation solution for the Energy industry.

Use cases

• Improved data quality leading to better analytical insights
• Enhanced compliance with industry regulations and standards
• Increased operational efficiency through automation
• Reduced risk of data-related errors in reporting
• Facilitated decision-making based on reliable data