Energy — Data Quality Normalization and Validation Pipeline

Overview

The primary purpose of the 'Data Quality Normalization and Validation Pipeline' is to ensure the integrity and compliance of ingested energy data. This DAG ingests various data sources, including ERP transaction logs, sensor data, and regulatory compliance reports. The ingestion pipeline initiates with data extraction from these sources, followed by a series of processing and transformation steps designed to validate and normalize the data. The first step involves data quality testing, where the

The primary purpose of the 'Data Quality Normalization and Validation Pipeline' is to ensure the integrity and compliance of ingested energy data. This DAG ingests various data sources, including ERP transaction logs, sensor data, and regulatory compliance reports. The ingestion pipeline initiates with data extraction from these sources, followed by a series of processing and transformation steps designed to validate and normalize the data. The first step involves data quality testing, where the system checks for inconsistencies, missing values, and adherence to predefined standards. Next, normalization processes standardize data formats and units, ensuring consistency across datasets. Historical data is then archived for auditing and compliance purposes, while sensitive information undergoes security controls, including data masking to protect privacy. The processed data is cataloged in a centralized repository, facilitating easy access for analysis and reporting. Monitoring key performance indicators (KPIs) such as data accuracy rates, processing times, and compliance adherence is integral to this workflow. The outputs include validated datasets, compliance reports, and a comprehensive data catalog. This DAG delivers significant business value by enhancing data reliability, ensuring regulatory compliance, and improving decision-making processes within the energy sector.

Part of the Customer Personalization solution for the Energy industry.

Use cases

• Improves data reliability for better decision-making
• Ensures compliance with industry regulations and standards
• Enhances operational efficiency through streamlined processes
• Reduces risks associated with data inaccuracies
• Facilitates easier access to critical data for stakeholders