Transport & Logistics — Data Lineage and Cataloging for Transport Operations

Overview

The primary purpose of the 'Data Lineage and Cataloging for Transport Operations' DAG is to systematically catalog ingested data and monitor its lineage within the transport and logistics sector. By leveraging metadata, this workflow documents the transformations applied to the data and the original sources from which it originates. This capability is crucial for ensuring data transparency and compliance with industry regulations. The pipeline begins with the ingestion of various data sources, i

The primary purpose of the 'Data Lineage and Cataloging for Transport Operations' DAG is to systematically catalog ingested data and monitor its lineage within the transport and logistics sector. By leveraging metadata, this workflow documents the transformations applied to the data and the original sources from which it originates. This capability is crucial for ensuring data transparency and compliance with industry regulations. The pipeline begins with the ingestion of various data sources, including ERP transaction logs, GPS tracking data, and inventory management records. Each data input is processed to extract relevant metadata, which is then stored in a centralized catalog. Processing steps include data validation to ensure quality, lineage tracking to document data transformations, and compliance checks to align with regulatory standards. The outputs of this DAG include a comprehensive data catalog, lineage reports, and alerts for any discrepancies detected during processing. Monitoring key performance indicators (KPIs) such as data accuracy, transformation success rates, and compliance adherence is integral to maintaining the integrity of the data pipeline. The business value of this DAG lies in its ability to enhance data governance, reduce compliance risks, and improve operational efficiency by providing stakeholders with clear visibility into data origins and transformations.

Part of the Data & Model Catalog solution for the Transport & Logistics industry.

Use cases

• Improved data governance and compliance with regulations
• Enhanced operational efficiency through streamlined data management
• Increased transparency in data handling and transformations
• Reduced risk of data errors and inconsistencies
• Better decision-making supported by accurate data lineage insights