Transport & Logistics — Multi-Source Data Extraction for Logistics Analysis

Overview

The primary purpose of this DAG is to facilitate the extraction and normalization of data from various systems within the transport and logistics industry, specifically targeting ERP, CRM, and IoT sources. By integrating data from internal databases and business APIs, the DAG supports the creation of a comprehensive data catalog that enhances governance and compliance efforts. The ingestion pipeline begins with the collection of data from specified sources, followed by a series of processing ste

The primary purpose of this DAG is to facilitate the extraction and normalization of data from various systems within the transport and logistics industry, specifically targeting ERP, CRM, and IoT sources. By integrating data from internal databases and business APIs, the DAG supports the creation of a comprehensive data catalog that enhances governance and compliance efforts. The ingestion pipeline begins with the collection of data from specified sources, followed by a series of processing steps that include normalization and validation. During these steps, quality control measures are implemented to ensure the integrity and accuracy of the data. This is crucial for maintaining compliance with industry regulations and standards. The outputs of this DAG are stored in a data warehouse, making them readily available for further analysis and reporting. Key performance indicators (KPIs) are monitored throughout the process to assess data quality and processing efficiency. The business value lies in the ability to provide reliable data for decision-making, improve operational efficiency, and ensure compliance with regulatory requirements in the transport and logistics sector.

Part of the Governance & Compliance solution for the Transport & Logistics industry.

Use cases

• Enhances data-driven decision-making capabilities
• Improves operational efficiency through accurate data
• Ensures compliance with regulatory standards
• Reduces risks associated with data inaccuracies
• Supports comprehensive data governance initiatives