Banking — Financial Data Cataloging and Lineage Management Pipeline

Overview

The purpose of this DAG is to create a comprehensive catalog of financial datasets while ensuring robust tracking of data lineage. By integrating metadata for each dataset, it enables users to understand the data's origin and transformations throughout its lifecycle. The data sources include financial transaction logs, compliance reports, and audit trails, which are ingested into the pipeline. The ingestion process begins with data extraction from these sources, followed by metadata enrichment,

The purpose of this DAG is to create a comprehensive catalog of financial datasets while ensuring robust tracking of data lineage. By integrating metadata for each dataset, it enables users to understand the data's origin and transformations throughout its lifecycle. The data sources include financial transaction logs, compliance reports, and audit trails, which are ingested into the pipeline. The ingestion process begins with data extraction from these sources, followed by metadata enrichment, where detailed information about each dataset is added. Next, the DAG implements access control checks to ensure that users have the appropriate permissions to access the datasets. Quality controls are embedded within the processing logic to verify the integrity and accuracy of the metadata. The outputs of this DAG include a searchable dataset catalog, lineage reports, and access logs, which are essential for regulatory compliance and governance. Monitoring key performance indicators (KPIs) such as the number of datasets cataloged and search time within the catalog allows for continuous improvement. In case of any failures during processing, a recovery mechanism is triggered to maintain operational continuity. The business value of this DAG lies in its ability to enhance data governance, ensure compliance with regulations, and improve data accessibility for financial analysts and decision-makers.

Part of the Governance & Compliance solution for the Banking industry.

Use cases

• Enhances regulatory compliance and reduces audit risks
• Improves data accessibility for financial analysts
• Facilitates informed decision-making through data transparency
• Increases operational efficiency with automated processes
• Strengthens data governance frameworks within the organization