Banking — Multi-Source Financial Data Ingestion Pipeline

Overview

The purpose of the finance_km1_ingestion_multi_sources DAG is to facilitate the ingestion of financial data from various sources, including ERP systems, CRM platforms, and business APIs. This integration is crucial for achieving comprehensive visibility into financial operations and ensuring regulatory compliance. The architecture comprises a robust data pipeline that begins with data extraction from the identified sources. Each data source feeds into the pipeline, where the data undergoes norma

The purpose of the finance_km1_ingestion_multi_sources DAG is to facilitate the ingestion of financial data from various sources, including ERP systems, CRM platforms, and business APIs. This integration is crucial for achieving comprehensive visibility into financial operations and ensuring regulatory compliance. The architecture comprises a robust data pipeline that begins with data extraction from the identified sources. Each data source feeds into the pipeline, where the data undergoes normalization and historical tracking. Quality control mechanisms are applied throughout the ingestion process to ensure data accuracy and integrity. In case of any ingestion failures, the DAG incorporates a recovery mechanism to reprocess the affected data. Once the data is successfully ingested, it is stored in a centralized data warehouse, making it readily available for analytical purposes. Key performance indicators (KPIs) for monitoring this process include the ingestion error rate and processing time, which provide insights into the efficiency and reliability of the data pipeline. The business value of this DAG lies in its ability to streamline data ingestion, enhance compliance with governance standards, and provide timely insights for decision-making in the banking sector.

Part of the Data & Model Catalog solution for the Banking industry.

Use cases

• Enhances regulatory compliance through accurate data management.
• Improves decision-making with timely access to integrated data.
• Reduces operational risks associated with data inconsistencies.
• Streamlines data workflows, increasing overall efficiency.
• Facilitates better financial reporting and analytics capabilities.