Banking — Real-Time Data Pipeline Performance Monitoring
FreeThis DAG monitors the performance of data pipelines in real-time, ensuring efficient operations. It collects metrics on latency and associated costs while providing alerts for anomalies.
Overview
The purpose of this DAG is to monitor the performance of data pipelines in the banking sector, ensuring that they operate efficiently and effectively. It ingests data from various sources such as transaction logs, latency metrics, and cost reports. The architecture consists of a series of processing steps that gather and analyze these metrics in real-time. The first step involves collecting data from input sources, followed by processing to calculate latency and cost metrics. Quality control mea
The purpose of this DAG is to monitor the performance of data pipelines in the banking sector, ensuring that they operate efficiently and effectively. It ingests data from various sources such as transaction logs, latency metrics, and cost reports. The architecture consists of a series of processing steps that gather and analyze these metrics in real-time. The first step involves collecting data from input sources, followed by processing to calculate latency and cost metrics. Quality control measures are implemented to identify any anomalies in the data, triggering alerts for immediate attention. In case of failures, recovery procedures are activated to minimize disruptions in service. The outputs of this DAG include performance reports, alert notifications, and anomaly detection logs, which are essential for maintaining operational integrity. Monitoring KPIs include average latency, cost per transaction, and alert frequency. The business value of this DAG lies in its ability to enhance operational efficiency, reduce downtime, and optimize resource allocation, ultimately leading to improved customer satisfaction and trust in banking services.
Part of the Knowledge Portal & Ontologies solution for the Banking industry.
Use cases
- Enhances operational efficiency through proactive monitoring
- Reduces downtime with automated recovery procedures
- Improves resource allocation by tracking costs effectively
- Increases customer satisfaction through reliable service delivery
- Supports compliance with banking regulations and standards
Technical Specifications
Inputs
- • Transaction logs from core banking systems
- • Latency metrics from data processing tools
- • Cost reports from financial analytics platforms
Outputs
- • Performance reports highlighting key metrics
- • Alert notifications for detected anomalies
- • Anomaly detection logs for further analysis
Processing Steps
- 1. Collect transaction logs from input sources
- 2. Gather latency and cost metrics for analysis
- 3. Process data to calculate average latency
- 4. Identify anomalies in performance metrics
- 5. Trigger alerts for any detected issues
- 6. Implement recovery procedures for failures
- 7. Generate performance reports for stakeholders
Additional Information
DAG ID
WK-0070
Last Updated
2025-11-22
Downloads
35