Data Engineering and Real-Time Processing in Finance

Real-time data pipelines can deliver information with very low latency to end users or applications, empowering real-time decision making, detection of urgent situations, and time-sensitive actions. Sectors such as transportation and logistics, telecommunications, social media, video games, and financial services require real-time data processing, and the real-time data pipeline architecture can be implemented in these domains. The streaming finance data pipeline architecture can be applied in the finance domain for regulatory reporting, risk analytics, fraud detection, anomaly adaptation, and algorithmic trading in compliance with rules and regulations.

Notably, the streaming finance data pipeline architecture also offers a finer data flow control model that complements the logic to satisfy user-defined security, privacy, and compliance requirements. Nevertheless, besides minimizing end-to-end latency, the architecture focuses on the quality, provenance, support for observability, and security and privacy aspects. Quality techniques are meant to ensure that any data ingested conforms to application requirements, while lineage techniques maintain a chain of custody, thereby helping in auditability. The monitoring of the pipeline is vital for detecting violations, failures, and performance degradation.