Next-Generation Data Integration Pipelines for Real-Time Financial Market Analytics
DOI:
https://doi.org/10.47363/JMSCM/2022(1)E122Keywords:
Real-Time Data Integration, Financial Market Analytics, Data Pipelines, Big Data Processing, Data Governance, Cloud Data Lakes, Data Ingestion and Streaming, Apache Spark, Data Transformation, Data Quality Assurance, Event-Driven Architecture, Market Insights and Predictions, Financial Data Management, Data Analytics and Reporting, Risk Management, Predictive Modeling, Scalable Data Infrastructure, Machine Learning Integration, Feature Engineering, Business Intelligence (BI) ToolsAbstract
The dynamic and fast-paced nature of financial markets necessitates real-time data processing and integration to support timely and accurate decision making. This paper explores next-generation data integration pipelines tailored for real-time financial market analytics. These pipelines leverage modern data engineering technologies such as Apache Spark, cloud-based data lakes, and event-driven architectures to ingest, process, and analyze massive data streams from various financial data sources. The integration framework emphasizes low-latency data processing, scalable infrastructure, and robust data governance to ensure data accuracy and compliance. Key focus areas include data normalization, feature engineering, real-time analytics for trading decisions, and dashboard-driven reporting for market insights. The proposed system demonstrates how seamless data integration can empower financial institutions with predictive insights, enhanced risk management, and improved customer targeting, ultimately driving profitability and competitive advantage in the digital economy.
