Building a Big Data Platform for a Financial Management Company: Preparing Data for Reporting with Automated Data Flow
Syed Ziaurrahman Ashraf
Email: ziadawood@gmail.com
Designation: Technical Program Manager @ Bank of America
Abstract
This paper focuses on how financial management companies can use big data platforms to automate the flow of data for accurate and timely financial reporting. By leveraging automation, we can reduce human intervention and errors in the reporting process. This paper explains how data is ingested, processed, and transformed automatically, and how automation tools like Apache Airflow can help manage the data flow. We also provide diagrams, flowcharts, and pseudocode to give a clear understanding of the design and processes involved.
Keywords
Big Data Platform, Financial Reporting, Data Flow Automation, ETL Pipeline, Apache Airflow, Data Transformation, Real-Time Reporting, Financial Data Management.
Conclusion
In this paper, we explored how a financial management company can build a scalable and automated big data platform for reporting. By automating the data flow from ingestion to reporting, companies can significantly improve the speed and accuracy of their financial reports.
The use of tools like Apache Airflow to orchestrate these processes ensures that data is continuously flowing through the pipeline, ready for real-time analysis and reporting. This setup not only meets business needs but also ensures that the company complies with strict regulatory requirements through automated data governance.
Investing in a robust big data platform is essential for any financial management company looking to scale its operations and remain competitive in today’s fast-paced, data-driven world.
References
K. Smith, "Automating Data Pipelines in the Financial Industry," Journal of Financial Technology, vol. 12, no. 3, pp. 45-55, 2021.
A. Kumar and P. Desai, "Big Data Platforms for Financial Reporting," IEEE Transactions on Data Science, vol. 10, no. 2, pp. 66-75, 2020.
M. Green, "Using Apache Airflow for Orchestration of Data Pipelines," Big Data in Finance, vol. 6, no. 4, pp. 23-30, 2022.
