Citation:

Naveen Edapurath Vijayan, 2024. "A Comprehensive Framework for Data Dependency Monitoring in Upstream Business Intelligence Systems", ESP Journal of Engineering & Technology Advancements 4(4): 68-75.

Abstract:

As organizations increasingly depend on data-driven decision-making, the complexity of Business Intelligence (BI) systems and their data pipelines has grown exponentially. This complexity introduces significant challenges in maintaining data quality, ensuring traceability, and guaranteeing system reliability. Unmanaged data dependencies in upstream BI components can lead to data inconsistencies, system failures, and compromised analytics. This paper presents a comprehensive framework for monitoring and managing data dependencies in upstream BI systems, with a primary focus on the Dependency Discovery Engine utilizing Static Code Analysis. The framework provides a systematic approach to identifying, tracking, and managing data dependencies across the entire BI ecosystem using advanced static code analysis techniques. Detailed methodologies, algorithms, and implementation considerations for static code analysis are discussed. This framework offers a robust solution for organizations seeking to enhance data quality, system reliability, and operational efficiency in their BI systems. Future research will expand on dynamic runtime analysis and machine learning- based methods to further enhance the framework.

References:

[1] Automated data retrieval and providing alerts for outdated data- S Shaji, HP Reddyvari, A Poudel, AK Roy - US Patent 12,086,115, 2024 - Google Patents

[2] Abiteboul, S., Hull, R., & Vianu, V. (1995). Foundations of Databases. Addison-Wesley.

[3] Chaudhuri, S., & Dayal, U. (1997). An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26(1), 65–74.

[4] Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.

[5] Zhang, D., Lu, H., & Ye, W. (2018). Dependency analysis for evolving ETL processes. Journal of Information Systems, 47(2), 23–34.

[6] Golab, L., & Özsu, M. T. (2003). Issues in data stream management. ACM SIGMOD Record, 32(2), 5–14.

[7] Meliou, A., Gatterbauer, W., Halpern, J. Y., Koch, C., & Suciu, D. (2010). Causality in databases. Proceedings of the ACM SIGMOD International Conference on Management of Data.

[8] Cuzzocrea, A., Song, I. Y., & Davis, K. C. (2011). Analytics over large- scale multidimensional data: The big data revolution! Proceedings of the ACM International Workshop on Data Warehousing and OLAP.

[9] Barrett, B., Paleyes, A., & Lawrence, N. D. (2021). Machine learning system design. Proceedings of the 2021 Conference on Neural Information Processing Systems.

[10] The Apache Software Foundation. (2023). Apache Airflow documentation. Retrieved from https://airflow.apache.org.

[11] SonarQube Foundation. (2023). SonarQube: Open-source platform for static code analysis. Retrieved from https://sonarqube.org.

[12] Ramakrishnan, R., & Gehrke, J. (2000). Database Management Systems. McGraw-Hill.

[13] Muller, R., & Rahm, E. (2018). Metadata management for data lakes. Journal of Big Data Research, 15, 1–12.

[14] Kumar, V., & Minz, S. (2015). Multi-level metadata lineage for Big Data pipelines. Journal of Information Management Systems, 33(3), 199–210.

Keywords:

Data Dependency Monitoring, Business Intelligence Systems, Static Code Analysis, Data Lineage, Data Governance, Data Quality, Dependency Discovery.