Automating End-to-End Lineage Tracking for Multi-Cloud Data Architectures
Authors: Sevinthi Kali Sankar Nagarajan  
Published: February 2025

Data lineage tracing is becoming more important for governance, compliance, and operational efficiency as more and more organisations use multi-cloud strategies to take advantage of different cloud platforms. Because of their inherent silos, traditional approaches to data lineage tracing are ill-equipped to manage the sheer volume and complexity of modern multi-cloud setups. An end-to-end data lineage tracing system that is completely automated and optimised for multi-cloud architectures is presented in this study. In order to provide a scalable and smooth solution, the framework incorporates AI-driven analytics, distributed tracing methods, and powerful metadata management tools. It guarantees that data translation, utilisation, and transfer across different cloud platforms can be monitored in real-time. Its capacity to increase data governance, traceability, and manual intervention reduction has been shown by empirical examination. This study tackles issues including scalability, security, and interoperability to provide a new benchmark for lineage tracing in multi-cloud environments. A company's data-driven processes may be made more transparent and reliable with the help of automated lineage tracking. The need for end-to-end lineage tracking has grown in importance as more and more organisations use multi-cloud setups to handle their expanding data ecosystems. Data lineage methods that were developed for static and single-cloud architectures aren't well-suited to handle the widespread, complicated, and ever-changing data activities that take place in multi-cloud environments. Using state-of-the-art metadata extraction, real-time monitoring, and dependency mapping based on machine learning, this research presents an automated method for full lineage tracing across multi-cloud infrastructures. Detecting lineage gaps caused by fragmented processes, scalability for large-scale datasets, and interoperability across multiple cloud platforms are all important difficulties that the proposed framework attempts to solve. Transparent and real-time insights into data migration, transformations, and relationships are provided by the system via the integration of technologies such as graph-based visualisation and AI-driven anomaly detection. Regulatory compliance, operational efficiency, and lineage correctness have all seen substantial advances, according to empirical reviews. The significance of automation in ensuring reliable lineage tracing in multi-cloud environments is highlighted in this study, which also provides a scalable answer to the increasing needs of contemporary data structures. Organisations seeking to improve governance, streamline procedures, and guarantee data dependability in more dispersed locations should use the results as a springboard.The difficulty of guaranteeing accountability and transparency in data processes is rising at an exponential rate as more and more organisations use multi-cloud environments to handle their data. In this research, we look at a new approach to automate full lineage tracing in data architectures that use several clouds. The suggested approach tackles the issues of disjointed data governance and cross-cloud traceability by using distributed ledger technology, real-time monitoring, and enhanced metadata management. Organisations may use the framework to thoroughly record the origin, transformation, and utilisation of data across different cloud platforms. Results from real-world tests show that it improves data visibility, reduces compliance risks, and simplifies audit procedures. Furthermore, by guaranteeing the veracity and correctness of genealogy records, it promotes more faith in data-driven decision-making. In order to help businesses better manage their complicated multi-cloud data ecosystems, our work offers a flexible and scalable solution.