Data lineage is the process of tracking the flow of data over time. It provides a visual map of the data's journey from its original source, through various transformations, ETL processes, and aggregations, to its final destination in reports or AI models. This visibility is vital for maintaining data integrity. By capturing the "who, what, where, and when," organizations can verify accuracy, ensuring that downstream analytics and machine learning models are built on trustworthy, traceable foundations.
For industries like Finance and Healthcare, lineage is non-negotiable. It proves to auditors that data is accurate, private, and handled according to regulations like GDPR, HIPAA, or Basel III. It allows you to demonstrate exactly how sensitive data was processed and who accessed it.
While often used interchangeably, provenance specifically documents the origin and history of a data object (where it came from), while lineage tracks the movement, flow, and transformations of that data throughout its lifecycle.
Yes. When a report or model fails, lineage allows engineers to trace the error upstream instantly. Instead of checking every stage manually, they can pinpoint exactly which transformation introduced the corruption, significantly reducing Time-to-Recovery (TTR).
Absolutely. To build reliable AI, you must understand your training data. Lineage ensures you can trace model outputs back to specific datasets, helping you identify bias, remove low-quality inputs, and explain model behavior to stakeholders.
Shakudo integrates lineage into the orchestration layer. Because Shakudo manages your entire tool ecosystem—from storage to compute—it maintains platform-wide audit trails and lineage maps automatically. This gives enterprises absolute control, ensuring sensitive data never leaves your governance boundary and simplifying compliance for critical infrastructure sectors.
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