Data lineage

Data lineage includes the data origin, what happens to it, and where it moves over time. Data lineage provides visibility and simplifies tracing errors back to the root cause in a data analytics process. It also enables replaying specific portions or inputs of the data flow for step-wise debugging or regenerating lost output. Database systems use such information, called data provenance, to address similar validation and debugging challenges. Data provenance refers to records of the inputs, entities, systems, and processes that influence data of interest, providing a historical record of the data and its origins. The generated evidence supports forensic activities such as data-dependency analysis, error/compromise detection and recovery, auditing, and compliance analysis. "Lineage is a simple type of why provenance." Data lineage can be represented visually to discover the data flow/movement from its source to destination via various changes and hops on its way in the enterprise environment, how the data gets transformed along the way, how the representation and parameters change, and how the data splits or converges after each hop. A simple representation of the Data Lineage can be shown with dots and lines, where dot represents a data container for data points and lines connecting them represents the transformations the data point undergoes, between the data containers. Representation broadly depends on the scope of the metadata management and reference point of interest. Data lineage provides sources of the data and intermediate data flow hops from the reference point with backward data lineage, leading to the final destination's data points and its intermediate data flows with forward data lineage. These views can be combined with end-to-end lineage for a reference point that provides a complete audit trail of that data point of interest from sources to their final destinations. As the data points or hops increase, the complexity of such representation becomes incomprehensible.