Scalable lineage capture for debugging DISC analytics

Abstract

A fundamental challenge for big-data analytics is how to efficiently tune and debug multi-step dataflows. This paper presents Newt, a scalable architecture for capturing and using record-level data lineage to discover and resolve errors in analytics. Newt's flexible instrumentation allows system developers to collect this fine-grain lineage from a range of data intensive scalable computing (DISC) architectures, actively recording the flow of data through multi-step, user-defined transformations. Newt pairs this API with a scale-out, fault-tolerant lineage store and query engine.