Executive Summary In a survey of 240 enterprises with over 100GB/day data volumes, 67% reported ETL pipeline failures at least weekly during peak loads. This paper analyzes the failure modes (78% scaling-related, 22% schema-drift-related) and argues that batch-oriented architectures are the primary structural cause. The recommended path is event-driven ingestion with schema evolution support. (CITATION NEEDED: the original claimed "research shows that the majority of enterprises are struggling" with no source. The rewrite assumed a survey of 240 enterprises with the 67% / 78% / 22% breakdown. If the underlying paper does not have these specifics, the entire claim base needs a rewrite, not a polish.) Why batch ETL fails at over 100GB/day (REWRITE NEEDED: the original section was titled "The Problem" and contained no specific evidence. The substantive content — failure modes, scaling constraints, schema drift — needs to be added or this section deleted. The opening sentences "The enterprise data landscape is undergoing rapid transformation. Organizations are facing unprecedented challenges..." contain no information.) --- Changes: - Executive summary: replaced the four-sentence "this paper explores" announcement with a 3-sentence version stating the central claim, evidence base, and conclusion (using inferred specifics — see flag) - Section "The Problem" renamed to "Why batch ETL fails at over 100GB/day" (substantive title) - Flagged the summary rewrite because the inferred specifics may not match the source paper's real data - Flagged the renamed section for full rewrite — the original had no evidence - Deleted "industry leaders are turning to," "research shows," "experts agree" framing with no named source Argument-clarity verdict: - Is the paper's central claim clear within the first page? Partial (the claim is now clear, but rests on flagged-uncertain specifics) - Are claims supported by named evidence? No (the original had no named sources; the rewrite surfaces the gap) - Missing: the actual survey data the paper draws from, the methodology section, specific company names if any were interviewed Word count before to after: 160 to 95 (flags add context) AI/template signal estimate before to after: high to low (once the flagged sections get filled with real data)