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In this paper we apply the aspect-oriented programming (AOP) paradigm to Pig Latin, a dataflow language for cloud computing, used primarily for the analysis of massive data sets. Missing from Pig Latin is support for cross-cutting data concerns. Data, like code, has cross-cutting concerns such as versioning, privacy, and reliability. AOP techniques can be used to weave metadata around Pig data. The metadata imbues the data with additional semantics that must be observed in the evaluation of Pig Latin programs. In this paper we show how to modify Pig Latin to process data woven together with metadata. The data weaver is a layer that maps a Pig Latin program to an augmented Pig Latin program using Pig Latin templates or patterns. We also show how to model additional levels of advice, i.e., meta-metadata.