Graph-Based Modeling of ETL Activities with Multi-level Transformations and Updates

Extract-Transform-Load (ETL) workflows are data centric workflows responsible for transferring, cleaning, and loading data from their respective sources to the warehouse. In this paper, we build upon existing graph-based modeling techniques that treat ETL workflows as graphs by (a) extending the activity semantics to incorporate negation, aggregation and selfjoins, (b) complementing querying semantics with insertions, deletions and updates, and (c) transforming the graph to allow zoom-in/out at multiple levels of abstraction (i.e., passing from the detailed description of the graph at the attribute level to more compact variants involving programs, relations and queries and vice-versa).