Extract-Transform-Load (ETL) workflows are data centric workflows responsible for transferring, cleaning, and loading data from their respective sources to the warehouse. Previous research has identified graphbased techniques, in order to construct the blueprints for the structure of such workflows. In this paper, we extend existing results by (a) extending the querying semantics to incorporate negation, aggregation and self-joins, (b) complementing query semantics in order to handle insertions, deletions and updates, and (c) transforming the graph to allow zoom-in/out at multiple levels action (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).