step cleansingintegratingand transforming data using views to simulate data joins and appends to avoid duplication of datayou virtually combine data with views in the previous example we took the monthly data and combined it in new physical table the problem is that we duplicated the data and therefore needed more storage space in the example we're working withthat may not cause problemsbut imagine that every table consists of terabytes of datathen it becomes problematic to duplicate the data for this reasonthe concept of view was invented view behaves as if you're working on tablebut this table is nothing but virtual layer that combines the tables for you figure shows how the sales data from the different months is combined virtually into yearly sales table instead of duplicating the data views do come with drawbackhowever while table join is only performed oncethe join that creates the view is recreated every time it' queriedusing more processing power than pre-calculated table would have january sales physical tables february sales december sales obs date obs date obs date -jan -feb -dec -jan -feb -dec -jan -feb -dec yearly sales viewvirtual table obs date -jan -jan -dec figure view helps you combine data without replication enriching aggregated measures data enrichment can also be done by adding calculated information to the tablesuch as the total number of sales or what percentage of total stock has been sold in certain region (figure extra measures such as these can add perspective looking at figure we now have an aggregated data setwhich in turn can be used to calculate the participation of each product within its category this could be useful during data exploration but more so when creating data models as always this depends on the exact casebut from our experience models with "relative measuressuch as sales (quantity of