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Have you ever wanted to create a data model from a table that was created from a flat file? Now you can with data modeler. I'll start by creating a model. Models are made up of dimension and fact tables and joins. Dimension tables describe the data attributes such as customer name or customer marital status. Fact tables contain measures such as revenue or billed units and joins show me the relationships between fact and dimension tables in the data models. Next, I'll review the table I want to use as a basis for the new model. It looks like the table contains over five thousand rows. Because this table contains both attributes and measures, I select the add as fact and dimension tables option. I rename the fact table and then preview a column to see the contents. I can add other dimension tables too. Deleting a column is easy. I can right click the column in a table and select remove. To add a column, I drag and drop it from the source table pane or click Add column. Next, I review the suggested dimension tables. I can change the table names and combine tables but to create reports, I have to join each dimension table to a fact table. Data modeler has recommended the joins for me. If I deselect the check box in the dimension table, the join is removed from the dimension table and the corresponding column is deleted from the fact table. I pick a new column for the two table join. It looks like data modeler will create five views, one fact table, four dimension tables and four joins from the source table. Next I'll make a few enhancements. I'll start by updating the revenue table and making all of the columns available for editing. I can use customer number to count my customers but the customer dimension is joined to the fact table on this column. It's preferable to create a join on columns that don't have aggregation methods because business requirements change over time and the meaning of the original aggregation method could become ambiguous. To eliminate the likelihood of this ambiguity, I can use the copy option to create a new column for customer number, and then add the aggregation method to the new column. I continue with my updates by renaming the columns to make them more user-friendly and then add aggregation rules for the measures. Six measures are defined and count distinct is selected as the aggregation method for the new customer number column. I can also update the column names in the dimension tables to suit the needs of my business. Next I review the model including products and revenue and then validate and publish it. The tables are now ready to use in reports. Finally I create a visualization that uses the data model. First, I pick a subject area as the data source. I use the ctrl key to pick columns I want and then drag them on to the canvas. The system selects the best visualization type for the set of data elements that I want to add to the canvas and intuitively picks the best locations in which to place them. In this video I showed you how to create a data model from a flat file that contains one fact and several dimension tables, add aggregation rules to the metrics,rename columns and then validate and publish the model. Find out more at docs.oracle.com