Dimensional Data Warehousing with MySQL: A Tutorial by Djoni Darmawikarta

By Djoni Darmawikarta

Laptop programmers who have to construct a knowledge warehouse will locate proper examples and data written in an intensive, easy-to-follow variety during this step by step educational.

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4 using this command. mysql> \. sql The result is this. 09 sec) Again, the query produces a total order amount of 58,000 (15,000+23,000+20,000). 4, sums the order amounts across the date and order dimensions. 5 using this command. mysql> \. sql You should see the following on your console. 03 sec) The total is again 58,000 (1,000+6,000+1,000+8,000+8,000+4,000+4,000+10,000+6,000+10,000). All the four queries produce the same total (58,000), which confirms that this measure is fully-additive. Summary In this chapter you learned measure additivity.

3 gives you the annual sales summary. The order amounts and the number of orders are not only aggregated by date, but also by product and customer city. The three joins, between the fact table and each of the three dimension tables (date, product, and customer dimensions), are on the surrogate keys. customer_sk GROUP BY year, product_name, customer_city ORDER BY year, product_name, customer_city ; /* end of script */ Run the script as follows: mysql> \. 03 sec) The query result presents the annual total order amounts (sum) and number of orders (count) of all orders grouped by years, products, and cities.

When you populate the date_dim table by loading dates from the source, your date_dim table will store only the dates that are used, saving you disk space. Unfortunately, this method is more complex because you must load all dates to the date dimension from your data sources that have dates. 3 loads the sales order dates from the sales_order table in the source database into the date_dim table. You use the DISTINCT keyword in the script to make sure no duplicates are loaded. 3, truncate the date_dim table.

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