By Manpreet Singh, Sha Kanjoor Anandan, Sahil Malik, Srini Sistla, Steve Wright
To get forward in present day hyper-competitive market, what you are promoting has to exploit the knowledge you have already got and mine that information to offer you new perception, metrics, and clues to what drives profitable consumer interactions. In professional SharePoint 2013 enterprise Intelligence suggestions, you will study precisely how one can release that magic, construct company intelligence amenities on SharePoint, and glean insights from info to propel your small business to the subsequent point. to appreciate a number of the company intelligence choices in SharePoint 2013, you want to comprehend the middle SQL Server enterprise intelligence suggestions, and the 1st a part of the booklet provides a finished instructional on these basics.
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Extra info for Pro SharePoint 2013 Business Intelligence Solutions
6. Writing an MDX query is similar to writing a T-SQL query. However, instead of querying from tables, MDX queries from a cube’s measures and dimensions. 7. Enter your MDX query in the query window, and execute to view the output, as shown in Figure 1-35. info Chapter 1 ■ Business Intelligence Basics Figure 1-35. Executing a simple MDX query OK, so far you’ve seen the basic cube (using one from the AdventureWorks database) and also how to query from the cube using a simple MDX query. Let’s now begin a simple exercise to build a cube from scratch.
In the query window, type the MDX query to retrieve the values from the cube. In this case, to get the Sum of Sales YTD in the Sales Territory Measure, the query should look like the one in Figure 1-49. 47. Execute and parse the query to view the output in the results window. Final Pointers for Migrating OLTP Data to OLAP Now that you know the concepts, let’s see some of the important factors to keep in mind while planning for a migration. First, you need to be completely aware of why you need to migrate to an OLAP database from an OLTP database.
Note Make sure the column data types are in sync; or (at least) while extracting and loading the data, alter the data so that it matches the column types. info Chapter 1 ■ Business Intelligence Basics You might be wondering how we arrived at the design of the tables. Rule #1 when you create a schema targeting a cube is to identify what you would like to accomplish. In this case, we would like to know how Sales did with stores, territories, and customers, and across various date and time factors.