Each row shows:. In addition to the data, this file includes the pivot tables that I showed in my blog post on hockey player data analysis. For example, one pivot table shows player counts, for each position, plus age, height and weight. Get this data, with the data analysis, in Excel format, in the download section at the end of this page. After you copy the Office Supply Sales sample data from the table above, and paste it into Excel, you can format the data as a named Excel table.
This will make it easier to sort and filter the data. Watch this short video to see the steps, and there are written instructions on the Creating an Excel Table page. Office Supply Sales : To get the sample office supply sales data in an Excel file, click this link: Office Suply Sales sample data workbook. When working with big data, there are a number of technologies and techniques that can be applied to make these three patterns successful. Many customers use a connection to bring external data into Excel as a refreshable snapshot.
The advantage here is that it creates a self-contained document that can be used for working offline, but refreshed with new data when online.
Since the data is contained in Excel, customers can also transform it to reflect their own personal context or analytics needs. When importing big data into Excel, there are a few key challenges that need to be accounted for:. Sometimes, either the sheer volume of data or the pattern of the analysis mean that importing all of the source data into Excel is either prohibitive or problematic e.
Customers using OLAP PivotTables are already intimately familiar with the power of combining lightweight client side experiences in PivotTables and PivotCharts with scalable external engines. Interactively querying external sources with a business-friendly metadata layer in PivotTables allows users to explore and find useful aggregations and slices of data easily and rapidly. Once a user has created a data model, the process of turning it into a SQL Server Analysis Services cube is relatively straightforward for a BI professional, which in turn enables a centrally managed and controlled asset that can provide sophisticated security and data partitioning support.
As new technologies become available, look for more connectors that provide this level of interactivity with those external sources. This typically creates a static export of a subset of data in the source application, typically exported for reporting purposes, free from the underlying business rules.
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