Microsoft sql server olap services version 7

By joining Download. Free YouTube Downloader. IObit Uninstaller. Internet Download Manager. Advanced SystemCare Free. VLC Media Player. MacX YouTube Downloader. Microsoft Office YTD Video Downloader. Adobe Photoshop CC. VirtualDJ Avast Free Security. WhatsApp Messenger. Talking Tom Cat. Clash of Clans. OLAP features in Excel. Software components that you need to access OLAP data sources. A business analyst often wants to get a big picture of the business, to see broader trends based on aggregated data, and to see these trends broken down by any number of variables.

Business intelligence is the process of extracting data from an OLAP database and then analyzing that data for information that you can use to make informed business decisions and take action. For example, OLAP and business intelligence help answer the following types of questions about business data:. How much money did customers over the age of 35 spend last year, and how has that behavior changed over time?

For each customer age group, what is the breakdown of profitability both margin percentage and total by product category? OLAP is a database technology that has been optimized for querying and reporting, instead of processing transactions. OLAP data is derived from this historical data, and aggregated into structures that permit sophisticated analysis. OLAP data is also organized hierarchically and stored in cubes instead of tables.

It is a sophisticated technology that uses multidimensional structures to provide rapid access to data for analysis. This organization makes it easy for a PivotTable report or PivotChart report to display high-level summaries, such as sales totals across an entire country or region, and also display the details for sites where sales are particularly strong or weak.

OLAP databases are designed to speed up the retrieval of data. Because the OLAP server, rather than Microsoft Office Excel, computes the summarized values, less data needs to be sent to Excel when you create or change a report. This approach enables you to work with much larger amounts of source data than you could if the data were organized in a traditional database, where Excel retrieves all of the individual records and then calculates the summarized values. OLAP databases contain two basic types of data: measures, which are numeric data, the quantities and averages that you use to make informed business decisions, and dimensions, which are the categories that you use to organize these measures.

OLAP databases help organize data by many levels of detail, using the same categories that you are familiar with to analyze the data. Cube A data structure that aggregates the measures by the levels and hierarchies of each of the dimensions that you want to analyze. Cubes combine several dimensions, such as time, geography, and product lines, with summarized data, such as sales or inventory figures.

Cubes are not "cubes" in the strictly mathematical sense because they do not necessarily have equal sides. However, they are an apt metaphor for a complex concept.

Measure A set of values in a cube that are based on a column in the cube's fact table and that are usually numeric values. Measures are the central values in the cube that are preprocessed, aggregated, and analyzed. Common examples include sales, profits, revenues, and costs. Member An item in a hierarchy representing one or more occurrences of data.

A member can be either unique or nonunique. For example, and represent unique members in the year level of a time dimension, whereas January represents nonunique members in the month level because there can be more than one January in the time dimension if it contains data for more than one year.

Calculated member A member of a dimension whose value is calculated at run time by using an expression. Calculated member values may be derived from other members' values. For example, a calculated member, Profit, can be determined by subtracting the value of the member, Costs, from the value of the member, Sales. Dimension A set of one or more organized hierarchies of levels in a cube that a user understands and uses as the base for data analysis. If so, narrow your options to those that support real-time analytics.

Real-time analytics in this context applies to a single data source, such as an enterprise resource planning ERP application, that will run both an operational and an analytics workload. If you need to integrate data from multiple sources, or require extreme analytics performance by using pre-aggregated data such as cubes, you might still require a separate data warehouse.

Do you need to use pre-aggregated data, for example to provide semantic models that make analytics more business user friendly? If yes, choose an option that supports multidimensional cubes or tabular semantic models. Providing aggregates can help users consistently calculate data aggregates.

Pre-aggregated data can also provide a large performance boost when dealing with several columns across many rows. Data can be pre-aggregated in multidimensional cubes or tabular semantic models. Do you need to integrate data from several sources, beyond your OLTP data store?

If so, consider options that easily integrate multiple data sources. For more information, see Pipeline orchestration, control flow, and data movement. Use a domain Active Directory account instead. Skip to main content.

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