Olap softwares

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OLAP Tools. Arts and Crafts. Broadcast Media. Building Materials. Business Supplies and Equipment. Capital Markets. Civil Engineering. Commercial Real Estate. Computer Hardware. Computer Networking. Computer Software. Consumer Electronics. Consumer Goods. For more information, see Design the layout and format of a PivotTable report.

Office display language A cube administrator can define translations for data and errors on the server for users who need to see PivotTable information in another language. If a database on an OLAP server is available on your network, you can retrieve source data from it directly.

If you have an offline cube file that contains OLAP data or a cube definition file, you can connect to that file and retrieve source data from it. Data sources A data source gives you access to all of the data in the OLAP database or offline cube file. For more information, see Use Microsoft Query to retrieve external data. If you work with PivotTable reports and PivotChart reports from both OLAP source data and other types of source data, you will notice some feature differences.

With other types of external source data, you query for all the source data at once, or you can set options to query only when you display different report filter field items. You also have several other options for refreshing the report. In reports based on OLAP source data, the report filter field settings are unavailable, background query is unavailable, and the optimize memory setting is not available. Field types OLAP source data, dimension fields can be used only as row series , column category , or page fields.

Measure fields can be used only as value fields. For other types of source data, all fields can be used in any part of a report.

Access to detail data For OLAP source data, the server determines what levels of detail are available and calculates summary values, so the detail records that make up summary values may not be available. The server may, however, provide property fields that you can display. Other types of source data don't have property fields, but you can display the underlying detail for data field values and for items, and you can show items with no data.

You can then sort or manually rearrange the items. For other types of source data, the items in a new report first appear sorted in ascending order by item name. Calculations OLAP servers provide summarized values directly for a report, so you cannot change the summary functions for value fields.

For other types of source data, you can change the summary function for a value field and use multiple summary functions for the same value field. You cannot create calculated fields or calculated items in reports with OLAP source data.

With other types of source data, you can change subtotal summary functions and show or hide subtotals for all row and column fields.

For OLAP source data, you can include or exclude hidden items when you calculate subtotals and grand totals. For other types of source data, you can include hidden report filter field items in subtotals, but hidden items in other fields are excluded by default. Need more help? Expand your skills. Get new features first. Was this information helpful? Yes No. Thank you! Any more feedback? This makes it easier for end users to query data without performing aggregates and joins over the underlying schema.

Also, usually columns are renamed to more user-friendly names, so that the context and meaning of the data are more obvious. Semantic modeling is predominately used for read-heavy scenarios, such as analytics and business intelligence OLAP , as opposed to more write-heavy transactional data processing OLTP. This is mostly due to the nature of a typical semantic layer:.

An organization has data stored in a large database. It wants to make this data available to business users and customers to create their own reports and do some analysis. One option is just to give those users direct access to the database. However, there are several drawbacks to doing this, including managing security and controlling access.

Also, the design of the database, including the names of tables and columns, may be hard for a user to understand. Users would need to know which tables to query, how those tables should be joined, and other business logic that must be applied to get the correct results. Users would also need to know a query language like SQL even to get started. Typically this leads to multiple users reporting the same metrics but with different results. Another option is to encapsulate all of the information that users need into a semantic model.

The semantic model can be more easily queried by users with a reporting tool of their choice. The data provided by the semantic model is pulled from a data warehouse, ensuring that all users see a single version of the truth. The semantic model also provides friendly table and column names, relationships between tables, descriptions, calculations, and row-level security.

OLAP is especially useful for applying aggregate calculations over large amounts of data. OLAP systems are optimized for read-heavy scenarios, such as analytics and business intelligence.

OLAP allows users to segment multi-dimensional data into slices that can be viewed in two dimensions such as a pivot table or filter the data by specific values. This process is sometimes called "slicing and dicing" the data, and can be done regardless of whether the data is partitioned across several data sources.

This helps users to find trends, spot patterns, and explore the data without having to know the details of traditional data analysis.