These aggregations, for example Turnover by Year and Region, are used when a business user queries the cube for this type of information. Therefore the query response time can be very short. When the query is fired SSAS does not have to calculate the outcome from the underlying details like T-SQL has to do , but can take the values directly from the stored aggregations. Besides that SSAS stores query-results in a cache. So the next time the same type of query is fired, it will try to get it from the cache.
This is typically the stuff decision-makers in the organisation want to have. Performance and Scalability An SSAS database is designed as a product and by the developers of the solution to support fast query response times and large datasets. Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in:. Email required Address never made public.
Name required. Follow Following. Insight Quest Join other followers. Sign me up. Already have a WordPress. Data Marts consisting of dimension and fact tables work best for Analysis Services. SSAS is part of a larger data warehouse for enterprises. The data might be scattered in multiple systems and the data warehouse brings them together in one reporting system.
SSAS helps bring this data together with faster retrieval and the creation of aggregations or measures. This was through the acquisition of Panorama Software. The first Tabular Model was in the release. Tabular was originally built as a multiuser solution for PowerPivot embedded in Excel.
This model was obtained when Microsoft bought the VertiPak engine and developed xVelocity. The mature Multidimensional version is still used in enterprises, but with the release of and , the path forward looks to be the Tabular Model of SSAS. The transformation from Cubes to Tabular means going from disk based to memory based in addition to using column storage instead of row storage. Kind of sounds like where the database engine is progressing — columns store indexes and in-memory tables.
This is all to help speed things up and move towards real time analytics. Both models can slice and dice measures through the attributes or hierarchies of dimensions.
Figure 4 shows a data mart structure. This represents a star schema with a fact table related to dimension tables. The center of the star schema is the Sales fact table. The grain of this table is Sales Line Item. The key is the combination of Sales Key and Invoice Date Key which are surrogate keys, not business keys.
Data Marts do not use business keys to uniquely identify rows, but these are used for lookups when importing data. Surrogate keys are generated during the import or ETL process for these databases. This follows dimensional modeling practices which have been around for more than 20 years. The dimension tables are the entry to sales data like region of sale, color of item, or year of sales. The dimension as well as fact tables are flattened relational tables from one or more transactional systems.
The Date dimension has rows for all possible dates, not just dates that have data. The date dimension can be related to more than one field in the fact table.
Figure 5 zooms in one the date relationship. Both are integer data types, not date or datetime data types. The structure also includes just the date portion of the original column and not the time The time portion can be added but it would be in a different key column in the fact like Invoice Time Key.
This time key column would also be related to a Time dimension and not the Date Dimension in Figure 5. The other dimensions are City, Employee, and Customer. Employee and Customer are typical lookups for invoices, but City seems out of place.
The City is the geography of the Sale. This is separated because the dimension table can and is related to other Fact tables like Orders and Inventory.
This way, slicing by Region in the City dimension can be compared to measures in other fact tables. These form a hierarchy for slicing and dicing data in a report. It also helps the OLAP database to pre-aggregate results for the lower levels of the hierarchy for quick measure computation at a higher level.
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