An instance of SQL Server Analysis Services Tabular plus client compo- nents. .. Some database professionals, accustomed to relational data model-. SQL Server Tutorials: Analysis Services -. Multidimensional Modeling. SQL Server Books Online. Summary: This tutorial describes how to use SQL. Analysis Services Architecture: One Product, Two Models . Working with SQL Server Management Studio. This book is aimed at professional Business Intelligence developers: consultants or members.
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Professional Microsoft SQL Server Analysis Services with MDX and DAX ( eBook, PDF). Leseprobe · Professional Microsoft SQL Server Analysis. xiii. Introduction. Microsoft SQL Server Analysis Services is the multidimensional online analytical pro- cessing (OLAP) component of Microsoft SQL Server. book Professional Microsoft SQL Server Integration Services, and has While SQL Server was a large jump forward for SSIS, SQL Server
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Discover how to: DAX Basics. Download the sample content. Click the following link: Download the companion content. Click Save. Locate the. Right-click the file, click Extract All, and then follow the instructions. Parent, [Measures]. One example of this is the case of finance models where you have a chart of accounts usually in a parent-child format with specific rollup logic required for each account.
Choosing a Data Modeling Paradigm in SQL Server Analysis Services 15 Not only do multidimensional models provide native support for parent-child hierarchies, they also provide built-in account intelligence, which enables you to easily apply unary operators and MDX formulas at the account level that drive the data rollup.
In tabular models, parent-child or account intelligence is not built-in, but you can build your own solution using a combination of calculated columns and measures to build out the parent child hierarchy and apply the custom rollup.
Semiadditive Measures Generally speaking, semiadditive measures are those that uniformly aggregate across all dimensions except for date. Examples of semiadditive measures include opening balance and closing balance. For these measures, you want to apply special logic to correctly summarize the data by time period.
After all, the inventory stock-on-hand balance for the month of March is not the sum of the stock-on-hand for all days in March. In addition, this balance should also correctly work across all date attributes like quarter and year. For example, the Q1 inventory stock-on-hand balance should be the same balance that was reported on March 31st assuming March 31st is the last day in Q1. If these aggregate functions do not satisfy your specific logic requirements, you can also write custom MDX formulas.
There are additional functions that apply across other date attributes like quarter and year. Time intelligence includes being able to calculate year-to-date summaries and perform prior year comparisons. Multidimensional models provide out-of-the-box time intelligence through the Analysis Services Business Intelligence Wizard. Using this wizard, time calculations can be added to the design of the time dimension and also applied to all measures in the model.
Although using the wizard is one way to build time calculations, you can also write your own MDX calculations within the multidimensional model. KPIs Key performance indicators KPIs identify special measures that you want to monitor against a target value using a visual indicator such as a stoplight.
Both multidimensional and tabular models provide support for KPIs.
Both provide the ability to assign a target for a measure and to use the comparison of actual to target to assess the performance status of the measure. Currency Conversion Currency conversions require you to convert currency data from one or more source currencies into one or more reporting currencies. For example if your organization processes sales transactions in EUR, JPY, and USD, in order to consolidate sales reporting across the entire organization, you will need to convert the sales transactions into one or more reporting currencies.
To implement currency conversions in either modeling experience, you must have access to the currency exchange rate data and include that data in your model. In multidimensional models, you can use the Analysis Services Business Intelligence Wizard to create MDX currency conversion calculations that are optimized to support multiple source and reporting currencies.
In a tabular model you can build your own currency conversion solution by creating DAX formulas. Named Sets In multidimensional modeling named sets provide a way for you to return a set of dimension members that are commonly used in reporting applications. For example, you may want to create a named set to return the last 12 months.
Creating this named set within your cube enables you to centrally define the set logic, to access the set from any reporting application, and to simplify the logic stored within your reporting application. Lag 11 :Max [Date]. As each of the modeling experiences leverage different underlying technologies, they have different performance characteristics and behaviors that you must understand to properly consider which modeling experience best fit your needs.
In MOLAP, data is stored on disk in an optimized multidimensional format with typical 3x compression. When you think about performance, it is generally useful to think about it into two buckets: query performance and processing performance.
Query Performance Query performance directly impacts the quality of the end-user experience. As such, it is the primary benchmark used to evaluate the success of an OLAP implementation.
Analysis Services provides a variety of mechanisms to accelerate query performance, including aggregations, caching, and indexed data retrieval. In addition, you can improve query performance by optimizing the design of your dimension attributes, cubes, and MDX queries. One of the primary ways to optimize query performance is the use of aggregations. An aggregation is a precalculated summary of data that is used to enhance query performance for multidimensional models. When you query a multidimensional model, the Analysis Services query processor decomposes the query into requests for the OLAP storage engine.
For each request, the storage engine first attempts to retrieve data from the storage engine cache in memory.
If no data is available in the cache, it attempts to retrieve data from an aggregation. Designing data aggregations involves identifying the most effective aggregation scheme for your querying workload. As you design aggregations, you must consider the querying benefits that aggregations provide compared with the time it takes to create and refresh the aggregations.
In fact, adding unnecessary aggregations can worsen query performance because the rare hits move the aggregation into the file cache at the cost of moving something else out.
Caching is also important for Analysis Services query performance tuning. You should have sufficient memory to store all dimension data plus room for caching query results. During querying, memory is primarily used to store cached results in the storage engine and query processor caches. To optimize the benefits of caching, you can often increase query responsiveness by preloading data into one or both of these caches. Processing Performance Processing is the operation that refreshes data in an Analysis Services database.
The faster the processing performance, the sooner users can access refreshed data.
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Analysis Services provides a variety of mechanisms that you can use to influence processing performance, including efficient dimension design, effective aggregations, partitions, and an economical processing strategy for example, incremental vs. You can use partitions to separate measure data typically fact table data into physical units.
Effective use of partitions can enhance query performance, improve processing performance, and facilitate data management. For each partition, you can have a separate aggregation design and a separate refresh schedule, which can greatly optimize processing performance.
This type of partitioning strategy can be used to provide real-time querying or can be used to provide access to data sets too large to process into a cube.
Using query and processing optimization techniques like these can help you scale your multidimensional models to handle terabytes of data.
Tabular Models Tabular models use the xVelocity analytics engine, which provides in-memory data processing, or DirectQuery, which passes queries to the source database to leverage its query processing capabilities.
The benefits of columnar databases and in-memory data processing go hand in hand. Columnar databases achieve higher compression than traditional storage, typically 10x compression depending on the data cardinality.
Data cardinality focuses on characterizing the data distribution within a single column. High data cardinality means that the data values within a column are highly unique for example, customer number. Low data cardinality means that the data values within a column can repeat for example, gender and marital status.
The lower the data cardinality, the higher the compression, which means that more data can fit into memory at a given time. As data modelers, it is important to understand the cardinality of your data to determine which data sets are best suited for your tabular model as well as the associated memory requirements to support the model. This approach can provide very high query performance without requiring special tuning and aggregation management. The best and easiest way to optimize query performance for tabular models is to maximize available memory.
It is highly recommended that you provide sufficient memory to contain all of the data in your tabular model.
Microsoft Analysis Services
In scenarios where memory is constrained, the in-memory engine also provides basic paging support according to physical memory. In addition, there are server-side configuration settings that allow IT to more finely manage the memory available to tabular models. Both of these differences mean there is less overhead with each data refresh which in turn can enable quicker turnaround times and greater agility.
Consider this example. In your organization it is common for sales reps to move from one region to another on a regular basis. Business users want to see the sales data rolled up by the latest region and sales rep assignments. In a multidimensional model, to accomplish this task, you must first refresh your sales organization dimension. After the sales organization dimension has been refreshed, you must refresh the sales measure group partition.
Refreshing the sales partition updates both the detailed data and aggregations. The final step in your data preparation as a best practice is to warm the Analysis Services query cache to retrieve the most useful data from disk into memory.
Depending on your data model design, the data size, and your specific choice in processing techniques incremental vs. The good news is that there are a variety of proven techniques that BI professionals use every day to optimize the processing footprint of multidimensional models as they balance data processing requirements with data availability demands. Now consider the same scenario in a tabular model. In the tabular model, there is no concept of dimensions and measure groups.
Instead, data is organized into tables that have relationships to each other.
Assume that sales organization data and sales data are each in their own respective tables with a relationship based on the individual sales rep. With this design, when you refresh your sales organization table, it automatically updates any impacted calculated columns, relationships, and user hierarchies. This means that the sales data automatically reflects the updated sales region rollups without the need to reprocess the sales data.
This flexibility can provide significant benefits when you have rapidly changing dimensions and you need the data to reflect the latest updates. In addition, note that with tabular models, it is also not necessary to build aggregations, write data to disk, or warm the query cache to get the data into memory. With tabular models, data moves from disk directly into memory and is ready to go.
Choosing a Data Modeling Paradigm in SQL Server Analysis Services 21 Similar to multidimensional models, tabular models enable you to break your table data into partitions, eliminating the need for unnecessary data processing.
For example, you can break down larger tables into multiple partitions, such as one monthly partition for each month in the current year and then one yearly partition for each of the prior years. This approach enables you to isolate those processing partitions that require a refresh. DirectQuery As an alternate to the xVelocity in-memory mode of tabular models, BI professionals can also build tabular models using DirectQuery mode.
DirectQuery provides you with the ability to bypass data processing by passing DAX queries and calculations to the source database to leverage the capabilities of SQL Server. This can be especially useful with large data volumes that require frequent refreshing.
This API was created before tabular modeling was added to Analysis Services and so it only contains classes for objects traditionally associated with multidimensional modeling: cubes, dimensions, measures groups, MDX scripts, and so on. However, this API can also be used for developing and managing tabular models.
This is a benefit of multidimensional and tabular modeling being encapsulated by the BI Semantic Model. While internally tabular and dimensional models are distinct, the BISM presents the same external interface.
Although you can use AMO for programming both tabular and multidimensional models, it is a less intuitive interface for tabular models. Organizations must control data access in order to keep their data assets secure and to comply with privacy regulations. Both multidimensional models and tabular models offer a set of robust capabilities that satisfy a broad range of security requirements.
There are subtle differences in capabilities which are important to understand before choosing the modeling experience that will best meet your security needs.
In Analysis Services, you manage multidimensional and tabular project security by creating a role and granting permissions to the role. To implement dimension data security for a role, you grant or deny access to dimension data by selecting or deselecting dimension members.
You can also implement a more complex security configuration by defining a set of members using an MDX expression. You also specify whether the role should be granted or denied access to new dimension members. The access you grant or deny to a dimension member impacts the access a role has to related dimension members.
For example, if you limit a role so that it can only access the Mountain Bikes product subcategory, members of the role can only view the Bikes product category and the products and sales that belong to the Mountain Bikes subcategory.
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In a tabular project, you implement row-level security by granting access to rows in a table. The role has access to new table rows if they satisfy the DAX filter. The access you grant to a row in one table impacts the access a role has to rows in related tables.
If two tables have a one-to-many relationship, row filters on the table on the one side of the relationship filter rows in the table on the many side of the relationship, but not the other way around. For example, if you limit a role so that it can only view the Mountain Bikes row in the product subcategory table, members of the role can only view rows in the products and sales tables that are related to the Bikes subcategory.
However, members of the role can still view all the rows in the product category table Bikes, Clothing, and so on. For example, associates are only allowed to see their own performance and HR data. However, creating a security role for each individual in an organization may be impractical.
Instead, you can implement dynamic security, which provides the capability to drive security logic based on a user ID or some other dynamic criteria. Both tabular and multidimensional projects support dynamic security. You can configure dynamic, user-based security if your data contains a relationship between user IDs and the data users have permission to access by including the relationship in the MDX or DAX expression that you are using to manage permissions.
Cell-Level and Advanced Security For many applications, there is a need to restrict data access using more complex criteria than simply a row in a table.
Take, for example, an employee satisfaction survey that shares aggregate results from a feedback survey. These models often contain highly sensitive data, and individual survey responses must be kept protected.
In these cases, you may want to implement more complex logic that looks at the sample size and only allows access to the resulting measure if the number of responses are greater than a certain response count. Furthermore, there may be specific questions and metric combinations you would like to specifically restrict for only HR to see.
Multidimensional projects natively Choosing a Data Modeling Paradigm in SQL Server Analysis Services 23 allow you to implement advanced security capabilities not available in a tabular project. In a multidimensional project you can implement cell-level security to restrict access to a particular cell or group of cells in your model.
Cell-level security is not provided in a tabular model. In addition, multidimensional projects also enable you to control the use of visual totals, grant or deny permission to drill through to detail data, and create default members for each role. In a multidimensional project, preaggregated summary values are calculated when data is processed into a model in order to improve query response times. For example, the Sales of All Products is a precalculated value. Dimension data security is applied after the data is processed, so even if a user is only granted permission to access the Bikes category, by default the value of Sales for All Products will be the sum of sales for Accessories, Bikes, Clothing, and so on.
This may or may not be the value that you want members of the role to see. In this case, if you want the value of Sales for All Products to be limited to the value of Sales for Bicycles, you must enable visual totals. Enabling visual totals restricts the summary values so that they must be equal to the sum of the detail values that a role has permission to access.
This change impacts query response time, because summary values must be calculated at query time. Tabular projects do not precalculate summary values, so summary values are always equal to the sum of detail values, that is, visual totals are always enabled in a tabular model. In a multidimensional model you can enable permission to drill through to detail data on a role-by-role basis.
In a tabular model, roles are not used to control access to drillthrough capability. Instead all roles are able to drill through to detail. In a multidimensional model you can specify a default member for each attribute in a dimension.
A default member behaves like a filter that is automatically applied. For example, if the default member of Year is , then by default, only data for is displayed. However, a user can choose to see data from a different year or to see data for all years. In a multidimensional model, you can configure a default member for each attribute that applies to all roles or you can specify a different default member on a role-by-role basis.
In a tabular model, you cannot specify a default value. Instead, if you want a default filter, you will need to configure that capability in your reporting and analysis tool. The two modeling experiences encapsulated in the BISM, multidimensional and tabular modeling, provide complementary features that enable you take advantage of capabilities that best meet your needs.
Tabular modeling provides a readily accessible modeling experience with capabilities that will satisfy most reporting and analysis needs.
Most users are familiar with working with tables and relationships and quickly learn to implement business logic using the Excel-like DAX language. The ease of use and simplified and flexible modeling provided Choosing a Data Modeling Paradigm in SQL Server Analysis Services 24 by the tabular experience means that solutions can be developed quickly.
The in- memory, column-oriented xVelocity engine provides extremely fast query response for data sets that may contain billions of records. Multidimensional modeling provides extensive capabilities to help you manage your most complex and largest-scale BI challenges.
The multidimensional data model combined with MDX provides out-of-the-box functionality so that you can create sophisticated models and implement complex business logic.
On-disk data storage, precalculated aggregates, and in-memory caching enable multidimensional models to grow to multi-terabyte scale and provide fast query response.After the sales organization dimension has been refreshed, you must refresh the sales measure group partition. This is a benefit of multidimensional and tabular modeling being encapsulated by the BI Semantic Model.
Currency Conversion Currency conversions require you to convert currency data from one or more source currencies into one or more reporting currencies. In some situations, it is useful to create calculations that navigate the hierarchy.
Yuli Widiyatmoko. Download the errata. If you prefer, you can watch the tutorial about how to geocode addresses using QGIS at the bottom of this page. Although comprehensive data modeling and sophisticated analytics are important benefits of multidimensional modeling, they often come with the tradeoff of longer development cycles as well as decreased ability to quickly adapt to changing business conditions.
I would say this is a must have book for the person who is responsible for designing architecture of data warehouse using Microsoft technologies and for anyone who wants to know how to do that.
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