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Microsoft® SQL Server 2005 Analysis Services provides unified, fully integrated views of your business data to support online analytical processing (OLAP), key performance indicator (KPI) scorecards, and powerful data mining capabilities. It provides reliable business decision support solutions • SQL Server 2005 Analysis Services (SSAS) provides • Unified and integrated view of all your business data • Reporting, online analytical processing (OLAP) analysis • Key Performance Indicator (KPI) scorecards • Data mining
Advantages • Microsoft® SQL Server 2005 Analysis Services, organizations now have a single, consistent solution for reporting against either OLTP or OLAP data stores. • Reduces the amount of effort required to provide a consistent view of data that is integrated from an array of disparate applications and formats
Terminologies • Cube The basic unit of storage and analysis in Analysis Services is the cube. A cube is a collection of data that’s been aggregated to allow queries to return data quickly. • Dimension Each cube has one or more dimensions, each based on one or more dimension tables. A dimension represents a category for analyzing business data • Fact table A fact table contains the basic information that you wish to summarize. This might be order detail information, payroll records, or anything else that’s amenable to summing and averaging.
A schema is a collection of database objects, including tables, views, indexes, and synonyms. Star Schema • A relational database schema for representing multidimensional data. • It is the simplest form of data warehouse schema that contains one or more dimensions and fact tables. It is called a star schema because the entity-relationship diagram between dimensions and fact tables resembles a star where one fact table is connected to multiple dimensions. • The center of the star schema consists of a large fact table and it points towards the dimension tables. • The advantage of star schema are slicing down, performance increase and easy understanding of data.
Snowflake schema • A star schema structure normalized through the use of outrigger tables. i.e dimension table hierachies are broken into simpler tables. • In OLAP, this snow flake schema approach increases the number of joins and poor performance in retrieval of data. Since dimension tables hold less space, snow flake schema approach may be avoided.
Important aspects of Star Schema & Snow Flake Schema • In a star schema every dimension will have a primary key. • In a star schema, a dimension table will not have any parent table. • Whereas in a snow flake schema, a dimension table will have one or more parent tables. • Hierarchies for the dimensions are stored in the dimensional table itself in star schema. • Whereas hierarchies are broken into separate tables in snow flake schema. These hierarchies helps to drill down the data from topmost hierarchies to the lowermost hierarchies.
OLAP world, there are mainly 3 different types: • Multidimensional OLAP (MOLAP) • Advantages • Excellent performance • In MOLAP, data is stored in a multidimensional cube. • The storage is not in the relational database, but in proprietary formats. • MOLAP cubes are built for fast data retrieval, and are optimal for slicing and dicing operations. • Disadvantages: • It is limited in the amount of data it can handle. Because all calculations are performed when the cube is built, it is not possible to include a large amount of data in the cube itself. • It requires an additional investment in human and capital resources are needed.
Relational OLAP (ROLAP) This methodology relies on manipulating the data stored in the relational database • Advantages: • It can handle large amounts of data, ROLAP itself places no limitation on data amount • Disadvantages: • Performance can be slow. Because each ROLAP report is essentially a SQL query (or multiple SQL queries) in the relational database, the query time can be long if the underlying data size is large . • It is difficult to perform complex calculations .
Hybrid OLAP (HOLAP) refers to technologies that combine MOLAP and ROLAP. • Advantages • For summary-type information, HOLAP leverages cube technology for faster performance. • When detail information is needed, HOLAP can "drill through" from the cube into the underlying relational data.
Advantages of SSAS Cubes • SSAS is fast even on a large volume of data • SSAS calculated measures are fast execution-wise and easy reusable • They are defined centrally in the SSAS database, and the reports pick and choose the calculated measures they want.
To build a new data cube using BIDS, you need to perform these steps: • Create a new Analysis Services project • Define a data source • Define a data source view • Invoke the Cube Wizard
To create a new Analysis Services project, follow these steps: • Select Microsoft SQL Server 2005 > SQL Server Business Intelligence Development Studio from the Programs menu to launch Business Intelligence Development Studio.
To define a Data source for the new cube, follow these steps: • Right-click on the Data Sources folder in Solution Explorer and select New Data Source.
To create a new data source view, follow these steps: • Right-click on the Data Source Views folder in Solution Explorer and select New Data Source View.
BIDS will automatically display the schema of the new data source view
To create the new cube, follow these steps: • Right-click on the Cubes folder in Solution Explorer and select New Cube.
Aggregations & Aggregation Wizard • Pre calculated summaries of data from leaf levels
Aggregations • Aggregations provide performance improvements by allowing Microsoft SQL Server 2005 Analysis Services (SSAS) to retrieve pre-calculated totals directly from cube storage instead of having to recalculate data from an underlying data source for each query. • The Aggregation Design Wizard uses a sophisticated algorithm to select aggregations for pre calculation so that other aggregations can be quickly computed from the pre calculated values. • This technique saves processing time and reduces storage requirements, with minimal effect on query response time. • After the aggregation has been created, if the structure of a cube ever changes, or if data is added to or changed in a cube's source tables, it is usually necessary to review the cube's aggregations and process the cube again.
Aggregation Design Wizard. • Microsoft provides a nice wizard to generate aggregates on measure groups and partitions
MDX • Multidimensional Expressions (MDX) is the query language that you use to work with and retrieve multidimensional data in Microsoft SQL Server 2005 Analysis Services (SSAS). • MDX is superficially similar in many ways to the SQL syntax that is typically used with relational databases. However, MDX is not an extension of the SQL language and is different from SQL in many ways. • Basic MDX Select Query :
Calculations • Calculated members are customized measures or dimension members that are defined based on a combination of cube data, arithmetic operators, numbers, and functions. For example, you can create a calculated member that calculates the sum of two physical measures in the cube.
KPI’s • KPIs or Key Performance Indicators are one of the most important entities in driving business decisions. It can be defined as a (quantifiable) measurement used to define and measure an organization's progress in achieving business goals. • SQL Server 2005 Analysis Services, allows for the creation of KPIs on its cubes. • KPI measure the health of a business. • KPI uses graphic displays to display status and trend eg. Traffic light • KPI defines 4 expressions for performance metrics • Actual Value (-1 to 1) • Goal Value • Status (-1 to 1) • Trend (-1 to 1)
KPI Terms used in SSAS • Value • The value is an MDX expression used to return the actual value of the KPI • Goal • The goal is an MDX expression used to specify the target value of the KPI. • Status • Ideal values for the status would be a max of 1 (good) to a minimum of -1 (bad), while 0 indicates neutral status • Status Indicator • The status indicator is a visual element which is used to present the status of the KPI. Eg gauges, traffic lights or smileys. • Trend • The trend is an MDX expression that evaluates the value of a KPI across time. It can be expressed using any time based criteria. Using this, the business user will be able to determine how the KPI's value has progressed over time. • Trend Indicator • The trend indicator is a visual element which is used to present the trend of the KPI.
The KPIs are done! Next, process the cube. You will be able to view the KPIs using the built-in KPI Browser under the KPIs tab in BIDS.
Actions • Cube supports actions and action taken in basis of data • URL: Go to a specified URL. This type of action supports both directing the user to some URL to obtain further information, and directing the user to some Web-based application that allows a new task to be performed. • For example:For a product, go to the company website describing that product. • Reporting • Execute a specified report. For eg: for a given product code the action could execute a parameterized report providing description and current order status • Drill through • User can drill through to the lowest level of detail.
The most important aspect of it is that drill through returns detail level data from within the cube. • The target can be a cube, dimension, hierarchy, level, dimension members, hierarchy members, level members, set, cells, etc. An action that targets cells can be further restricted to a subspace of the cube using an MDX expression.
Partitions • A database partition is an independent subset of a database that contains its own data, indexes, configuration files, and transaction logs. • A partition group is a logical grouping of one or more database partitions that lets you control the placement of table spaces and buffer pools within the database partitions.
Security • Cube provide role based security. Roles can be defined and permissions can be granted to the role. • Administrative permissions can be granted independently of data access permissions. Also, separate permissions can be defined for reading the metadata of the object, and for read/write access to the data. • Data can be secured at levels of granularity down to individual cells.
Perspectives Users engaged on a particular task generally do not have to see the complete model. To avoid overwhelming users with the sheer size of the model, we need the ability to define a view that shows a subset of the model • The cube provides such views, called perspectives. A cube can have many perspectives, each one presenting only a specific subset of the model (measures, dimensions, attributes, and so on) that is relevant to a particular group of users. Each perspective can then be associated with the user security roles that define the users who are permitted to see that perspective.
Translations • International users frequently have a need to view metadata in their local language. To address this, the cube allows translations of metadata to be provided in any language. A client application that connects using a particular locale would receive all metadata in the appropriate language. • The model can also provide translations of data. An attribute can map to different elements in the data source, and provide the translations for those elements in different languages.
From a client computer that has a French locale, both the cube and the query results would be displayed in French
