SQL Analysis Services 2008

7/30/2019 SQL Analysis Services 2008

1/47

SQL Analysis Services 2008


2/47

Microsoft SQL Server 2008 Analysis Services providesunified, fully integrated views of your business datato support online analytical processing (OLAP), key

performance indicator (KPI) scorecards, and powerfuldata mining capabilities. It provides reliable businessdecision support solutions

SQL Server 2008 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


3/47

Advantages

Microsoft SQL Server 2008 Analysis Services, organizations now have a

single, consistent solution for reporting against either OLTP or OLAP datastores.

Reduces the amount of effort required to provide a consistent view of

data that is integrated from an array of disparate applications and formats


4/47

Terminologies

The basic unit of storage and analysis in Analysis Services is the cube.A cube is a collection of data thats been aggregated to allow queriesto return data quickly.

Each cube has one or more dimensions, each based on one or moredimension tables. A dimension represents a category for analyzingbusiness data

A fact tablecontains the basic information that you wish to summarize.

This might be order detail information, payroll records, or anythingelse thats amenable to summing and averaging.


5/47


6/47

ARCHITECTURE


7/47

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 whereone fact table is connected to multiple dimensions.

The center of the star schema consists of a large fact table and it points towardsthe dimension tables.

The advantage of star schema are slicing down, performance increase and easyunderstanding of data.
http://download-uk.oracle.com/docs/cd/B10501_01/server.920/a96520/glossary.htm


8/47

A star schema structure normalized through the use of outrigger tables. i.e dimensiontable hierachies are broken into simpler tables.

In OLAP, this snow flake schema approach increases the number of joins and poorperformance in retrieval of data. Since dimension tables hold less space, snow flakeschema approach may be avoided.


9/47

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 ormore parent tables.

Hierarchies for the dimensions are stored in the dimensional table itselfin star schema.

Whereas hierarchies are broken into separate tables in snow flakeschema. These hierarchies helps to drill down the data from topmosthierarchies to the lowermost hierarchies.


10/47

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 allcalculations are performed when the cube is built, it is not possibleto include a large amount of data in the cube itself.

It requires an additional investment in human and capital resourcesare needed.


11/47

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 ondata amount

Disadvantages:

Performance can be slow. Because each ROLAP report is essentially a SQLquery (or multiple SQL queries) in the relational database, the query timecan be long if the underlying data size is large .

It is difficult to perform complex calculations .


12/47

Hybrid OLAP (HOLAP) refers to technologies that combine MOLAP andROLAP.

Advantages

For summary-type information, HOLAP leverages cube technology forfaster performance.

When detail information is needed, HOLAP can "drill through" fromthe cube into the underlying relational data.


13/47

Advantages of SSAS Cubes


14/47

To build a new data cube using BIDS, you needto perform these steps:

Create a new Analysis Services project

Define a data source

Define a data source view

Invoke the Cube Wizard


15/47

To create a new Analysis Services project,

follow these steps: Select Microsoft SQL Server 2008 > SQL Server Business Intelligence

Development Studio from the Programs menu to launch Business Intelligence

Development Studio.


16/47

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.


17/47

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.


18/47

BIDS will automatically display the schema

of the new data source view


19/47

To create the new cube, follow these steps:

Right-click on the Cubes folder in Solution Explorer and select New Cube.


20/47

Deploying ,Processing, Browsing a Cube


21/47


22/47

Aggregations & Aggregation Wizard

Pre calculated summaries of data from

leaf levels


23/47

Aggregations

Aggregations provide performance improvements by allowing Microsoft SQLServer 2008 Analysis Services (SSAS) to retrieve pre-calculated totals directlyfrom cube storage instead of having to recalculate data from an underlying datasource for each query.

The Aggregation Design Wizard uses a sophisticated algorithm to selectaggregations 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 necessaryto review the cube's aggregations and process the cube again.


24/47

Aggregation Design Wizard.

Microsoft provides a nice wizard to generate aggregates

on measure groups and partitions


25/47

MDX Multidimensional Expressions (MDX) is the query language that you use to work with and

retrieve multidimensional data in Microsoft SQL Server 2008 Analysis Services (SSAS).

MDX is superficially similar in many ways to the SQL syntax that is typically used withrelational databases. However, MDX is not an extension of the SQL language and isdifferent from SQL in many ways.

Basic MDX Select Query :


26/47

Calculations

Calculated members are customized measures or dimensionmembers that are defined based on a combination of cubedata, arithmetic operators, numbers, and functions. For

example, you can create a calculated member that calculates

the sum of two physical measures in the cube.


27/47


28/47


29/47


30/47

SSAS 2008 Day 2


31/47

KPIs 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 2008 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)


32/47

KPI Terms used in SSAS Value

The value is an MDX expression used to return the actual value of the KPI

Goal Thegoal 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 indicatoris a visual element which is used to present the status of

the KPI. Eg gauges, traffic lights or smileys. Trend

The trendis an MDX expression that evaluates the value of a KPI across time. Itcan be expressed using any time based criteria. Using this, the business userwill be able to determine how the KPI's value has progressed over time.

Trend Indicator

The trend indicatoris a visual element which is used to present the trend of

the KPI.


33/47


34/47


35/47

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.


36/47

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 toobtain further information, and directing the user to some Web-based application that allows anew 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.


37/47

Actions- Drillthrough


38/47

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.


39/47

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.


40/47

Partitions


41/47

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 metadataof the object, and for read/write access to the data.

Data can be secured at levels of granularity down to individual cells.


42/47

Role based Security


43/47

Security


44/47


45/47

Perspectives

Users engaged on a particular task generally do not haveto see the complete model. To avoid overwhelmingusers with the sheer size of the model, we need theability 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 presentingonly a specific subset of the model (measures,dimensions, attributes, and so on) that is relevant to aparticular group of users. Each perspective can then beassociated with the user security roles that define the

users who are permitted to see that perspective.


46/47

Translations

International users frequently have a need to viewmetadata in their local language. To address this, thecube allows translations of metadata to be provided

in any language. A client application that connectsusing a particular locale would receive all metadatain the appropriate language.

The model can also provide translations of data. Anattribute can map to different elements in the data

source, and provide the translations for thoseelements in different languages.


47/47

Documents

SQL Analysis Services 2008