SQL Analysis Services 2005. Microsoft® SQL Server 2005 Analysis Services provides unified, fully integrated views of your business data to support online.
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SQL Analysis Services 2005
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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
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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
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Terminologies Cube 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 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 thats amenable to
summing and averaging.
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ARCHITECTURE
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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. A schema is a
collection of database objects, including tables, views, indexes,
and synonyms.schema
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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BIDS will automatically display the schema of the new data
source view
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To create the new cube, follow these steps: Right-click on the
Cubes folder in Solution Explorer and select New Cube.
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Deploying,Processing, Browsing a Cube
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Aggregations & Aggregation Wizard Pre calculated summaries
of data from leaf levels
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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.
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Aggregation Design Wizard. Microsoft provides a nice wizard to
generate aggregates on measure groups and partitions
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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 :
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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.
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SSAS 2005 Day 2
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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
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)
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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.
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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.
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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.
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Actions- Drillthrough
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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.
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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.
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Partitions
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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.
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Role based Security
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Security
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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.
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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.
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From a client computer that has a French locale, both the cube
and the query results would be displayed in French