SharePoint 2010 Business Intelligence

Module 6: Analysis Services

Overview

Analysis Services

Lesson: Analysis Services

IntroductionETLOLAP TermsStorage ModesQueriesToolsMining Models

Introduction

Analysis Services provides access to large data setsRunning SQL queries against a 100 million row table just doesn’t work

When your data sets get large, you need a better way of handling the load

Online analytical processing (OLAP) provides all your answers with speed!

Analysis services is an OLAP implementation

ETL

The Extract, Transform and Load (ETL) process is vital to the OLAP results

If you input junk, you get junk back out! All data that enters the OLAP database must be valid or

your results could be exponentially wrong!Make sure that all possible means are employed when ensuring only valid data is entered into the system and that it is entered only once!

Common issue is data inserted more than once which causes invalid results

OLAP Terms

Data Source A source or destination of data

Fact table A table that contains numerical information with keys that map to the dimension keys

Measure A single numerical value in a fact table

Dimension A set of labels/attributes that describe the measures in a fact table

Cube A set of aggregations of all the dimensions and facts complied together to produce

valuable informationPerspective

A subset of dimensions and measures specific to some group of usersData Modeling

Using statistical analysis to determine patterns in large sets of data

Tools

Visual Studio / BI Workbench Several project templates to facilitate the creation of

cubes, dimensions and work with data sources/viewsIntelliCube

A heuristic analysis tool for automatic generation of a cube based on its data and relationships

External Viewer forOutliersCandidate keysValue distributionsPatterns

Business Intelligence Development Studio

BI Studio is just Visual Studio with project templates installedProject templates provide item templates and wizards

New 2008 Wizards are much easier to use and more powerful

Context sensitive functionality keeps toolbars and menus trimmed to what elements you need based on what you are looking atBecause it is Visual Studio it is fully customizable

3rd party add-ons can make it even more powerful

Designing an Analysis Services Database

BI Studio will be used to create new AS DatabasesSteps include:

Define data source Define data view Create a new cube Define fact tables Define dimensions (data and time) Define measures Aggregate/Run the cube

Key Performance Indicators (KPI)

KPIs are used to show very simply whether a target is being accomplished

Components include the Goal, Value, Status, and Trend Example: Sales and Quality targets

Analysis Services allows you to build MDX expressions off of Cube data to build KPIs

KPI values can be queried from client applications Allows for visual display of meaningful data

Actions

Actions Allows client application users to be able to interact with

what the data means Example: Browse to a customer or product via URL

Types of Actions: CommandLine, DataSet, Drillthrough, Html, Custom,

Report, URL Define and assign to objects in the Cube

Perspectives

Perspectives work similar to views in a relational database

Shows different users the data they need to see for a particular role they may be in

Used to reduce complexity of cube dataNot meant to be security mechanism for data

Storage Modes

Data in an Analysis Services database is stored differently than a relational database

Optimized storage provides the OLAP query performancePartition

MOLAP – multidimensional OLAP (fact data and aggregations are stored in special format)

ROLAP – Relational OLAP (fact data and aggregations remain in relational database)

HOLAP – Hybrid OLAP (fact data is relational, aggregations are stored in special format)

Dimension (dimension attributes only) MOLAP – stored in special format ROLAP – stay in relational format

Querying Cubes

SQL is not used in OLAP databasesMDX (multidimensional queries) is used for querying cubesDMX (Data Mining queries) is used for querying data mining models

MDX Queries

Multidimensional Expressions (MDX) are used to query multidimensional dataSome common terms are:

Cell – the space at an intersection of a measure and attribute

Tuple – a unique cell based on a set of attribute members Set – an order set of tuples with same dimensionality

Calculated Members and Named Sets

Calculated Members are used when you need to determine something at query time

Can be query or session scoped Value are only stored in memory not on disk

Named Sets are basically predefined MDX queries that can be reused in other queries

Used to group dimension members

Analyzing Data with Data Mining Algorithms

Out of the box, Analysis services provides five algorithms: Classification (Decision Tree)

Predict one or more discrete variables, based on the other attributes in the dataset

Regression (Time Series)Predict one or more continuous variables, such as profit or loss, based on other attributes in the dataset

Segmentation (Clustering)Divide data into groups, or clusters, of items that have similar properties.

Association (Association)Find correlations between different attributes in a dataset

Sequence analysis (Sequence Clustering)Summarize frequent sequences or episodes in data, such as a Web path flow

What could go wrong?

Cubes really are simple things to build and utilize As simple as they are, it is easy to create a cube that has

the wrong dataAlways validate that the data that is displayed in the Cube is valid and accurate

Never hurts to be overly aggressive when testing Cube data

Lab 1: Analysis Services

Explore Analysis Services

Lab 2: Building a Cube

Explore Cubes and Dimensions

Lab 3: Data Mining Algorithms

Explore Data Mining Models

Review

Your instructor will ask a series of questions on this module

Summary

Extra Large databases are not easily queried for dataAnalysis Services is an OLAP tool to manage large databasesEnsure that your ETL process is accurateData Mining Algorithms can help you find patterns you didn’t know about before