SharePoint 2010 Business Intelligence Module 6: Analysis Services

Overview Analysis Services

Lesson: Analysis Services Introduction ETL OLAP Terms Storage Modes Queries Tools Mining Models

Introduction Analysis Services provides access to large data sets Running 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 information Perspective  A subset of dimensions and measures specific to some group of users Data 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/views IntelliCube  A heuristic analysis tool for automatic generation of a cube based on its data and relationships External Viewer for Outliers Candidate keys Value distributions Patterns

Business Intelligence Development Studio BI Studio is just Visual Studio with project templates installed Project 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 at Because it is Visual Studio it is fully customizable  3 rd party add-ons can make it even more powerful

Designing an Analysis Services Database BI Studio will be used to create new AS Databases Steps 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  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 data Not 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 performance Partition  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 databases MDX (multidimensional queries) is used for querying cubes DMX (Data Mining queries) is used for querying data mining models

MDX Queries Multidimensional Expressions (MDX) are used to query multidimensional data Some 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 data Always 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 data Analysis Services is an OLAP tool to manage large databases Ensure that your ETL process is accurate Data Mining Algorithms can help you find patterns you didn’t know about before