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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

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sharepoint 2010 business intelligence

SharePoint 2010 Business Intelligence

Module 6: Analysis Services

overview
Overview
  • Analysis Services
lesson analysis services
Lesson: Analysis Services
  • Introduction
  • ETL
  • OLAP Terms
  • Storage Modes
  • Queries
  • Tools
  • Mining Models
introduction
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
slide5
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
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
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
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
    • 3rd party add-ons can make it even more powerful
designing an analysis services database
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
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
  • 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
  • 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
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
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
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 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
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
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
Lab 1: Analysis Services
  • Explore Analysis Services
lab 2 building a cube
Lab 2: Building a Cube
  • Explore Cubes and Dimensions
lab 3 data mining algorithms
Lab 3: Data Mining Algorithms
  • Explore Data Mining Models
review
Review
  • Your instructor will ask a series of questions on this module
summary
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