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Overview of Analysis Services in SharePoint 2010 Business Intelligence

This module introduces Analysis Services and its role in handling large data sets within SharePoint 2010's Business Intelligence environment. It covers key aspects such as Extract, Transform, Load (ETL) processes, Online Analytical Processing (OLAP) concepts, storage modes (MOLAP, ROLAP, HOLAP), and querying cubes using MDX. The course details the creation of data models, defining measures and dimensions, and building Key Performance Indicators (KPIs). It also discusses tools like Business Intelligence Development Studio (BIDS) and highlights best practices for data validation to ensure accuracy and reliability in analytics.

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Overview of Analysis Services in SharePoint 2010 Business Intelligence

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  1. SharePoint 2010 Business Intelligence Module 6: Analysis Services

  2. Overview • Analysis Services

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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. Lab 1: Analysis Services • Explore Analysis Services

  20. Lab 2: Building a Cube • Explore Cubes and Dimensions

  21. Lab 3: Data Mining Algorithms • Explore Data Mining Models

  22. Review • Your instructor will ask a series of questions on this module

  23. 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

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