ssas r2 and sharepoint 2010 – business intelligence
DESCRIPTION
TRANSCRIPT
SQL SERVER SQL SERVER SQL SERVER SQL SERVER
ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES
INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES
ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES
DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING
DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING
INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES
SQL SERVER SQL SERVER SQL SERVER SQL SERVER
MS SQL Server Analysis Services 2008 and Enterprise
Data Warehousing
About Me
Slava KokaevE m a i l : v k o k a e v @ b o s t o n b i . o r gP e r s o n a l w e b s i t e : w w w. b i p r o . o r gB l o g : w w w. b o s t o n b i . o r g / b l o g . a s p x
Agenda
Understanding Business Architecture
Understanding BI Architecture
Introduction to Dimensional Modeling
“You can’t manage what you can’t measure. You can’t measure what you can’t
describe”
Robert Kaplan and David Norton Authors of “The Balanced Scorecard”
Drive Corporate PerformanceGiving a purpose to business intelligence
Bike FactoryTires FactoryStill Factory
Color Factory
Warehouse Resellers
Accessory Factory
Plastic Factory AdventureWorksHeadquarter
Operational System
Management System
Understanding The Business System
Business Intelligence System
Microsoft BI Platform
EnterpriseData WarehouseSystem
BusinessAnalysisSystem
Understanding a BI System
Source SystemETL System
DW SystemDA System
Microsoft’s BI platform
END USER TOOLS & PERFORMANCE MANAGEMENT APPS
Excel Power Pivot
BI PLATFORM
SQL Server Reporting Services
SQL Server Analysis Services
SQL Server DBMS
SQL Server Integration Services
SharePoint Server
Reports Dashboards Excel Workbooks
AnalyticViews Scorecards Plans
Microsoft’s BI Strategy and VisionTo improve organizations by providing business insights to all employees, leading to better, faster, more relevant decisions• Microsoft has a long-term commitment
to delivering a complete and integrated BI offering
• SQL Server has led innovation in the BI space for more than a decade
• There is widespread delivery of intelligence through Microsoft Office
• The platforms are enterprise-grade and affordable
Enterprise Business Analysis System
Customers Business partners
Vendors, Suppliers, Channel partners
IT providers Financial service providers
Monitoring Systems Analysis Systems
Business Processes and Operations
Controlling Systems Strategy and Planning Systems
Abstract Functional Business Model
Plan
Act
Check
Do
Data Mining Reporting Services ANALYSIS SERVICESSQL Server
Input Data
Resources
Plans, Business Rule and KPI
Result Data
Process Output (Facts /Measures)
Feedback (Improvement)
IDEF0 Modeling Notation
SQL Server 2008 BI Platform Components
Data acquisition from source systems and integration
Data transformation and synthesis
Data enrichment, with business logic, hierarchical views
Data discovery via data mining
Data presentation and distribution
Data access for the masses
Integrate Analyze Report
MS BI Platform
Data Mining
Analysis Services
Integration Services
Reporting
Services
Data Mining
Analysis Services
Reporting Services
Understanding BI Architecture
Source System ETL System Clients ToolsData Warehouse
Enterprise Data Source Structure
Data Warehouse
CRM
ERPHR
Call Center
Web Apps
FinanceInventory
ETL SystemExtract data from the source systems
Transform the data to convert it to a desired state
Load the data into the data warehouse
E T L
ETL Workflow
OLTP SSIS DW OLAP
Analysis SystemMultidimensional databases are also called online analytical processing (OLAP) databases and…Contain structures optimized for rapid ad hoc information retrievalPre-calculate and store aggregated valuesInclude calculation engines for fast, flexible transformation of base dataDesigned to reveal business trends and statistics not directly visible in the data retrieved from a data warehouseData mining models discover patterns in data, typically for prediction analysis
ProductAssociation
Sales FinanceProduction
Data Visualization SystemClient access and distribution mechanisms can include:
Static report viewers and browsers Ad hoc query tools Report writers Modeling applications Scorecard applications Portals and dashboards
Delivering data is a process of continuous business improvement:
Monitor Analyze Plan
What is a dimensional model ?A dimensional model is made up a central fact table (or tables) and its associated dimensions. The dimensional model is also called a star schema because it looks like a star with the fact table in the middle and the dimensions serving as the points on the star. From a relational data modeling perspective, the dimensional model consists of a normalized fact table with denormalized dimension tables.
Fact and Dimensions together or “Star Schema” Database
DimensionsDimensions are the foundation of the dimensional model, describing the objects of the business, such as employee, product, customer, service.They describe the surrounding measurement events. The business processes (facts) or actions of the business in which the dimensions participate. Each dimension table links to all the business processes in which it participates. A single dimension that is shared across all these processes is called a conformed dimension.
Fact TablesEach fact table contains the measurements associated with a specific business process. A record in a fact table is a measurement, and a measurement event can always produce a fact table record. These events usually have numeric measurements that quantify the magnitude of the event, such as quantity ordered, sale amount, or call duration. These numbers are called facts (or measures in Analysis Services).The key to the fact table is a multi-part key made up of a subset of the foreign keys from each dimension table involved in the business event.
Sales Business Process
Plan Sales
Analyze Sales
Monitor Sales
Resellers Sales
SQL Server DB
SALES MANAGERSales Representative
Stock Data
Reseller (Dimension)
Sales Quota
Sales Result
Sale Orders (Facts /Measures)
Sales corrections and Improvement
Sales Summary
Balance Scorecards
Sales Transaction
Product Hierarchies
Designing DimensionsDatabase Table
DataSourceView
DatabaseDimension
DimensionHierarchy
CubeDimension
HierarchiesA hierarchy is a collection of logically structured levels based on attributes. In some hierarchies, each member attribute uniquely implies the member attribute above it.
Surrogate KeysPrimary key purpose
Identifies uniqueness Relates to foreign keys in a fact table
Two candidates Business key
Represents source primary key
Surrogate keyConsolidates multiple data sources
Consolidates multi-value business keys
Allows tracking of dimension history
Limits fact table width for optimization
Using a surrogate key is considered best practice
Surrogate Keys
MS-1981163MS-1981
Source OLTP Table Target DW Table
Surrogate Key
Business Key
SnowflakingSnowflaking is the practice of connecting lookup tables to fields in the dimension tables. Sometimes it's easier to maintain a dimension in the ETL process when it's been partially normalized or snowflaked.
Reviewing Star Schema Benefits • Transforms normalized data into a simpler
model• Delivers high-performance queries• Delivers higher performing queries using
Star Join Query Optimization• Uses mature modeling techniques that are
widely supported by many BI tools• Requires low maintenance as the data
warehouse design evolves
VT
COM
A
TXSt
ate
Understanding Cube Structure
12451576
29543007
1383
16542145
2012
645
8451082
905
275
345875
745 745
905
2012
3007
761
745
1365
1575
234
2455
988
700
645
2322
18741479
15531576 2954 3007
1455 1874445 1575
995 1945 945 1479
1164 1893 13761245
Quarter 1Quarter 2
Quarter 3 Quarter 4
Semester 1
Semester 2
Calendar Year - 2009
Australia
United States
Canada
France
Coun
try
Accessories
Bikes
Clothing
Components
Prod
uct
Line
Reviewing Star Schema Benefits
Normalized(OLTP)
Denormalized (Star Schema)
MDX vs. T-SQLcalculate YTD monthly average and compare it over several years for the same selected month
WITH MEMBER Measures.MyYTD AS SUM(YTD([Date].[Calendar]),[Measures].[Internet Sales Amount])
MEMBER Measures.MyMonthCount AS SUM(YTD([Date].[Calendar]),(COUNT([Date].[Month of Year])))
MEMBER Measures.MyYTDAVG AS Measures.MyYTD / Measures.MyMonthCount
SELECT {Measures.MyYTD, Measures.MyMonthCount,[Measures].[Internet Sales Amount],Measures.MyYTDAVG} On 0, [Date].[Calendar].[Month] On 1FROM [Adventure Works]WHERE ([Date].[Month of Year].&[7])
Slowly Changing DimensionsSupport primary role of data warehouse to describe the past accuratelyMaintain historical context as new or changed data is loaded into dimension tables
Slowly Changing Dimension (SCD) types Type 1: Overwrite the existing dimension record Type 2: Insert a new ‘versioned’ dimension record Type 3: Track limited history with attributes
The concept of Slowly Changing Dimensions was introduced by Ralph Kimball
Slowly Changing Dimensions Type 1Existing record is updated
History is not preserved
LastName update to Valdez-Smythe
Slowly Changing Dimensions Type 2Existing record is ‘expired’ and new record insertedHistory is preservedMost common form of Slowly Changing Dimension
SalesTerritoryKey update to 10
Slowly Changing Dimensions Type 2Existing record is updated
Limited history is preserved
Implementation is rare
SalesTerritoryKey update to 10
ResourcesSQL Server 2008 Books Online,msdn2.microsoft.com/en-us/library/bb543165(sql.100).aspx
The Microsoft Data Warehouse Toolkit by Joy Mundy, Warren Thornthwaite, and Ralph Kimball
The Data Warehouse Lifecycle Toolkit by Ralph Kimball, et al.