1 1 the it perspective: data warehousing, management, and analytical structures rafal lukawiecki...

Download 1 1 The IT Perspective: Data Warehousing, Management, and Analytical Structures Rafal Lukawiecki Strategic Consultant, Project Botticelli Ltd rafal@projectbotticelli.com

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  • Slide 1
  • 1 1 The IT Perspective: Data Warehousing, Management, and Analytical Structures Rafal Lukawiecki Strategic Consultant, Project Botticelli Ltd rafal@projectbotticelli.com
  • Slide 2
  • 2 2 Objectives Explain the basics of: 1.Master Data Management 2.Data Warehousing 3.ETL 4.OLAP/Multidimensional Data The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Microsoft makes no warranties, express, implied or statutory, as to the information in this presentation. Portions 2010 Project Botticelli Ltd & entire material 2010 Microsoft Corp. Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Microsoft Copyright ownerships. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE. This seminar is based on a number of sources including a few dozen of Microsoft-owned presentations, used with permission. Thank you to Chris Dial, Tara Seppa, Aydin Gencler, Ivan Kosyakov, Bryan Bredehoeft, Marin Bezic, and Donald Farmer with his entire team for all the support.
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  • 3 3
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  • 4 4 SQL Services Why? Install only the ones you need Which? Integration Services Get your data from the world outside (ETL) Analysis Services Cubes, Data Mining, support for PowerPivot on SharePoint Reporting Services DIY Report Builder and traditional big reports Master Data Services Quality of critical master data (cities, colours, customers) Database Engine Data warehouse and OLTP relational storage
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  • 5 Master Data Management
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  • 6 6 MDM Ensures consistency of data across all organisational uses Impacts overall data quality Processes and tools for: Collection, aggregation, matching, distribution, and persistence of master data Consistently Related to Federated Data Management Key to MDM: Modelling
  • Slide 7
  • 7 7 Why MDM? Its About Evolution of Enterprise Architecture
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  • 8 8 MDM Processes Batched Acquisition from Staging Tables Members, Attributes, Parent-Child Relationships SQL Integration Services Import & Integration Versioning Changes Auditing Compliance Tracking of Instances Modeling Subscription Views Export to: Operational Systems Data Warehouses BI Analytics Reporting Tools Export & Subscription
  • Slide 9
  • 9 9 Microsoft Master Data Services SQL 2008 R2 Enterprise, Datacenter, Developer Tools: Master Data Manager Primary tool for managing your master data MDS Configuration Manager IT Pro tool MDS Web Service For developers wanting to extend MDS Concepts: Models Entities Attributes Members Hierarchies Collections Versions Database
  • Slide 10
  • 10 Modelling Master Data Model organises data at highest level Allowing versioning of changes to data There are typically four categories of models: People (Customers, Staff) Places (Geographies, Cities, Countries) Things (Products) Concepts (Accounts, Behaviours, Transactions)
  • Slide 11
  • 11 Example: Product MDM Model Product (model) Product (entity) Name (free- form attr) Code (free- form attr) Subcategory (domain- based attr) Name (free- form attr) Code (free- form attr) Category (domain- based attr) Name (free- form attr) Code (free- form attr) StandardCost (free-form attr) ListPrice (free-form attr) Photo (file attr)
  • Slide 12
  • 12 1. Reviewing a Data Model Using Master Data Services
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  • 13 Data Warehouse
  • Slide 14
  • 14 OLE DB ODBC DB2 Oracle XML SQL Server Analysis Services SQL Server Report Server Models SQL Server Data Mining Models SQL Server Integration Services MySAP Hyperion Essbase SAP NetWeaver BI SQL Server Teradata Rich Connectivity Data Providers
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  • 15 Star Schema
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  • 16 Star Schema Benefits Simple, not-so-normalized model High-performance queries Especially with Star Join Query Optimization Mature and widely supported Low-maintenance
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  • 17 Snowflake Dimension Tables Define hierarchies using multiple dimension tables Support fact tables with varying granularity Simplify consolidation of heterogeneous data Potential for slower query performance in relational reporting No difference in performance in Analysis Services database Potential for slower query performance in relational reporting No difference in performance in Analysis Services database
  • Slide 18
  • 18 Fact Table Fundamentals Collection of measurements associated with a specific business process Specific column types Foreign keys to dimensions Measures numeric and additive Metadata and lineage Consistent granularity the most atomic level by which the facts can be defined
  • Slide 19
  • 19 Fact Table Examples Day Grain Quarter Grain Reseller sales data by: Product Order Date Reseller Employee Sales Territory Sales quota data by: Employee Time
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  • 20 Date Dimension Table Most common dimension used in analysis (aka Time dimension) Use consistently with all facts Useful common attributes Year, Quarter, Month, Day Time series analysis support Navigation and summarization enabled with hierarchies, such as calendar or fiscal Single table design (typically not snowflake design) Tip: Format the key of the dimension as yyyymmdd (e.g. 20100115) to make it readily understandable
  • Slide 21
  • 21 Parent-Child Hierarchy A dimension that contains a parent attribute A parent attribute describes a self-referencing relationship, or a self-join, within a dimension table Common examples Organizational charts General Ledger structures Bill of Materials
  • Slide 22
  • 22 Parent-Child Hierarchy Example Brian Amy Stacia Stephen ShuMichael Peter Jos Syed
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  • 23 Slowly Changing Dimensions Maintain historical context as dimension data changes Three common ways (there are more): Type 1: Overwrite the existing dimension record Type 2: Insert a new versioned dimension record Type 3: Track limited history with attributes
  • Slide 24
  • 24 SCD Type 1 Existing record is updated History is not preserved
  • Slide 25
  • 25 SCD Type 2 Existing record is expired and new record inserted History is preserved Most common form of SCD
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  • 26 SCD Type 3 Existing record is updated Limited history is preserved Implementation is rare SalesTerritoryKey update to 10
  • Slide 27
  • 27 Integration and ETL
  • Slide 28
  • 28 Lets do ETL with SSIS SQL Server Integration Services (SSIS) service SSIS object model Two distinct runtime engines: Control flow Data flow 32-bit and 64-bit editions
  • Slide 29
  • 29 The Package The basic unit of work, deployment, and execution An organized collection of: Connection managers Control flow components Data flow components Variables Event handlers Configurations Can be designed graphically or built programmatically Saved in XML format to the file system or SQL Server
  • Slide 30
  • 30 Control Flow Control flow is a process-oriented workflow engine A package contains a single control flow Control flow elements Containers Tasks Precedence constraints Variables
  • Slide 31
  • 31 Data Flow The Data Flow Task Performs traditional ETL and more Fast and scalable Data Flow Components Extract data from Sources Load data into Destinations Modify data with Transformations Service Paths Connect data flow components Create the pipeline
  • Slide 32
  • 32 1. Using SQL Server Integration Services for Splitting Data
  • Slide 33
  • 33 OLAP/Multidimensional Data
  • Slide 34
  • 34 Cube = Unified Dimensional Model Multidimensional data Combination of measures and dimensions as one conceptual model Measures are sourced from fact tables Dimensions are sourced from dimension tables
  • Slide 35
  • 35 Dimensions Members from tables/views in a data source view (based on a Data Warehouse) Contain attributes matching dimension columns Organize attributes as hierarchies One All level and one leaf level User hierarchies are multi-level combinations of attributes Can be placed in display folders Used for slicing and dicing by attribute
  • Slide 36
  • 36 Hierarchies Benefits View of data at different levels of summarization Path to drill down or drill up Implementation Denormalized star schema dimension Normalized snowflake dimension Self-referencing relationship

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