Datawarehousing Concepts | 7.0 9/7/2015 Datawarehousing Concepts

© 29/7/2015Datawarehousing Concepts | 7.0 Objectives The participants will be able to:  Discuss the basic concepts of Data warehousing  Explain the business need for decision support system  Define the Data warehouse features like KPI, fact, dimension  Describe the architecture of Data warehouse  Describe the terms OLTP and OLAP and explain the difference between them  Describe Entity Relationship Diagram with the help of an example  Describe classical star schema  Explain different variations of classical star schema

© 39/7/2015Datawarehousing Concepts | 7.0 Topics  Business need for decision support system  Datawarehouse definitions  Features of Datawarehouse  Entity Relationship diagram  Classical Star Schema  Different variations of Classical Star Schema

© 49/7/2015Datawarehousing Concepts | 7.0 A Decision support system needs to meet the following demands made by decision makers:  Immediate, single-point access to all relevant information regardless of source  Coverage of all business processes.  High quality of information not only in terms of Data content, but also in terms of the ability to evaluate Data flexibly.  High quality decision-making support: The Data warehouse must be developed and structured on the basis of requirements of operative and strategic management.  Short implementation time with less resources: As well being quick to implement, a Data warehouse must enable simple and quick access to relevant Data. Business need for decision support system

© 59/7/2015Datawarehousing Concepts | 7.0  Data warehousing is a tool dedicated to the delivery of information which advances decision making, improves business practices, and empowers business users.  Integrating Data from multiple sources, internal and external.  Providing subject-oriented views of the business through current and historical Data.  Providing a platform for consistent Data repository to analyze different sources of information. Datawarehouse Definitions

© 69/7/2015Datawarehousing Concepts | 7.0  Data Extraction & Loading  Gathering Data from operational systems (ERP / Legacy)  Cleansing Data  Aggregating the Data  Data Warehouse  Optimized for performance  Storing historical Data  Building the schema : Star Schema  The OLAP Cube  Multi-dimensional modeling  front-end access tools Datawarehouse Definitions

© 79/7/2015Datawarehousing Concepts | 7.0 Fact:  The information that business users want to know  The performance measures of the business  Facts are numbers, percentages  Sales volume, sales quantity etc. can be considered as facts Dimension:  How the Data needs to be viewed, like by Sales Organization, Distribution Channel etc. A Data Model based on:  Business Objectives  Business Strategy Facts and Dimension

© 89/7/2015Datawarehousing Concepts | 7.0 Key Performance Indicators (KPI) Internal Process Measures Innovation and Learning Measures Customer Measures Financial Measures  % Sales of New Products  Customers Acquired  Customer Satisfaction  Market Share  ROI and ROA  Revenue Growth  Product Time to Market  Unit Manufacturing Cost  Days Supply to inventory  New Product Introduction  Mgmt Skills  Employee Turnover

© 99/7/2015Datawarehousing Concepts | 7.0 Invoicing Systems Purchasing Systems General Ledger Ext. Data Sources Other Int. Systems Source Data Data Extraction Integration and Cleansing Processes Purchasing Marketing and Sales Corporate Information Product Line Location Summation Functional Area Translate Attribute Calculate Derive Synchronize Summarize Segmented Data Subsets Summarized Data Custom Developed Applications Query Access Tools Data Mining Statistical Programs Data Marts Extract Operational Data Store Transformation Applications Data Warehouse Generic Data warehouse Architecture

© 109/7/2015Datawarehousing Concepts | 7.0 Distinction between the Operative/inoperative environment

© 119/7/2015Datawarehousing Concepts | 7.0 OLTP Systems compared to OLAP Systems OLTP SystemsOLAP Systems TargetEfficiency through automation of business processes Generation of knowledge (competitive advantage) PrioritiesHigh availability, higher Data volume simple use, flexible Data access View of Datadetailedfrequently aggregated Database operationsadd, change, delete (refresh) and read read Typical Data structuresrelational (flat tables, high normalization) multi-dimensional structures Integration of Data from various modules/applications minimalcomprehensive

© 129/7/2015Datawarehousing Concepts | 7.0 OLTP Systems compared to OLAP Systems…contd(1) OLTP SystemsOLAP Systems DatasetDynamic, short lived ( days ) Static; historical ( 2+ years ) Application orientedSubject oriented PurposeDay-to-day operationsPlanning & knowledge based functions Highly structured repetitive processing Highly unstructured analytical processing User baseMostly operational community Mostly managerial community

© 139/7/2015Datawarehousing Concepts | 7.0 OLAP, MOLAP, ROLAP, HOLAP  OLAP : On Line Analytical Processing  MOLAP  MOLAP: Multidimensional OLAP  A multidimensional Database and an analytical engine e.g. EssBase from Arbor Software  ROLAP  ROLAP: Relational OLAP  Analytical engine that front-ends a relational DB: Data stored in relational DBMS and build multidimensional views of the Data  HOLAP: Hybrid OLAP  A combination of relational OLAP and multidimensional OLAP

© 149/7/2015Datawarehousing Concepts | 7.0 Entity Relationship Diagram

© 159/7/2015Datawarehousing Concepts | 7.0  Developing an ERD  Developing an ERD requires an understanding of the system and its components.  Consider a hospital: Patients are treated in a single ward by the doctors assigned to them. Usually each patient will be assigned a single doctor, but in rare cases they will have two.  Healthcare assistants also attend to the patients, a number of these are associated with each ward.  Initially the system will be concerned solely with drug treatment. Each patient is required to take a variety of drugs a certain number of times per day and for varying lengths of time.  The system must record details concerning patient treatment and staff payment. Some staff are paid part time and doctors and care assistants work varying amounts of overtime at varying rates (subject to grade).  The system will also need to track what treatments are required for which patients and when and it should be capable of calculating the cost of treatment per week for each patient (though it is currently unclear to what use this information will be put). Building an Entity Relationship Diagram

© 169/7/2015Datawarehousing Concepts | 7.0 Building an Entity Relationship Diagram…contd(1)

© 179/7/2015Datawarehousing Concepts | 7.0 Customer ID Customer name City Region Time ID Month Quarter Year Material Name Customer ID Material ID Time ID Sales Volume Sales Quantity Customer dimension Fact Time dimension Material dimension Classical Star Schema Material ID Material Group

© 189/7/2015Datawarehousing Concepts | 7.0 Dimension Tables Customer Dimension Table Material Dimension Table Time Dimension Table Customer idCustomer name CityRegion C100DavidLondonNorth C200PeterParisWest Material idMaterial name Material Group ….. M1111Hard DiscHardware….. M2222KeyboardSoftware…. Time idMonthQuarterYear Q1/ Q3/

© 199/7/2015Datawarehousing Concepts | 7.0 Fact Table Time idCustomer idMaterial idSales Volume Quantity C100M111150, C100M22223, C200M , C200M222210, C100M111125, C200M ….

© 209/7/2015Datawarehousing Concepts | 7.0 Classical Star Schema Customer Dimension TableMaterial Dimension Table Fact Table Time Dimension Table Customer idCustomer name C100David C200Peter Material idMaterial name….. M1111Hard Disc….. M2222Keyboard…. Time idMonth… … …. Time idCustomer idMaterial idSales VolumeQuantity C100M111150, C100M22223,00060 ….

© 219/7/2015Datawarehousing Concepts | 7.0 Multidimensional Analysis of Data

© 229/7/2015Datawarehousing Concepts | 7.0 Multidimensional Analysis of Data Contd..

© 239/7/2015Datawarehousing Concepts | 7.0 Material Name Customer dimension Fact Time dimension Material dimension Snowflake Schema Material ID Customer ID Material ID Time ID Sales Volume Sales Quantity Material Group Material ID Customer Name Customer ID City Customer ID Region Month Time ID Quarter Year

© 249/7/2015Datawarehousing Concepts | 7.0 Summary of Datawarehousing and Modeling  Datawarehouse reflects subject oriented view of Data suitable for analysis purpose.  Datawarehouse provides high quality information to support decision making in an organization.  KPIs are set of measures derived from strategies, goals and objectives.  Facts are numeric measures, dimensions are a perspective by which a fact is viewed.  Generic Datawarehouse architecture consists of source system, extraction, transformation and loading, storing Data and analysis.  OLTP is best suitable for transactional systems( for insert/update/delete), whereas OLAP is most suited for analytical purpose (executing adhoc queries)

© 259/7/2015Datawarehousing Concepts | 7.0 Summary of Datawarehousing and Modeling…contd(1)  Classical star schema consists of a single fact table surrounded by large demoralized dimension tables.  Dimension tables are linked relationally with the fact table by way of foreign key or primary key relationships.  Multi dimensional modeling represents a dimensional view of Data suitable for analysis  Snow flake schema is a type of star schema where dimension tables are normalized to eliminate redundancy but increases number of table joins.