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13.Planning & Project management/D.S.Jagli2/23/2012 Slide 2 3.Planning & Project management: topics to be covered 1. How is it different? 2. Life-cycle approach 3. The Development Phases 4. Dimensional Analysis 5. Dimensional Modeling i. Star Schema ii. Snowflake Scheme 23.Planning & Project management/D.S.Jagli2/23/2012 Slide 3 3.Planning & Project management Reasons for DWH projects failure 1. Improper planning 2. Inadequate project management Planning for Data ware house is necessary. I. Key issues needs to be planned 1. Value and expectation 2. Risk assessment 3. Top-down or bottom up 4. Build or Buy 5. Single vender or best of breed II. Business requirement,not technology III. Top management support IV. Justification 33.Planning & Project management/D.S.Jagli2/23/2012 Slide 4 3.Planning & Project management Example for DWH Project Outline for overall plan Introduction Mission statement Scope Goals& objectives Key issues & Options Value& expectations Justification Executive sponsorship Implementation Strategy Tentative schedule Project authorization 43.Planning & Project management/D.S.Jagli2/23/2012 Slide 5 3.1 How is it different? DWH Project Different from OLTP System Project DWH Distinguish features and Challenges for Project Management Data Acquisition Data Storage Info. Delivery- 53.Planning & Project management/D.S.Jagli2/23/2012 Slide 6 63.Planning & Project management/D.S.Jagli2/23/2012 Slide 7 3.2 The life-cycle Approach Fig: DW functional components and SDLC 73.Planning & Project management/D.S.Jagli2/23/2012 Slide 8 DWH Project Plan: Sample outline 83.Planning & Project management/D.S.Jagli2/23/2012 Slide 9 3.3 DWH Development Phases 93.Planning & Project management/D.S.Jagli2/23/2012 Slide 10 3.3 DWH Development Phases 1) Project plan 2) Requirements definition 3) Design 4) Construction 5) Deployment 6) Growth and maintenance Interleaved within the design and construction phases are the three tracks along with the definition of the architecture and the establishment of the infrastructure 103.Planning & Project management/D.S.Jagli2/23/2012 Slide 11 3.4 Dimensional Analysis A data warehouse is an information delivery system. It is not about technology, but about solving users problems and providing strategic information to the user. In the phase of defining requirements, you need to concentrate on what information the users need, not on how you are going to provide the required information. 113.Planning & Project management/D.S.Jagli2/23/2012 Slide 12 Dimensional Analysis Usage of Information Unpredictable In providing information about the requirements for an operational system, the users are able to give you precise details of the required functions, information content, and usage patterns Dimensional Nature of Business Data Even though the users cannot fully describe what they want in a data warehouse, they can provide you with very important insights into how they think about the business. 123.Planning & Project management/D.S.Jagli2/23/2012 Slide 13 Managers think in business dimensions : example 133.Planning & Project management/D.S.Jagli2/23/2012 Slide 14 Dimensional Nature of Business Data 143.Planning & Project management/D.S.Jagli2/23/2012 Slide 15 Dimensional Nature of Business Data 153.Planning & Project management/D.S.Jagli2/23/2012 Slide 16 Examples of Business Dimensions 163.Planning & Project management/D.S.Jagli2/23/2012 Slide 17 Examples of Business Dimensions 173.Planning & Project management/D.S.Jagli2/23/2012 Slide 18 INFORMATION PACKAGESA NEW CONCEPT a novel idea is introduced for determining and recording information requirements for a data warehouse. This concept helps us to give a concrete form to the various insights, nebulous thoughts, and opinions expressed during the process of collecting requirements. The information packages, put together while collecting requirements, are very useful for taking the development of the data warehouse to the next phases. 183.Planning & Project management/D.S.Jagli2/23/2012 Slide 19 Requirements Not Fully Determinate Information packages enable us to: 1. Define the common subject areas 2. Design key business metrics 3. Decide how data must be presented 4. Determine how users will aggregate or roll up 5. Decide the data quantity for user analysis or query 6. Decide how data will be accessed 7. Establish data granularity 8. Estimate data warehouse size 9. Determine the frequency for data refreshing 10. Ascertain how information must be packaged 193.Planning & Project management/D.S.Jagli2/23/2012 Slide 20 An information package. 203.Planning & Project management/D.S.Jagli2/23/2012 Slide 21 Business Dimensions business dimensions form the underlying basis of the new methodology for requirements definition. Data must be stored to provide for the business dimensions. The business dimensions and their hierarchical levels form the basis for all further phases. 213.Planning & Project management/D.S.Jagli2/23/2012 Slide 22 Dimension Hierarchies/Categories Examples: 1) Product: Model name, model year, package styling, product line, product category, exterior color, interior color, first model year 2) Dealer: Dealer name, city, state, single brand flag, date first operation 3) Customer demographics: Age, gender, income range, marital status, household size, vehicles owned, home value, own or rent 4) Payment method: Finance type, term in months, interest rate, agent 5) Time: Date, month, quarter, year, day of week, day of month, season, holiday flag 223.Planning & Project management/D.S.Jagli2/23/2012 Slide 23 Key Business Metrics or Facts The numbers the users analyze are the measurements or metrics that measure the success of their departments. These are the facts that indicate to the users how their departments are doing in fulfilling their departmental objectives. 233.Planning & Project management/D.S.Jagli2/23/2012 Slide 24 Example: automobile sales The set of meaningful and useful metrics for analyzing automobile sales is as follows: Actual sale price MSRP sale price Options price Full price Dealer add-ons Dealer credits Dealer invoice Amount of down payment Manufacturer proceeds Amount financed 243.Planning & Project management/D.S.Jagli2/23/2012 Slide 25 Star Schema Snowflake Scheme 3.Planning & Project management/D.S.Jagli252/23/2012 Slide 26 FROM REQUIREMENTS TO DATA DESIGN The requirements definition completely drives the data design for the data warehouse. A group of data elements form a data structure. Logical data design includes determination of the various data elements that are needed and combination of the data elements into structures of data. Logical data design also includes establishing the relationships among the data structures. 3.Planning & Project management/D.S.Jagli262/23/2012 Slide 27 FROM REQUIREMENTS TO DATA DESIGN The information package diagrams form the basis for the logical data design for the data warehouse. The data design process results in a dimensional data model 3.Planning & Project management/D.S.Jagli272/23/2012 Slide 28 From requirements to data design. 3.Planning & Project management/D.S.Jagli282/23/2012 Slide 29 Dimensional Modeling Basics: Formation of the automaker sales fact table. 3.Planning & Project management/D.S.Jagli292/23/2012 Slide 30 Formation of the automaker dimension tables. 3.Planning & Project management/D.S.Jagli302/23/2012 Slide 31 31 Concept of Keys for Dimension table Surrogate Keys A surrogate key is the primary key for a dimension table and is independent of any keys provided by source data systems. Surrogate keys are created and maintained in the data warehouse and should not encode any information about the contents of records; Automatically increasing integers make good surrogate keys. The original key for each record is carried in the dimension table but is not used as the primary key. Surrogate keys provide the means to maintain data warehouse information when dimensions change. Business Keys Natural keys Will have a meaning and can be generated out of the data from source system or can be used as is from source system field Slide 32 The criteria for combining the tables into a dimensional model. 1. The model should provide the best data access. 2. The whole model must be query-centric. 3. It must be optimized for queries and analyses. 4. The model must show that the dimension tables interact with the fact table. 5. It should also be structured in such a way that every dimension can interact equally with the fact table. 6. The model should allow drilling down or rolling up along dimension hierarchies. 3.Planning & Project management/D.S.Jagli322/23/2012 Slide 33 With these requirements, we find that a dimensional model with the fact table in the middle and the dimension tables arranged around the fact table satisfies the condition 3.Planning & Project management/D.S.Jagli33 The dimensional model :a STAR schema 2/23/2012 Slide 34 Case study: STAR schema for automaker sales. 3.Planning & Project management/D.S.Jagli342/23/2012 Slide 35 E-R Modeling Versus Dimensional Modeling 3.Planning & Project management/D.S.Jagli35 1. OLTP systems capture details of events transactions 2. OLTP systems focus on individual events 3. An OLTP system is a window into micro-level transactions 4. Picture at detail level necessary to run the business 5. Suitable only for questions at transaction level 6. Data consistency, non- redundancy, and efficient data storage critical 1. DW meant to answer questions on overall process 2. DW focus is on how managers view the business 3. DW focus business trends 4. Information is centered around a business process 5. Answers show how the business measures the process 6. The measures to be studied in many ways along several business dimensions 2/23/2012 Slide 36 E-R Modeling Versus Dimensional Modeling E-R modeling for OLTP systems Dimensional modeling for the data warehouse. 3.Planning & Project management/D.S.Jagli362/23/2012 Slide 37 3.Planning & Project management/D.S.Jagli372/23/2012 Slide 38 Star Schemas Data Modeling Technique to map multidimensional decision support data into a relational database. Current Relational modeling techniques do not serve the needs of advanced data requirements 2/23/2012383.Planning & Project management/D.S.Jagli Slide 39 Star Schema 4 Components 1. Facts 2. Dimensions 3. Attributes 4. Attribute Hierarchies 2/23/2012393.Planning & Project management/D.S.Jagli Slide 40 Facts Numeric measurements (values) that represent a specific business aspect or activity. Stored in a fact table at the center of the star scheme. Contains facts that are linked through their dimensions. Updated periodically with data from operational databases 2/23/2012403.Planning & Project management/D.S.Jagli Slide 41 Dimensions Qualifying characteristics that provide additional perspectives to a given fact DSS data is almost always viewed in relation to other data Dimensions are normally stored in dimension tables 2/23/2012413.Planning & Project management/D.S.Jagli Slide 42 Attributes 1. Dimension Tables contain Attributes 2. Attributes are used to search, filter, or classify facts 3. Dimensions provide descriptive characteristics about the facts through their attributed 4. Must define common business attributes that will be used to narrow a search, group information, or describe dimensions. (ex.: Time / Location / Product) 5. No mathematical limit to the number of dimensions (3-D makes it easy to model) 2/23/2012423.Planning & Project management/D.S.Jagli Slide 43 Attribute Hierarchies Provides a Top-Down data organization Aggregation Drill-down / Roll-Up data analysis Attributes from different dimensions can be grouped to form a hierarchy 2/23/2012433.Planning & Project management/D.S.Jagli Slide 44 44 Concept of Keys for Star schema Surrogate Keys The surrogate keys are simply system-generated sequence numbers and is independent of any keys provided by source data systems. They do not have any built-in meanings. Surrogate keys are created and maintained in the data warehouse and should not encode any information about the contents of records; Automatically increasing integers make good surrogate keys. The original key for each record is carried in the dimension table but is not used as the primary key. Business Keys Primary Keys Each row in a dimension table is identified by a unique value of an attribute designated as the primary key of the dimension. Foreign Keys Each dimension table is in a one-to-many relationship with the central fact table. So the primary key of each dimension table must be a foreign key in the fact table. Slide 45 Star Schema for Sales Fact Table Dimension Tables 2/23/2012453.Planning & Project management/D.S.Jagli Slide 46 Star Schema Representation Fact and Dimensions are represented by physical tables in the data warehouse database. Fact tables are related to each dimension table in a Many to One relationship (Primary/Foreign Key Relationships). Fact Table is related to many dimension tables The primary key of the fact table is a composite primary key from the dimension tables. Each fact table is designed to answer a specific DSS question 2/23/2012463.Planning & Project management/D.S.Jagli Slide 47 Star Schema The fact table is always the larges table in the star schema. Each dimension record is related to thousand of fact records. Star Schema facilitated data retrieval functions. DBMS first searches the Dimension Tables before the larger fact table 2/23/2012473.Planning & Project management/D.S.Jagli Slide 48 Star Schema : advantages 1. Easy to understand 2. Optimizes Navigation 3. Most Suitable for Query Processing 3.Planning & Project management/D.S.Jagli482/23/2012 Slide 49 3.Planning & Project management/D.S.Jagli492/23/2012 Slide 50 Snowflaking is a method of normalizing the dimension tables in a STAR schema. 3.Planning & Project management/D.S.Jagli50 THE SNOWFLAKE SCHEMA 2/23/2012 Slide 51 Sales: a simple STAR schema. 3.Planning & Project management/D.S.Jagli512/23/2012 Slide 52 Product dimension: partially normalized 3.Planning & Project management/D.S.Jagli522/23/2012 Slide 53 When to Snowflake The principle behind snowflaking is normalization of the dimension tables by removing low cardinality attributes and forming separate tables. In a similar manner, some situations provide opportunities to separate out a set of attributes and form a subdimension. 2/23/20123.Planning & Project management/D.S.Jagli53 Slide 54 Advantages and Disadvantages Advantages Small savings in storage space Normalized structures are easier to update and maintain Disadvantages Schema less intuitive and end-users are put off by the complexity Ability to browse through the contents difficult Degraded query performance because of additional joins 3.Planning & Project management/D.S.Jagli542/23/2012 Slide 55 ??? Thank you 3.Planning & Project management/D.S.Jagli552/23/2012