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Dimensional Modelling


Dimensional modeling (DM) is the name of a set of techniques and concepts used in data warehouse design. It is considered to be different from entity-relationship modeling (ER). Dimensional Modeling does not necessarily involve a relational database. The same modeling approach, at the logical level, can be used for any physical form, such as multidimensional database or even flat files. According to data warehousing consultant Ralph Kimball, DM is a design technique for databases intended to support end-user queries in a data warehouse. It is oriented around understandability and performance. According to him, although transaction-oriented ER is very useful for the transaction capture, it should be avoided for end-user delivery.
  • Relational database experience on any RDBMS platform
  • Conceptual understanding of Business Intelligence, DSS, or Data Warehousing objectives
  • It is a 12 days program and extends up to 2hrs each.
  • The format is 40% theory, 60% Hands-on.

  • It is a 3 days program and extends up to 8hrs each.
  • The format is 40% theory, 60% Hands-on.
    Private Classroom arranged on request and minimum attendies for batch is 4.
course content
  • Dimensional Modeling Fundamentals
    • Publishing responsibilities of DW/BI professionals
    • Role of dimensional modeling in the Kimball, Corporate Information Factory (CIF), and hybrid architectures
    • Fact and dimension table characteristics
    • Surrogate key for dimensions
    • Fact table granularity
    • Degenerate dimensions
    • Benefits of dimensional modeling
    • 4-step design process
  • Retail Sales Case Study
    • Transaction fact tables
    • Denormalized dimension table hierarchies
    • Dealing with nulls
    • Dimension role-playing
    • Date and time-of-day dimension considerations
    • Centipede fact tables with too many dimensions
    • Star versus snowflake schemas
    • Factless fact tables
  • Order Management Design Workshop
    • Complications with operational header/line data
    • Allocated facts at different levels of detail
    • Abstract, generic dimensions
    • Freeform text comments
    • Junk dimensions for miscellaneous transaction indicators
    • Multiple currencies
  • Inventory Case Study
    • Implications of business processes on data architecture
    • Semi-additive facts
    • Three types of fact tables – transaction, periodic snapshot and accumulating snapshot
    • Conformed dimensions – identical and shrunken roll-ups
    • Enterprise Data Warehouse Bus Architecture and matrix for master data and integration
    • Drilling across fact tables
    • Consolidated cross-process fact tables
  • Billing Design Review Exercise
    • Common design flaws and mistakes to avoid
    • Checklist for conducting design reviews
  • Slowly Changing Dimensions
    • Basic Type 1, 2 and 3 techniques
    • Advanced techniques to deliver current and point-in-time attribute values
    • Mini-dimensions for large, rapidly changing dimensions
    • Multiple mini-dimensions and outriggers
  • Credit Card Design Workshop
    • Complementary transaction and periodic snapshot schemas
    • Design considerations for one dimension versus two dimensions
    • Bridge tables for many-valued dimension attributes
    • Fact table normalization
  • Insurance Case Study
    • Review of design patterns and techniques
    • Development of bus matrix from extended case study
    • Complex, unpredictable accumulating snapshots
    • Detailed implementation bus matrix
  • Dimensional Modeling Process
    • Process flow, tasks and deliverables
  • Financial Applications – Profit Equation
    • Allocating costs to the same grain as revenue
    • Profit margin point analysis and value banding
  • Financial Applications – General Ledger
    • Tracking instantaneous balances
    • Multiple time zones
    • Drilling down in the general ledger to a document
  • Financial Applications – Budgeting Value Chain
    • Budgets, commitments and expenditures
    • Bridge tables for variable-depth ragged hierarchies
    • Shared ownership and time-varying ragged hierarchies
    • Pathstring alternative for ragged hierarchies
    • Tracking the "age of the book"
    • Calculating the "policy loss triangle"
  • Retail Bank Account Tracking Workshop
    • Multiple account types with hundreds of potential attributes and facts
    • Many-to-many account to customer map and weighted versus "impact" reports
    • Tagging accounts as "about to go bankrupt"
    • Super-types and sub-types
  • Automobile Options Exercise
    • Column versus row trade-offs based on usability and scalability
  • Compliance-Enabled Data Warehouses
    • Eliminating Type 1 and Type 3 updates
  • ETL Back Room Dimensional Designs
    • Tracking data quality with error event fact table
    • Column, structure, and business rule tests for data quality
    • Reporting data quality with audit dimension
  • Customer Relationship Management Payoffs Discussion
    • Business users' expectations and bottom line impact?
    • Data sources needed? Common quality/integration problems?
  • Complex Customer Behavior Case Studies
    • Building study groups
    • Sequential time dependent study groups
    • Applying study groups to marketing panels and medical outcomes
  • Customer Dimension Modeling Challenges
    • Sparse but wide demographics attributes
    • Finding detailed customer profile at random times in the past
    • Tricky time span queries
    • Simultaneous facts and dimensions
    • Relationship between prospects and customers
  • Real Time Customer Tracking
    • Hot partitions
    • Handling unresolved customer identities in real time
  • Modeling Sequential Behavior
    • Step dimension for describing sequential behavior
    • RFID and web page challenges
    • Modeling product purchase sequences
  • Big Data Analytic Use Cases
    • Competing DBMS and Hadoop architectures
    • Attaching dimensions to big data
    • Drilling across conventional and big data sources
  • Final Customer-Centric Topics
    • "Text" facts for customer cluster identification
    • Structured questionnaires
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