Prof 1- Chapter 1

• The first step to writing an essay is choosing the right topic.
• Information can be considered as one of the most valuable assets for any business organization
• Data warehouse


  A way for organizations to manage their information in a centralized location (either cloud or local server) from which business intelligence can be leveraged to better inform decision making for end users
• Business Intelligence (BI)

  A set of processes, architectures, and technologies that convert raw data into meaningful information that drives profitable business actions. It is a suite of software and services to transform data into actionable intelligence and knowledge
• Data warehousing and business intelligence

  Can function as the information backbone of an organization, when optimized, helping them align every aspect of their business with a data-driven strategy
• Data flow in a data warehousing and BI system

  1. Data Source

  2. Data Warehouse

  3. Business Intelligence

  4. End User Access
• Goals of Data Warehousing and Business Intelligence

  • The DW/BI system must make information easily accessible
  • The DW/BI system must present information consistently
  • The DW/BI system must adapt to change
  • The DW/BI system must present information in a timely way
  • The DW/BI system must be a secure bastion that protects the information assets
  • The business community must accept the DW/BI system to deem it successful
• Responsibilities of DW/BI Managers

  • Understand the business users
  • Deliver high-quality, relevant, and accessible information and analytics to the business users
  • Sustain the DW/BI environment
• Dimensional modeling


  A longstanding technique for making databases simple. It addresses two simultaneous requirements: Deliver data that's understandable to the business users, and Deliver fast query performance
• Dimensional Model


  Designed to read, summarize, analyze numeric information like values, balances, counts, weights, etc. in a data warehouse
• Normalized 3NF structures


  Divide data into many discrete entities, each of which becomes a relational table, prioritizing efficient addition, updating, and deletion of data to minimize redundancy
• Star Schema


  Dimensional models implemented in relational database management systems, resembling a star-like structure
• OLAP Cube


  A data structure that allows fast analysis of data according to the multiple Dimensions that define a business problem
• Two Key Components of Star Schema

  • Fact Tables for Measurements
  • Dimension Tables for Descriptive Context
• Fact Table


  Stores the performance measurements resulting from an organization's business process events. Fact represents a business measure
• Dimension Tables


  Contain the textual context associated with a business process measurement event. They describe the "who, what, where, when, how, and why" associated with the event
• Kimball's DW/BI Architecture


  • Operational Source System
  • ETL System
  • Presentation Area
  • Business Intelligence Application
• ETL in the Back Room Kitchen

  Layout must highly efficient, For Consistent Quality Output, For Data Quality, Integrity, and Consistency, Skilled Professionals and Closed Environment
• Data Presentation and BI in the Front Dining Room

  Four Key Factors: Food (quality, taste, and presentation), Decor (appealing, comfortable surroundings for the patrons), Service (prompt food delivery, attentive support staff, and food received as ordered), Cost
• Independent Data Mart Architecture


  Data Mart - a subset of a data warehouse focused on a particular line of business, department, or subject area. This approach, analytic data is deployed on a departmental basis without concern for sharing and integrating information across the enterprise
• Hub-and-Spoke Corporate Information Factory Inmon Architecture

  The Hub: Enterprise Data Warehouse (EDW) - This is the core repository of the CIF, storing all raw, atomic data extracted from operational systems. The Spokes: Data Marts - These are smaller data stores built on top of the EDW, catering to the specific needs of individual departments
• Hybrid Spoke and Hub and Kimball Architecture

  The Hub and Spoke model can be applied to the management of data across different business units or departments. Each Spoke may implement Kimball Architecture internally to organize its specific data using dimensional models. The Central hub ensures coordination, governance and standardization of data practices across all spokes
• Dimensional Modeling Myths


  • Dimensional Models are Only for Summary Data
  • Dimensional Models are Departmental, Not Enterprise
  • Dimensional Models are Not Scalable
  • Dimensional Models are Only for Predictable Usage
  • Dimensional Models Can't Be Integrated
• Fact tables frequently have billions of rows; fact tables containing 2 trillion rows have been reported
• Standalone systems are software programs or solutions that function independently and is not integrated with any other solutions and/or devices