Data mapping for data warehouse design /

Data mapping in data warehouse lifecycle is the process of creating a link between two distinct data models' (source and target) tables/attributes. It is required at many stages of DW life-cycle to transform data from one state to another; every stage has its own unique requirements and challen...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Haq, Qazi Muhammad Rashid Ul (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Elsevier, [2016]
Temas:
Data warehousing.
Data mining.
Data Mining
Entrep�ots de donn�ees (Informatique)
Exploration de donn�ees (Informatique)
COMPUTERS > Databases > Data Mining.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Data Mapping for Data Warehouse Design
  • Copyright Page
  • Dedication
  • Contents
  • 1 Introduction
  • Definition
  • 2 Data Mapping Stages
  • Mapping from the Source to the Data Warehouse Landing Area
  • Mapping from the Landing Area to the Staging Database
  • Mapping from the Staging Database to the Load Ready or Target Database
  • Mapping from Logical Data Model to the Semantic or Access Layer
  • 3 Data Mapping Types
  • Logical Data Mapping
  • Physical Data Mapping
  • 4 Data Models
  • Definition
  • Entity
  • Relationship
  • Attributes
  • Normalized Data Model.
  • First Normal Form
  • Second Normal Form
  • Third Normal Form
  • Dimensional Data Model
  • Fact
  • Dimension
  • Measure
  • Drill-Down and Roll-Up
  • Star Schema
  • Fact Tables
  • Dimension Tables
  • 5 Data Mapper's Strategy and Focus
  • Mapper Who? How Does He or She Do It?
  • 6 Uniqueness of Attributes and its Importance
  • Telecom
  • Manufacturing
  • Finance
  • Uniqueness in Data Warehouse
  • 7 Prerequisites of Data Mapping
  • Logical Data Model
  • Entities and Their Description
  • Attributes and Their Description
  • Primary Key of Entities
  • Relationship Between Entities.
  • Cardinality of the Relationship
  • Change Capture Column of History-Handled Entities
  • Physical Data Model
  • Source System Data Model
  • Source System Table and Attribute Details
  • Subject Matter Expert
  • Production Quality Data
  • 8 Surrogate Keys versus Natural Keys
  • Natural Keys
  • Surrogate Keys
  • 9 Data Mapping Document Format
  • Header-Level Rules
  • Column-Level Rules
  • Major Parts of the Data Mapping Document
  • Data Mapping Columns Explained
  • Change Date
  • Subject Area
  • Target Table Name
  • Target Column Name
  • Data Type
  • PK
  • Nullable
  • Source System
  • Record ID.
  • Source Table Name
  • Source Column Name
  • Data Type of Source Column
  • Transformation Category
  • Transformation Rule
  • Updated By
  • Mapping Priority or Sequence
  • 10 Data Analysis Techniques
  • Source Data Sample
  • Direct Access
  • Extraction from a Source
  • Data Files
  • What to Look For
  • High-Level Inter-Source System Relationship
  • Intra-Source System Table-Level Analysis
  • Column-Level Analysis
  • Uniqueness
  • Full Row Duplicates
  • Primary Key Duplicates
  • Multiple Extracts
  • Source System Updates
  • History Pattern Analysis
  • Type 0
  • Type 1
  • Type 2
  • Type 3
  • Type 4.
  • Type 6
  • Temporal Database
  • Transaction Time
  • Definition
  • Limitations
  • Valid Time
  • Definition
  • Limitations
  • History Data Verification
  • SQL Tools
  • Automatic Query Generators
  • Aggregate Functions
  • Window and Rank Functions
  • Microsoft Excel and Other Tools
  • Remove Duplicates
  • Sort
  • Pivot Tables
  • 11 Data Quality
  • What Is Data Quality?
  • How Do You Benefit from Data Quality?
  • Factors Determining Data Quality
  • Accurate Data
  • Complete Data
  • Legible Data
  • Relevant Data
  • Reliable Data
  • Timely Data
  • Valid Data.

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