Online resource; title from PDF title page (EBSCO, viewed December 18, 2015).

Includes bibliographical references.

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 challenges. This book provides basic and advanced knowledge about data mapping/data transformation. It contains real life scenarios that readers face and presents solutions/standard techniques across various domains. -- Edited summary from book.

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.