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Data mapping for data warehouse design

Haq, Qazi Muhammad Rashid Ul,

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Data mapping for data warehouse design

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data mapping for data warehouse design/ Qamar Shahbaz Ul Haq.
作者:	Haq, Qazi Muhammad Rashid Ul,
出版者:	Amsterdam :Elsevier, : 2016.,
面頁冊數:	1 online resource.
內容註:	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 fromthe 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.
內容註:	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 TheirDescription -- Attributes and Their Description -- Primary Key of Entities -- Relationship Between Entities.
內容註:	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 -- ChangeDate -- Subject Area -- Target Table Name -- Target Column Name -- Data Type -- PK -- Nullable -- Source System -- Record ID.
內容註:	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.
內容註:	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.
標題:	Data warehousing. -
電子資源:	https://www.sciencedirect.com/science/book/9780128051856
ISBN:	9780128053355 (electronic bk.)

Data mapping for data warehouse design
Haq, Qazi Muhammad Rashid Ul,

Data mapping for data warehouse design[electronic resource] /Qamar Shahbaz Ul Haq. - Amsterdam :Elsevier,2016. - 1 online resource.

Includes bibliographical references.

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 fromthe 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.

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. --

ISBN: 9780128053355 (electronic bk.)Subjects--Topical Terms:

606996
Data warehousing.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QA76.9.D37 / H37 2016

Dewey Class. No.: 005.74

Data mapping for data warehouse design
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505 0 $a 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 fromthe 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.
505 8 $a 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 TheirDescription -- Attributes and Their Description -- Primary Key of Entities -- Relationship Between Entities.
505 8 $a 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 -- ChangeDate -- Subject Area -- Target Table Name -- Target Column Name -- Data Type -- PK -- Nullable -- Source System -- Record ID.
505 8 $a 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.
505 8 $a 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.
520 $a 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. -- $c Edited summary from book.
650 0 $a Data warehousing. $3 606996
650 0 $a Data mining. $3 562972
650 7 $a COMPUTERS $x Databases $x Data Mining. $2 bisacsh $3 3509008
655 0 $a Electronic books. $2 lcsh $3 542853
856 4 0 $u https://www.sciencedirect.com/science/book/9780128051856