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

MARC

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020 |a 0128053356  |q (electronic bk.) 
020 |z 9780128051856 
035 |a (OCoLC)932289266  |z (OCoLC)956740331  |z (OCoLC)1105183491  |z (OCoLC)1105570580 
050 4 |a QA76.9.D37  |b H37 2016 
072 7 |a COM  |x 021030  |2 bisacsh 
082 0 4 |a 005.74  |2 23 
100 1 |a Haq, Qazi Muhammad Rashid Ul,  |e author. 
245 1 0 |a Data mapping for data warehouse design /  |c Qamar Shahbaz Ul Haq. 
264 1 |a Amsterdam :  |b Elsevier,  |c [2016] 
264 4 |c �2016 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
588 0 |a Online resource; title from PDF title page (EBSCO, viewed December 18, 2015). 
504 |a Includes bibliographical references. 
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. 
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 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. 
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 Their Description -- 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 -- Change Date -- 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. 
650 0 |a Data warehousing. 
650 0 |a Data mining. 
650 2 |a Data Mining  |0 (DNLM)D057225 
650 6 |a Entrep�ots de donn�ees (Informatique)  |0 (CaQQLa)201-0300302 
650 6 |a Exploration de donn�ees (Informatique)  |0 (CaQQLa)201-0300292 
650 7 |a COMPUTERS  |x Databases  |x Data Mining.  |2 bisacsh 
650 7 |a Data mining.  |2 fast  |0 (OCoLC)fst00887946 
650 7 |a Data warehousing.  |2 fast  |0 (OCoLC)fst00888026 
776 0 8 |i Erscheint auch als:  |a Ul Haq, Qamar Shahbaz.  |t Data mapping for data warehouse design 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128051856  |z Texto completo 

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