Data Warehouse Systems: Design and Implementation (Data-Centric Systems and Applications)

Foreword to the Second Edition
Foreword to the First Edition
Preface
	Objective of the Book
	Organization of the Book and Teaching Paths
Acknowledgments
About the Authors
Contents
Part I Fundamental Concepts
	Chapter 1 Introduction
		1.1 An Overview of Data Warehousing
		1.2 Emerging Data Warehousing Technologies
		1.3 Review Questions
	Chapter 2 Database Concepts
		2.1 Database Design
		2.2 The Northwind Case Study
		2.3 Conceptual Database Design
		2.4 Logical Database Design
			2.4.1 The Relational Model
			2.4.2 Normalization
			2.4.3 Relational Query Languages
		2.5 Physical Database Design
		2.6 Summary
		2.7 Bibliographic Notes
		2.8 Review Questions
		2.9 Exercises
	Chapter 3 Data Warehouse Concepts
		3.1 Multidimensional Model
			3.1.1 Hierarchies
			3.1.2 Measures
		3.2 OLAP Operations
		3.3 Data Warehouses
		3.4 Data Warehouse Architecture
			3.4.1 Back-End Tier
			3.4.2 Data Warehouse Tier
			3.4.3 OLAP Tier
			3.4.4 Front-End Tier
			3.4.5 Variations of the Architecture
		3.5 Overview of Microsoft SQL Server BI Tools
		3.6 Summary
		3.7 Bibliographic Notes
		3.8 Review Questions
		3.9 Exercises
	Chapter 4 Conceptual Data Warehouse Design
		4.1 Conceptual Modeling of Data Warehouses
		4.2 Hierarchies
			4.2.1 Balanced Hierarchies
			4.2.2 Unbalanced Hierarchies
			4.2.3 Generalized Hierarchies
			4.2.4 Alternative Hierarchies
			4.2.5 Parallel Hierarchies
			4.2.6 Nonstrict Hierarchies
		4.3 Advanced Modeling Aspects
			4.3.1 Facts with Multiple Granularities
			4.3.2 Many-to-Many Dimensions
			4.3.3 Links between Facts
		4.4 Querying the Northwind Cube Using the OLAP Operations
		4.5 Summary
		4.6 Bibliographic Notes
		4.7 Review Questions
		4.8 Exercises
	Chapter 5 Logical Data Warehouse Design
		5.1 Logical Modeling of Data Warehouses
		5.2 Relational Data Warehouse Design
		5.3 Relational Representation of Data Warehouses
		5.4 Time Dimension
		5.5 Logical Representation of Hierarchies
			5.5.1 Balanced Hierarchies
			5.5.2 Unbalanced Hierarchies
			5.5.3 Generalized Hierarchies
			5.5.4 Alternative Hierarchies
			5.5.5 Parallel Hierarchies
			5.5.6 Nonstrict Hierarchies
		5.6 Advanced Modeling Aspects
			5.6.1 Facts with Multiple Granularities
			5.6.2 Many-to-Many Dimensions
			5.6.3 Links between Facts
		5.7 Slowly Changing Dimensions
		5.8 Performing OLAP Queries with SQL
		5.9 Defining the Northwind Data Warehouse in Analysis Services
			5.9.1 Multidimensional Model
			5.9.2 Tabular Model
		5.10 Summary
		5.11 Bibliographic Notes
		5.12 Review Questions
		5.13 Exercises
	Chapter 6 Data Analysis in Data Warehouses
		6.1 Introduction to MDX
			6.1.1 Tuples and Sets
			6.1.2 Basic Queries
			6.1.3 Slicing
			6.1.4 Navigation
			6.1.5 Cross Join
			6.1.6 Subqueries
			6.1.7 Calculated Members and Named Sets
			6.1.8 Relative Navigation
			6.1.9 Time-Related Calculations
			6.1.10 Filtering
			6.1.11 Sorting
			6.1.12 Top and Bottom Analysis
			6.1.13 Aggregation Functions
		6.2 Introduction to DAX
			6.2.1 Expressions
			6.2.2 Evaluation Context
			6.2.3 Queries
			6.2.4 Filtering
			6.2.5 Hierarchy Handling
			6.2.6 Time-Related Calculations
			6.2.7 Top and Bottom Analysis
			6.2.8 Table Operations
		6.3 Key Performance Indicators
			6.3.1 Classification of Key Performance Indicators
			6.3.2 Defining Key Performance Indicators
		6.4 Dashboards
			6.4.1 Types of Dashboards
			6.4.2 Guidelines for Dashboard Design
		6.5 Summary
		6.6 Bibliographic Notes
		6.7 Review Questions
	Chapter 7 Data Analysis in the Northwind Data Warehouse
		7.1 Querying the Multidimensional Model in MDX
		7.2 Querying the Tabular Model in DAX
		7.3 Querying the Relational Data Warehouse in SQL
		7.4 Comparison of MDX, DAX, and SQL
		7.5 KPIs for the Northwind Case Study
			7.5.1 KPIs in Analysis Services Multidimensional
			7.5.2 KPIs in Analysis Services Tabular
		7.6 Dashboards for the Northwind Case Study
			7.6.1 Dashboards in Reporting Services
			7.6.2 Dashboards in Power BI
		7.7 Summary
		7.8 Review Questions
		7.9 Exercises
Part II Implementation and Deployment
	Chapter 8 Physical Data Warehouse Design
		8.1 Physical Modeling of Data Warehouses
		8.2 Materialized Views
			8.2.1 Algorithms Using Full Information
			8.2.2 Algorithms Using Partial Information
		8.3 Data Cube Maintenance
		8.4 Computation of a Data Cube
			8.4.1 PipeSort Algorithm
			8.4.2 Cube Size Estimation
			8.4.3 Partial Computation of a Data Cube
		8.5 Indexes for Data Warehouses
			8.5.1 Bitmap Indexes
			8.5.2 Bitmap Compression
			8.5.3 Join Indexes
		8.6 Evaluation of Star Queries
		8.7 Partitioning
		8.8 Parallel Processing
		8.9 Physical Design in SQL Server and Analysis Services
			8.9.1 Indexed Views
			8.9.2 Partition-Aligned Indexed Views
			8.9.3 Column-Store Indexes
			8.9.4 Partitions in Analysis Services
		8.10 Query Performance in Analysis Services
		8.11 Summary
		8.12 Bibliographic Notes
		8.13 Review Questions
		8.14 Exercises
	Chapter 9 Extraction, Transformation, and Loading
		9.1 Business Process Modeling Notation
		9.2 Conceptual ETL Design Using BPMN
		9.3 Conceptual Design of the Northwind ETL Process
		9.4 SQL Server Integration Services
		9.5 The Northwind ETL Process in Integration Services
		9.6 Implementing ETL Processes in SQL
		9.7 Summary
		9.8 Bibliographic Notes
		9.9 Review Questions
		9.10 Exercises
	Chapter 10 A Method for Data Warehouse Design
		10.1 Approaches to Data Warehouse Design
		10.2 General Overview of the Method
		10.3 Requirements Specification
			10.3.1 Business-Driven Requirements Specification
			10.3.2 Data-driven Requirements Specification
			10.3.3 Business/Data-driven Requirements Specification
		10.4 Conceptual Design
			10.4.1 Business-Driven Conceptual Design
			10.4.2 Data-driven Conceptual Design
			10.4.3 Business/Data-driven Conceptual Design
		10.5 Logical Design
			10.5.1 Logical Schemas
			10.5.2 ETL Processes
		10.6 Physical Design
		10.7 Characterization of the Various Approaches
			10.7.1 Business-Driven Approach
			10.7.2 Data-driven Approach
			10.7.3 Business/Data-driven Approach
		10.8 Summary
		10.9 Bibliographic Notes
		10.10 Review Questions
		10.11 Exercises
Part III Advanced Topics
	Chapter 11 Temporal and Multiversion Data Warehouses
		11.1 Manipulating Temporal Information in SQL
		11.2 Conceptual Design of Temporal Data Warehouses
			11.2.1 Time Data Types
			11.2.2 Synchronization Relationships
			11.2.3 A Conceptual Model for Temporal Data Warehouses
			11.2.4 Temporal Hierarchies
			11.2.5 Temporal Facts
		11.3 Logical Design of Temporal Data Warehouses
		11.4 Implementation Considerations
			11.4.1 Period Encoding
			11.4.2 Tables for Temporal Roll-Up
			11.4.3 Integrity Constraints
			11.4.4 Measure Aggregation
			11.4.5 Temporal Measures
		11.5 Querying the Temporal Northwind Data Warehouse in SQL
		11.6 Temporal Data Warehouses versus Slowly Changing Dimensions
		11.7 Conceptual Design of Multiversion Data Warehouses
		11.8 Logical Design of Multiversion Data Warehouses
		11.9 Querying the Multiversion Northwind Data Warehouse in SQL
		11.10 Summary
		11.11 Bibliographic Notes
		11.12 Review Questions
		11.13 Exercises
	Chapter 12 Spatial and Mobility Data Warehouses
		12.1 Conceptual Design of Spatial Data Warehouses
			12.1.1 Spatial Data Types
			12.1.2 Topological relationships
			12.1.3 Continuous Fields
			12.1.4 A Conceptual Model of Spatial Data Warehouses
		12.2 Implementation Considerations for Spatial Data
			12.2.1 Spatial Reference Systems
			12.2.2 Vector Model
			12.2.3 Raster Model
		12.3 Logical Design of Spatial Data Warehouses
		12.4 Topological Constraints
		12.5 Querying the GeoNorthwind Data Warehouse in SQL
		12.6 Mobility Data Analysis
		12.7 Temporal Types
		12.8 Temporal Types in MobilityDB
		12.9 Mobility Data Warehouses
		12.10 Querying the Northwind Mobility Data Warehouse in SQL
		12.11 Summary
		12.12 Bibliographic Notes
		12.13 Review Questions
		12.14 Exercises
	Chapter 13 Graph Data Warehouses
		13.1 Graph Data Models
		13.2 Property Graph Database Systems
			13.2.1 Neo4j
			13.2.2 Introduction to Cypher
			13.2.3 Querying the Northwind Cube with Cypher
		13.3 OLAP on Hypergraphs
			13.3.1 Operations on Hypergraphs
			13.3.2 OLAP on Trajectory Graphs
		13.4 Graph Processing Frameworks
			13.4.1 Gremlin
			13.4.2 JanusGraph
		13.5 Bibliographic Notes
		13.6 Review Questions
		13.7 Exercises
	Chapter 14 Semantic Web Data Warehouses
		14.1 Semantic Web
			14.1.1 Introduction to RDF and RDFS
			14.1.2 RDF Serializations
			14.1.3 RDF Representation of Relational Data
		14.2 Introduction to SPARQL
			14.2.1 SPARQL Basics
			14.2.2 SPARQL Semantics
		14.3 RDF Representation of Multidimensional Data
		14.4 Representation of the Northwind Cube in QB4OLAP
		14.5 Querying the Northwind Cube in SPARQL
		14.6 Summary
		14.7 Bibliographic Notes
		14.8 Review Questions
		14.9 Exercises
	Chapter 15 Recent Developments in Big Data Warehouses
		15.1 Data Warehousing in the Age of Big Data
		15.2 Distributed Processing Frameworks
			15.2.1 Hadoop
			15.2.2 Hive
			15.2.3 Spark
			15.2.4 Comparison of Hadoop and Spark
			15.2.5 Kylin
		15.3 Distributed Database Systems
			15.3.1 MySQL Cluster
			15.3.2 Citus
		15.4 In-Memory Database Systems
			15.4.1 Oracle TimesTen
			15.4.2 Redis
		15.5 Column-Store Database Systems
			15.5.1 Vertica
			15.5.2 MonetDB
			15.5.3 Citus Columnar
		15.6 NoSQL Database Systems
			15.6.1 HBase
			15.6.2 Cassandra
		15.7 NewSQL Database Systems
			15.7.1 Cloud Spanner
			15.7.2 SAP HANA
			15.7.3 VoltDB
		15.8 Array Database Systems
			15.8.1 Rasdaman
			15.8.2 SciDB
		15.9 Hybrid Transactional and Analytical Processing
			15.9.1 SingleStore
			15.9.2 LeanXcale
		15.10 Polystores
			15.10.1 CloudMdsQL
			15.10.2 BigDAWG
		15.11 Cloud Data Warehouses
		15.12 Data Lakes and Data Lakehouses
		15.13 Future Perspectives
		15.14 Summary
		15.15 Bibliographic Notes
		15.16 Review Questions
Appendix A Graphical Notation
	A.1 Entity-Relationship Model
	A.2 Relational Model
	A.3 MultiDim Model for Data Warehouses
	A.4 MultiDim Model for Spatial Data Warehouses
	A.5 MultiDim Model for Temporal Data Warehouses
	A.6 BPMN Notation for ETL
References
Glossary
Index