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Biblioteca

MARC Lista (tabellare)
Azure Data Factory by example : practical implementation for data engineers / / Richard Swinbank
Azure Data Factory by example : practical implementation for data engineers / / Richard Swinbank
Autore Swinbank Richard
Edizione [1st ed. 2021.]
Pubbl/distr/stampa [Place of publication not identified] : , : Apress, , [2021]
Descrizione fisica 1 online resource (XXII, 335 p. 148 illus.)
Disciplina 658.40380285574
Soggetto topico Data warehousing
ISBN 1-4842-7029-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Creating an Azure Data Factory Instance -- 2. Your First Pipeline -- 3. The Copy Data Activity -- 4. Expressions -- 5. Parameters -- 6. Controlling Flow -- 7. Data Flows -- 8. Integration Runtimes -- 9. Power Query in ADF -- 10. Publishing to ADF -- 11. Triggers -- 12. Monitoring.
Record Nr. UNINA-9910484859203321
Swinbank Richard  
[Place of publication not identified] : , : Apress, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Beginning Azure synapse analytics : transition from data warehouse to data lakehouse / / Bhadresh Shiyal
Beginning Azure synapse analytics : transition from data warehouse to data lakehouse / / Bhadresh Shiyal
Autore Shiyal Bhadresh
Pubbl/distr/stampa [Place of publication not identified] : , : Apress, , [2021]
Descrizione fisica 1 online resource (263 pages)
Disciplina 658.40380285574
Soggetto topico Data warehousing - Management
Microsoft Azure (Computing platform)
ISBN 1-4842-7061-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Core Data and Analytics Concepts -- Core Data Concepts -- What Is Data? -- Structured Data -- Semi-structured Data -- Unstructured Data -- Data Processing Methods -- Batch Data Processing -- Streaming or Real-Time Data Processing -- Relational Data and Its Characteristics -- Non-Relational Data and Its Characteristics -- Core Data Analytics Concepts -- What Is Data Analytics? -- Data Ingestion -- Data Exploration -- Data Processing -- ETL -- ELT -- ELT / ETL Tools -- Data Visualization -- Data Analytics Categories -- Descriptive Analytics -- Diagnostic Analytics -- Predictive Analytics -- Prescriptive Analytics -- Cognitive Analytics -- Summary -- Chapter 2: Modern Data Warehouses and Data Lakehouses -- What Is a Data Warehouse? -- Core Data Warehouse Concepts -- Data Model -- Model Types -- Schema Types -- Metadata -- Why Do We Need a Data Warehouse? -- Efficient Decision-Making -- Separation of Concerns -- Single Version of the Truth -- Data Restructuring -- Self-Service BI -- Historical Data -- Security -- Data Quality -- Data Mining -- More Revenues -- What Is a Modern Data Warehouse? -- Difference Between Traditional & -- Modern Data Warehouses -- Cloud vs. On-Premises -- Separation of Compute and Storage Resources -- Cost -- Scalability -- ETL vs. ELT -- Disaster Recovery -- Overall Architecture -- Data Lakehouse -- What Is a Data Lake? -- What Is Delta Lake? -- What Is Apache Spark? -- What Is a Data Lakehouse? -- Characteristics of a Data Lakehouse -- Various Data Types -- AI -- Decoupled Compute and Storage Resources -- Open Source Storage Format -- Data Analytics and BI Tools -- ACID Properties -- Differences Between a Data Warehouse and a Data Lakehouse -- Architecture -- Access to Raw Data.
Open Source vs. Proprietary -- Workloads -- Query Engines -- Data Processing -- Real-Time Data -- Examples of Data Lakehouses -- Azure Synapse Analytics -- Databricks -- Benefits of Data Lakehouse -- Support for All Types of Data -- Time to Market -- More Cost Effective -- AI -- Reduction in ETL/ELT Jobs -- Usage of Open Source Tools and Technologies -- Efficient and Easy Data Governance -- Drawbacks of Data Lakehouse -- Monolithic Architecture -- Technical Infancy -- Migration Cost -- Lack of Many Products/Options -- Scarcity of Skilled Technical Resources -- Summary -- Chapter 3: Introduction to Azure Synapse Analytics -- What Is Azure Synapse Analytics? -- Azure Synapse Analytics vs. Azure SQL Data Warehouse -- Why Should You Learn Azure Synapse Analytics? -- Main Features of Azure Synapse Analytics -- Unified Data Analytics Experience -- Powerful Data Insights -- Unlimited Scale -- Security, Privacy, and Compliance -- HTAP -- Key Service Capabilities of Azure Synapse Analytics -- Data Lake Exploration -- Multiple Language Support -- Deeply Integrated Apache Spark -- Serverless Synapse SQL Pool -- Hybrid Data Integration -- Power BI Integration -- AI Integration -- Enterprise Data Warehousing -- Seamless Streaming Analytics -- Workload Management -- Advanced Security -- Summary -- Chapter 4: Architecture and Its Main Components -- High-Level Architecture -- Main Components of Architecture -- Synapse SQL -- Compute Layer -- Dedicated Synapse SQL Pool -- Serverless Synapse SQL Pool -- Storage Layer -- Synapse Spark or Apache Spark -- Synapse Pipelines -- Synapse Studio -- Synapse Link -- Summary -- Chapter 5: Synapse SQL -- Synapse SQL Architecture Components -- Massively Parallel Processing Engine -- Distributed Query Processing Engine -- Control Node -- Compute Nodes -- Data Movement Service -- Distribution -- Hash Distribution.
Round-Robin Distribution -- Replication-based Distribution -- Azure Storage -- Dedicated or Provisioned Synapse SQL Pool -- Serverless or On-Demand Synapse SQL Pool -- Synapse SQL Feature Comparison -- Database Object Types -- Query Language -- Security -- Tools -- Storage Options -- Data Formats -- Resource Consumption Model for Synapse SQL -- Synapse SQL Best Practices -- Best Practices for Serverless Synapse SQL Pool -- Best Practices for Dedicated Synapse SQL Pool -- How-To's -- Create a Dedicated Synapse SQL Pool -- Create a Serverless or On-Demand Synapse SQL Pool -- Load Data Using COPY Statement in Dedicated Synapse SQL Pool -- Ingest Data into Azure Data Lake Storage Gen2 -- Summary -- Chapter 6: Synapse Spark -- What Is Apache Spark? -- What Is Synapse Spark in Azure Synapse Analytics? -- Synapse Spark Features & -- Capabilities -- Speed -- Faster Start Time -- Ease of Creation -- Ease of Use -- Security -- Automatic Scalability -- Separation of Concerns -- Multiple Language Support -- Integration with IDEs -- Pre-loaded Libraries -- REST APIs -- Delta Lake and Its Importance in Synapse Spark -- Synapse Spark Job Optimization -- Data Format -- Memory Management -- Data Serialization -- Data Caching -- Data Abstraction -- Join and Shuffle Optimization -- Bucketing -- Hyperspace Indexing -- Synapse Spark Machine Learning -- Data Preparation and Exploration -- Build Machine Learning Models -- Train Machine Learning Models -- Model Deployment and Scoring -- How-To's -- How to Create a Synapse Spark Pool -- How to Create and Submit Apache Spark Job Definition in Synapse Studio Using Python -- How to Monitor Synapse Spark Pools Using Synapse Studio -- Summary -- Chapter 7: Synapse Pipelines -- Overview of Azure Data Factory -- Overview of Synapse Pipelines -- Activities -- Pipelines -- Linked Services -- Dataset -- Integration Runtimes (IR).
Azure Integration Runtime (Azure IR) -- Self-Hosted Integration Runtimes (SHIR) -- Azure SSIS Integration Runtimes (Azure SSIS IR) -- Control Flow -- Parameters -- Data Flow -- Data Movement Activities -- Category: Azure -- Category: Database -- Category: NoSQL -- Category: File -- Category: Generic -- Category: Services and Applications -- Data Transformation Activities -- Control Flow Activities -- Copy Pipeline Example -- Transformation Pipeline Example -- Pipeline Triggers -- Summary -- Chapter 8: Synapse Workspace and Studio -- What Is a Synapse Analytics Workspace? -- Synapse Analytics Workspace Components and Features -- Azure Data Lake Storage Gen2 Account and File System -- Serverless Synapse SQL Pool -- Shared Metadata Management -- Code Artifacts -- What Is Synapse Studio? -- Main Features of Synapse Studio -- Home Hub -- Data Hub -- Develop Hub -- Integrate Hub -- Monitor Hub -- Integration -- Activities -- Manage Hub -- Analytics Pools -- External Connections -- Integration -- Security -- Synapse Studio Capabilities -- Data Preparation -- Data Management -- Data Exploration -- Data Warehousing -- Data Visualization -- Machine Learning -- Power BI in Synapse Studio -- How-To's -- How to Create or Provision a New Azure Synapse Analytics Workspace Using Azure Portal -- How to Launch Azure Synapse Studio -- How to Link Power BI with Azure Synapse Studio -- Summary -- Chapter 9: Synapse Link -- OLTP vs. OLAP -- What Is HTAP? -- Benefits of HTAP -- No-ETL Analytics -- Instant Insights -- Reduced Data Duplication -- Simplified Technical Architecture -- What Is Azure Synapse Link? -- Azure Cosmos DB -- Azure Cosmos DB Analytical Store -- Columnar Storage -- Decoupling of Operational Store -- Automatic Data Synchronization -- SQL API and MongoDB API -- Analytical TTL -- Automatic Schema Updates -- Cost-Effective Archiving -- Scalability.
When to Use Azure Synapse Link for Cosmos DB -- Azure Synapse Link Limitations -- Azure Synapse Link Use Cases -- Industrial IOT -- Predictive Maintenance Pipeline -- Operational Reporting -- Real-Time Applications -- Real-Time Personalization for E-Commerce Users -- How-To's -- How to Enable Azure Synapse Link for Azure Cosmos DB -- How to Create an Azure Cosmos DB Container with Analytical Store Using Azure Portal -- How to Connect to Azure Synapse Link for Azure Cosmos DB Using Azure Portal -- Summary -- Chapter 10: Azure Synapse Analytics Use Cases and Reference Architecture -- Where Should You Use Azure Synapse Analytics? -- Large Volume of Data -- Disparate Sources of Data -- Data Transformation -- Batch or Streaming Data -- Where Should You Not Use Azure Synapse Analytics? -- Use Cases for Azure Synapse Analytics -- Financial Services -- Manufacturing -- Retail -- Healthcare -- Reference Architectures for Azure Synapse Analytics -- Modern Data Warehouse Architecture -- Real-Time Analytics on Big Data Architecture -- Summary -- Index.
Record Nr. UNINA-9910485588003321
Shiyal Bhadresh  
[Place of publication not identified] : , : Apress, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
BigQuery for data warehousing : managed data analysis in the Google cloud / / Mark Mucchetti
BigQuery for data warehousing : managed data analysis in the Google cloud / / Mark Mucchetti
Autore Mucchetti Mark
Edizione [1st ed. 2020.]
Pubbl/distr/stampa [Place of publication not identified] : , : Apress, , [2020]
Descrizione fisica 1 online resource (539 pages)
Disciplina 658.40380285574
Soggetto topico Data warehousing
ISBN 1-4842-6186-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Part I. Building a Warehouse -- 1. Settling into BigQuery -- 2. Starting Your Warehouse Project -- 3. All My Data -- 4. Managing BigQuery Costs -- Part II. Filling the Warehouse -- 5. Loading Data Into the Warehouse -- 6. Streaming Data Into the Warehouse -- 7. Dataflow -- Part III. Using the Warehouse -- 8. Care and Feeding of Your Warehouse -- 9. Querying the Warehouse -- 10. Scheduling Jobs -- 11. Serverless Functions with GCP -- 12. Cloud Logging -- Part IV. Maintaining the Warehouse -- 13. Advanced BigQuery -- 14. Data Governance -- 15. Adapting to Long-Term Change -- Part V. Reporting On and Visualizing Your Data -- 16. Reporting -- 17. Dashboards and Visualization -- 18. Google Data Studio -- Part VI. Enhancing Your Data's Potential -- 19. BigQuery ML -- 20. Jupyter Notebooks and Public Datasets -- 21. Conclusion -- 22. Appendix A: Cloud Shell and Cloud SDK -- 23. Appendix B: Sample Project Charter.
Record Nr. UNINA-9910427044503321
Mucchetti Mark  
[Place of publication not identified] : , : Apress, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Building a scalable data warehouse with data vault 2.0 / Daniel Linstedt, Michael Olschimke
Building a scalable data warehouse with data vault 2.0 / Daniel Linstedt, Michael Olschimke
Autore LINSTENDT, Daniel
Pubbl/distr/stampa Amsterdam, : Morgan Kaufmann, 2016
Descrizione fisica Testo elettronico (PDF) (661 p.)
Disciplina 658.40380285574
Soggetto topico Data warehouse
Formato Risorse elettroniche
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996459053103316
LINSTENDT, Daniel  
Amsterdam, : Morgan Kaufmann, 2016
Risorse elettroniche
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Data warehouse design solutions / Christopher Adamson, Michael Venerable
Data warehouse design solutions / Christopher Adamson, Michael Venerable
Autore ADAMSON, Christopher
Pubbl/distr/stampa New York : J. Wiley & Sons, c1998
Descrizione fisica XX, 523 p. ; 24 cm + cd-rom
Disciplina 658.40380285574
Altri autori (Persone) VENERABLE, Michael
Soggetto topico Aziende - Servizi di informazione - Automazione
ISBN 0-471-25195-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-990000377760203316
ADAMSON, Christopher  
New York : J. Wiley & Sons, c1998
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Data warehouse systems : design and implementation / / Alejandro Vaisman and Esteban Zimányi
Data warehouse systems : design and implementation / / Alejandro Vaisman and Esteban Zimányi
Autore Vaisman Alejandro
Edizione [Second edition.]
Pubbl/distr/stampa Berlin, Germany : , : Springer, , [2022]
Descrizione fisica 1 online resource (713 pages)
Disciplina 658.40380285574
Collana Data-centric systems and applications
Soggetto topico Data warehousing
Management information systems
Database management
ISBN 3-662-65167-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- 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.
Record Nr. UNISA-996483163203316
Vaisman Alejandro  
Berlin, Germany : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Data warehouse systems : design and implementation / / Alejandro Vaisman and Esteban Zimányi
Data warehouse systems : design and implementation / / Alejandro Vaisman and Esteban Zimányi
Autore Vaisman Alejandro
Edizione [Second edition.]
Pubbl/distr/stampa Berlin, Germany : , : Springer, , [2022]
Descrizione fisica 1 online resource (713 pages)
Disciplina 658.40380285574
Collana Data-centric systems and applications
Soggetto topico Data warehousing
Management information systems
Database management
ISBN 3-662-65167-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- 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.
Record Nr. UNINA-9910584481803321
Vaisman Alejandro  
Berlin, Germany : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Data Warehouse Systems : Design and Implementation / / by Alejandro Vaisman, Esteban Zimányi
Data Warehouse Systems : Design and Implementation / / by Alejandro Vaisman, Esteban Zimányi
Autore Vaisman Alejandro
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Descrizione fisica 1 online resource (XVI, 625 p. 133 illus.)
Disciplina 658.40380285574
Collana Data-Centric Systems and Applications
Soggetto topico Database management
Information storage and retrieval
Management information systems
Application software
Database Management
Information Storage and Retrieval
Business IT Infrastructure
Computer Appl. in Administrative Data Processing
ISBN 3-642-54655-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Part I Fundamental Concepts -- 1 Introduction -- 2 Database Concepts -- 3 Data Warehouse Concepts -- 4 Conceptual Data Warehouse Design -- 5 Logical Data Warehouse Design -- 6 Querying Data Warehouses -- Part II Implementation and Deployment -- 7 Physical Data Warehouse Design -- 8 Extraction, Transformation and Loading -- 9 Data Analytics: Exploiting the Data Warehouse -- 10 A Method for Data Warehouse Design -- Part III Advanced Topics -- 11 Spatial Data Warehouses -- 12 Trajectory Data Warehouses -- 13 New Data Warehouse Technologies -- 14 Data Warehouses and the Semantic Web -- 15 Conclusion.
Record Nr. UNINA-9910298988003321
Vaisman Alejandro  
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
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The data warehouse toolkit [[electronic resource] ] : the definitive guide to dimensional modeling / / Ralph Kimball, Margy Ross
The data warehouse toolkit [[electronic resource] ] : the definitive guide to dimensional modeling / / Ralph Kimball, Margy Ross
Autore Kimball Ralph
Edizione [3rd ed.]
Pubbl/distr/stampa Indianapolis, Ind., : Wiley, c2013
Descrizione fisica 1 online resource (601 p.)
Disciplina 658.40380285574
Altri autori (Persone) RossMargy
Soggetto topico Data warehousing
Business enterprises - Data processing
Soggetto genere / forma Electronic books.
ISBN 1-118-73228-6
1-118-53077-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; 1 Data Warehousing, Business Intelligence, and Dimensional Modeling Primer; Different Worlds of Data Capture and Data Analysis; Goals of Data Warehousing and Business Intelligence; Publishing Metaphor for DW/BI Managers; Dimensional Modeling Introduction; Star Schemas Versus OLAP Cubes; Fact Tables for Measurements; Dimension Tables for Descriptive Context; Facts and Dimensions Joined in a Star Schema; Kimball's DW/BI Architecture; Operational Source Systems; Extract, Transformation, and Load System; Presentation Area to Support Business Intelligence
Business Intelligence ApplicationsRestaurant Metaphor for the Kimball Architecture; Alternative DW/BI Architectures; Independent Data Mart Architecture; Hub-and-Spoke Corporate Information Factory Inmon Architecture; Hybrid Hub-and-Spoke and Kimball Architecture; Dimensional Modeling Myths; Myth 1: Dimensional Models are Only for Summary Data; Myth 2: Dimensional Models are Departmental, Not Enterprise; Myth 3: Dimensional Models are Not Scalable; Myth 4: Dimensional Models are Only for Predictable Usage; Myth 5: Dimensional Models Can't Be Integrated; More Reasons to Think Dimensionally
Agile ConsiderationsSummary; 2 Kimball Dimensional Modeling Techniques Overview; Fundamental Concepts; Gather Business Requirements and Data Realities; Collaborative Dimensional Modeling Workshops; Four-Step Dimensional Design Process; Business Processes; Grain; Dimensions for Descriptive Context; Facts for Measurements; Star Schemas and OLAP Cubes; Graceful Extensions to Dimensional Models; Basic Fact Table Techniques; Fact Table Structure; Additive, Semi-Additive, Non-Additive Facts; Nulls in Fact Tables; Conformed Facts; Transaction Fact Tables; Periodic Snapshot Fact Tables
Accumulating Snapshot Fact TablesFactless Fact Tables; Aggregate Fact Tables or OLAP Cubes; Consolidated Fact Tables; Basic Dimension Table Techniques; Dimension Table Structure; Dimension Surrogate Keys; Natural, Durable, and Supernatural Keys; Drilling Down; Degenerate Dimensions; Denormalized Flattened Dimensions; Multiple Hierarchies in Dimensions; Flags and Indicators as Textual Attributes; Null Attributes in Dimensions; Calendar Date Dimensions; Role-Playing Dimensions; Junk Dimensions; Snowflaked Dimensions; Outrigger Dimensions; Integration via Conformed Dimensions
Conformed DimensionsShrunken Dimensions; Drilling Across; Value Chain; Enterprise Data Warehouse Bus Architecture; Enterprise Data Warehouse Bus Matrix; Detailed Implementation Bus Matrix; Opportunity/Stakeholder Matrix; Dealing with Slowly Changing Dimension Attributes; Type 0: Retain Original; Type 1: Overwrite; Type 2: Add New Row; Type 3: Add New Attribute; Type 4: Add Mini-Dimension; Type 5: Add Mini-Dimension and Type 1 Outrigger; Type 6: Add Type 1 Attributes to Type 2 Dimension; Type 7: Dual Type 1 and Type 2 Dimensions; Dealing with Dimension Hierarchies
Fixed Depth Positional Hierarchies
Record Nr. UNINA-9910452802803321
Kimball Ralph  
Indianapolis, Ind., : Wiley, c2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The data warehouse toolkit : the definitive guide to dimensional modeling / / Ralph Kimball, Margy Ross
The data warehouse toolkit : the definitive guide to dimensional modeling / / Ralph Kimball, Margy Ross
Autore Kimball Ralph
Edizione [Third edition]
Pubbl/distr/stampa Indianapolis, Ind. : , : Wiley, , [2013]
Descrizione fisica 1 online resource (601 p.)
Disciplina 658.40380285574
Altri autori (Persone) RossMargy
Soggetto topico Gestor de dades
Empreses - Informàtica
Data warehousing
Business enterprises - Data processing
ISBN 1-118-73228-6
1-118-53077-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; 1 Data Warehousing, Business Intelligence, and Dimensional Modeling Primer; Different Worlds of Data Capture and Data Analysis; Goals of Data Warehousing and Business Intelligence; Publishing Metaphor for DW/BI Managers; Dimensional Modeling Introduction; Star Schemas Versus OLAP Cubes; Fact Tables for Measurements; Dimension Tables for Descriptive Context; Facts and Dimensions Joined in a Star Schema; Kimball's DW/BI Architecture; Operational Source Systems; Extract, Transformation, and Load System; Presentation Area to Support Business Intelligence
Business Intelligence ApplicationsRestaurant Metaphor for the Kimball Architecture; Alternative DW/BI Architectures; Independent Data Mart Architecture; Hub-and-Spoke Corporate Information Factory Inmon Architecture; Hybrid Hub-and-Spoke and Kimball Architecture; Dimensional Modeling Myths; Myth 1: Dimensional Models are Only for Summary Data; Myth 2: Dimensional Models are Departmental, Not Enterprise; Myth 3: Dimensional Models are Not Scalable; Myth 4: Dimensional Models are Only for Predictable Usage; Myth 5: Dimensional Models Can't Be Integrated; More Reasons to Think Dimensionally
Agile ConsiderationsSummary; 2 Kimball Dimensional Modeling Techniques Overview; Fundamental Concepts; Gather Business Requirements and Data Realities; Collaborative Dimensional Modeling Workshops; Four-Step Dimensional Design Process; Business Processes; Grain; Dimensions for Descriptive Context; Facts for Measurements; Star Schemas and OLAP Cubes; Graceful Extensions to Dimensional Models; Basic Fact Table Techniques; Fact Table Structure; Additive, Semi-Additive, Non-Additive Facts; Nulls in Fact Tables; Conformed Facts; Transaction Fact Tables; Periodic Snapshot Fact Tables
Accumulating Snapshot Fact TablesFactless Fact Tables; Aggregate Fact Tables or OLAP Cubes; Consolidated Fact Tables; Basic Dimension Table Techniques; Dimension Table Structure; Dimension Surrogate Keys; Natural, Durable, and Supernatural Keys; Drilling Down; Degenerate Dimensions; Denormalized Flattened Dimensions; Multiple Hierarchies in Dimensions; Flags and Indicators as Textual Attributes; Null Attributes in Dimensions; Calendar Date Dimensions; Role-Playing Dimensions; Junk Dimensions; Snowflaked Dimensions; Outrigger Dimensions; Integration via Conformed Dimensions
Conformed DimensionsShrunken Dimensions; Drilling Across; Value Chain; Enterprise Data Warehouse Bus Architecture; Enterprise Data Warehouse Bus Matrix; Detailed Implementation Bus Matrix; Opportunity/Stakeholder Matrix; Dealing with Slowly Changing Dimension Attributes; Type 0: Retain Original; Type 1: Overwrite; Type 2: Add New Row; Type 3: Add New Attribute; Type 4: Add Mini-Dimension; Type 5: Add Mini-Dimension and Type 1 Outrigger; Type 6: Add Type 1 Attributes to Type 2 Dimension; Type 7: Dual Type 1 and Type 2 Dimensions; Dealing with Dimension Hierarchies
Fixed Depth Positional Hierarchies
Record Nr. UNINA-9910779708303321
Kimball Ralph  
Indianapolis, Ind. : , : Wiley, , [2013]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui